1. Owning Work Design

Eleven issues ago I restarted this newsletter with a question I kept finding nobody was asking: who designs Work? Not who automates the tasks, and not who buys the tools. Who decides how work should flow through an organisation now that a machine can do part of it, and who answers for the result?

In Issue 57 I asked you a sharper version: in your organisation, who owns the cost that no function owns? And last time I ended by saying that who that somebody is would be this issue’s problem.

So this issue does two things. It puts the arc back together, because each issue was a piece of one argument and I think the pieces are easier to see side by side. And it makes the proposal as concrete as I can. I will say now that the proposal is not a new job title. I argued in Issue 55 that the proliferation of “Chief of…” titles is a symptom of the problem, and I have not changed my mind. What I want to describe is a way of governing and executing. Owning work design, in practice, means owning the capabilities through which the work gets done.

How we got here, one piece at a time

It started with a question about who designs Work in Issue 49, and the first thing that question needed was evidence. Issue 50 went looking in the productivity numbers and found a gap between what AI can do and what it is actually producing inside organisations. That gap is not a technology problem. It is a design problem: the tools get bought, while the work around them stays as it was.

If the gap is about design, the next question is what kind of capability would close it. Issue 51 answered with a way of governing the capabilities an organisation holds: treating them as a portfolio, managed with the same discipline a pension fund applies to its assets. I’m coming back to that proposal as the backbone of this issue.

Before going further, the arc had to look at what the gap costs. Issue 52 showed that when nobody redesigns the work, people inherit what is left behind: the checking and the monitoring, or an exhausting load of supervision. That raised an awkward question for my own profession: why had HR not noticed? Issue 53’s attempted answer was that HR’s instruments measure jobs, while AI moves work across the boundaries between them, and accountability quietly lands on whichever human happens to be nearest. Issue 54 went one step further and turned the lens on the operating model HR built for itself: designed for a world AI is dissolving, and asked to answer for outcomes it has no real authority to design, causing it to behave defensively.

So who should hold the design, if not HR as it stands today? Issue 55 looked at three companies that had tried to answer with a new structure, and concluded that the answer was a brief, not a title: the authority to rebalance the capability portfolio, not just the duty to describe it. Issue 56 created a link with my past, celebrating one of my earlier mentors, Franco D’Egidio. In the work I did with him (writing one of his books) I have been able to trace the reason why that brief is so hard to create: organisations are, in his words, over-managed and under-led, and they struggle to change from the inside because they cannot stop being what once made them successful. Once again, the obstacle is not the technology.

The last three issues turned from the organisation to the people inside it, because that is where the cost of the gap is paid. Issue 57 described the adoption debt, the cost that lands in the system while the gain is booked at the task, and introduced Sarah, Jack and Clara. Issue 58 added Ben, and showed that the work being automated first is the work through which people used to become good. And Issue 59 argued that development is a capability, not a culture, and that the right nobody holds is the right to end things.

Put side by side, all this makes a coherent argument. There is a gap between what the technology makes possible and what organisations do with it. The gap is in how capability is governed. It has a human cost that falls unevenly. And it persists because nobody holds the whole. Howard Yu's recent account of Japan Airlines' collapse puts it in a single line: "Competence in each job doesn't guarantee overall competence across different jobs."

Four people, four meanings of work

Before the proposal, one thing I have not said explicitly until now.

Sarah, Jack, Clara and Ben (the people you met in the last 3 issues) were not just four random employees. Each of them stands for a different answer to the question of what work means, the lens I have been developing on my website as the discourses of work. Over history, people have understood work in several distinct ways, and the older ways never really disappeared. They sit underneath the newer ones, inside the same office.

1. Sarah works in the discourse of Craftsmanship. Work is mastery, built slowly against a standard. What AI took from her was the practice through which the mastery was made.

2. Jack works in the discourse of Personal Realisation. Work is how he becomes someone. What AI took from him was the gradient; he is not disengaged, he is becalmed.

3. Clara works in the discourse of Subjugation. Work was imposed on her, not chosen. What AI brought her was more speed and more monitoring, and less slack in which any meaning could be improvised.

4. Ben works in the discourse of Work as Job. Work is a role in a system, with a title, a dashboard and a credential. What AI changed for him is that the role still looks the same from the outside, while the thing it was supposed to build inside him is no longer being built.

This matters for the proposal I am making, because it means the same redesign has to land in different ways. A change that frees Sarah’s time can wound her craft. A change that speeds up Clara’s day tightens a system she never agreed to. You cannot govern work well if you cannot see these differences, and most organisations have no instrument that sees them.

What owning something actually means

The word I would like to use for the brief is ownership, because every manager understands it. But it needs defining, because we use it loosely, and the loose version is exactly what has failed.

To own a piece of the organisation’s work, a capability, a process, a platform, you need three things at once.

1. You answer for the result. If it does not deliver, you are the one who explains why.

2. You can change how the work is done. Not request a change, not escalate it: change it.

3. You can narrow it or stop it. Or at least you can propose ending it, and be heard.

Take away any one of the three and what you have is not ownership. If you answer for the result but cannot change the design, you get the defensive behaviour Issue 54 described: you block, you delay, you ask for sign-off, and everyone calls it a culture problem. If you can change the design but answer for nothing, you get tinkering without consequence. And if nobody can stop anything, every capability becomes permanent by default, which is how organisations end up carrying things for years that nobody would choose today.

That third part is the one we forget, and it is the one Issue 59 was about. Functional ownership is a good way to build a capability and a poor way to end one, because the function that carries it depends on it continuing. So when I say ownership, I mean all three, including the uncomfortable one.

The proposal: holding the capability portfolio

In Issue 51 I proposed that organisations manage their capabilities as a portfolio, and that doing so imports four disciplines most of them lack.

1. Trade-offs made explicitly. Every decision to build, buy or borrow a capability is a choice between cost, speed, quality and dependency, and it should be made in the open. With AI there is now a fourth option on the table, automate, and it needs to be weighed the same way.

2. Continuous rebalancing. Not once a year at budget time, because with AI the value of a capability can shift within a quarter.

3. Risk as an explicit dimension. Borrow too much and you hollow out what you know. Build too much and you lock yourself in. These effects are predictable, but only if someone looks at the whole.

4. Ownership at the level where trade-offs are made. Somebody has to hold the whole picture, not just the line items.

Everything the arc has argued since adds four more.

5. Every capability has one owner, in the full sense. One named person who answers for it, can change it, and can propose ending it. Not a committee, and not a dotted line.

6. Every capability has an ending condition, and is argued for again on a cycle. When it is created, someone writes down what would have to be true for it to stop. Some endings cannot be undone, so the condition also says whether the decision can be reversed, and the ones that cannot need a higher bar of evidence. At regular intervals its owner explains why it is still worth what it costs, and what it costs is visible without anyone having to ask.

7. Formation is on the list. Before the preparatory work inside a capability is automated, the portfolio says where the next generation will learn to do the real work. This is Ben’s point, and it is the one that disappears first when the business case is written.

8. Meaning is read before work is redesigned. Any significant redesign comes with a reading of what it does to the people inside the work: to the Sarahs, the Jacks, the Claras and the Bens. Not a satisfaction survey. A deliberate account of which meanings of work the change supports and which it breaks.

So who holds it? This is the part I want to be precise about.

The portfolio belongs to the CEO and the executive team as a whole, because that is where trade-offs between capabilities can actually be made. It should sit on the agenda the way strategic planning or budget does: on a fixed rhythm, capability by capability, not only when a crisis forces the conversation. It is not an extra meeting. Most executive teams already spend hours arguing about capabilities; they just do it indirectly, line by line, in the budget round and in every restructuring proposal. The cycle moves that argument to the place where it can actually be settled, one capability at a time.

Each capability then needs its defined owner, usually the executive or senior manager whose part of the business depends on it most. The owner defends it at each cycle.

Anyone can table a proposal to redesign or retire a capability, and the owner has to answer it. This is where the right to end things finally lives: not with the custodian alone, and not with a committee for shutting things down, which would be captured within a year, but in the obligation to defend what you hold.

And one function runs the cycle without owning any of it. It keeps the map of what the organisation holds, makes the carrying costs visible, flags where formation is at risk, and brings the reading of meaning into the room. It is a steward, not an owner.

In a company of a few hundred people this is simpler than it sounds. The executive team already is the portfolio conversation, and the steward may be one person with a map, a cost column and the licence to ask awkward questions.

Why insist that ending something has to be an active decision? Because even in medicine, where the evidence against a practice can be overwhelming, a scoping review of how low-value clinical practices get abandoned found that active change interventions were associated with the greatest likelihood of actually stopping them. The evidence was not enough on its own. I see no reason to think organisations are better at stopping things than hospitals are.

What it looks like on a Monday morning

An example helps, so let me build one. It is a composite, but I have seen every piece of it.

A mid-sized company deploys an AI agent to handle customer complaints. Under the usual way of governing, IT buys the tool, operations deploys it, HR maybe runs some training, and the productivity gain is booked. Under the portfolio way, the complaints-handling capability has an owner, for example the head of customer operations, and the change goes through the cycle.

The trade-off is made in the open: first-line complaints are automated, escalations stay human, and the cost of the checking that remains is counted rather than assumed away. The formation question is asked before go-live: if the agent now handles the easy cases, where does a new hire learn what a hard case looks like? The answer is written down: every new handler resolves a number of difficult complaints end to end, alongside someone experienced.

The meaning is read. For the experienced handler, the craft now lives in the hard cases, so her role is redesigned around them. For the team lead who expected to run an escalation desk that no longer exists, a different next step is designed, perhaps owning the quality of the agent itself. For the outsourced night shift, the monitoring that came bundled with the tool is reviewed, and whatever nobody actually needs is switched off. And the old scripted-response training gets an ending condition: it stops when the new formation path is running.

A year later the owner stands up and explains whether the capability is still worth what it costs. Someone else may propose redesigning it again. The cycle is doing its job.

None of this requires a new executive. It requires the executive team to govern capability the way it already governs money.

Where HR fits

Which function should be the steward? In most organisations the obvious candidate is the people function, and I want to state the condition as sharply as I can: only if it is redefined, not rebranded.

My profession has asked for a seat at the table for twenty years. We got the seat. The problem was never the seat; it was what the function was built to do. A people function designed around transactions, expertise and translation, the three things AI is now absorbing, does not become able to steward a capability portfolio because someone changes its name. It has to be rebuilt around the portfolio: the map, the costs, the formation, the meaning. If it is not, handing it this role simply creates another silo next to the ones that were not talking to each other in the first place.

And the steward must not become the owner. The moment the people function owns the capabilities, it inherits the same interest in their continuation as every other custodian, and the right to end things disappears again.

What I cannot close

I told myself I would not tidy things away at the end, so three things stay open.

The first is Clara. The portfolio can switch off the monitoring nobody needed, and it can redesign her work around her rather than around the software. What it cannot do on its own is give her a say in what the work is for. Reading meaning is a start. Letting the people inside the work change what the organisation thinks the work means is a much longer road.

The second is that the reading of meaning cannot simply be installed. The authority to say what work means inside an organisation moves slowly, and only through deliberate investment over years, as the Italian fieldwork I cited last time shows. A steward can bring the reading into the room. Whether the room listens is a matter of leadership, and no governance model substitutes for that.

The third is how I would know I am wrong. If organisations that run this cycle for a few years end up retiring no more capabilities than those that do not, and the Bens still have nowhere to learn, then the problem was never governance, and the portfolio is one more ritual. I would rather name that test now than be spared it later.

What comes next

This issue closes an arc I started in May. It does not close this newsletter.

The next few issues are deliberately provisional, and I would rather say so than pretend to a plan: the fallacy of treating work as a list of tasks, what a people function looks like once AI has dissolved the logic it was built on, and the difference between being accountable and being responsible. Alongside them I am starting a parallel thread on intentionality in practice, which I will introduce properly soon.

For now, one question: pick one capability your organisation has been carrying for years. Who owns it in all three senses, and who could end it?

Leave a comment

If you can name them, the portfolio is already being held, at least there.

If you cannot, you know where to start.

Sergio

References

1. D’Egidio, F. (2003). La Nuova Bussola del Manager. ETAS.

2. Niven, D. J., Mrklas, K. J., Holodinsky, J. K., Straus, S. E., Hemmelgarn, B. R., Jeffs, L. P. and Stelfox, H. T. (2015). Towards understanding the de-adoption of low-value clinical practices: a scoping review. BMC Medicine, 13, 255. https://doi.org/10.1186/s12916-015-0488-z

3. Yu, Howard (2026). “What If Doing Your Job Is Part of the Problem?” Substack newsletter. One Inch Ahead, September 18, 2026.

4. Zucca, A., Bieber, K., Ghignone, E. and Solari, L. (2026). Il ruolo necessario degli artefici della libertà del self-management. Harvard Business Review Italia, luglio-agosto 2026. https://www.hbritalia.it/luglio-agosto-2026/2026/07/01/news/il-ruolo-necessario-degli-artefici-della-liberta-del-self-management-16622/

2. Site Updates

A long piece went up on the site last Sunday, and it is this issue’s question seen from inside the people function. The One Thing the CHRO Designs starts from Dave Ulrich’s September reading of the CHRO priority lists, which puts work, rather than people, at the centre of the function, and sets it against two decades of survey data showing how little the function itself has moved. Its argument is that the HR operating model is the one thing a CHRO designs outright, and that the business partner is where that model meets the line, and so where it fails first.

Read it next to the section above on where HR fits. There I say the people function should steward the capability portfolio without owning it. The essay asks what the function would have to look like to do that, and starts from the interface with the line rather than from the chart. It runs to about half an hour, with three figures and a full reference list, so it is one for a quiet hour.

One correction, too. The page on consistency, congruence and coherence now claims less than it did. The study at its centre measured whether an institution’s stated values, goals and leadership signals pointed the same way, not whether any of that matched what the institution actually did, and I had read it as saying more. The corrected argument is smaller and, I think, stronger.

3. Reading Suggestions

1. We Know How to Worry About AI That Fails — Richard Claydon, Leadership, Rewritten. The Substack pick. Agents that each behave well can still fail together, because the failure lives in how they interact. The governance version of this issue’s argument: somebody has to look at the whole.

2. Impact pathway: beyond automation and augmentation – a relational ontology of AI in operations and supply chain management — Wilhelm, Yan, Hendriksen, Sanders & Micheli, International Journal of Operations & Production Management, 46(13), 2026. The scientific pick, from Zotero. The authors argue that AI can no longer be treated as a discrete tool added to existing operations, and read role drift, unstable process boundaries and contested performance attribution as things to study rather than implementation failures. The operations literature arriving at the same place as this arc.

3. The Gap Is Where the Job Gets Learned — John Sumser, HRExaminer. Work as prescribed and work as done are never the same, and the gap between them is where people actually learn the job. AI governance written only from the prescribed side misses it. Ben’s problem, seen from the governance desk.

4. AI agents are not people, and that’s a problem for HR — Colby Kennedy Nesbitt, Variance, Explained. Calling AI labour serves a lot of interests, and most of them gain more from the word than from the responsibility that would come with it. The strongest position is to talk labour and act software. He then asks HR a sharper question about its own instruments: does this measure need a continuous person behind it, or is it only counting units of work? Read it against the section above on where HR fits. He worked at Lattice when it tried to give agents employee records, so he is a participant as well as an observer.

5. Managing AI Employees — Matteo Cellini, on Substack. Agents need a defined job, reviews, limits and eventually a retirement, and someone in the business who answers for what each one produces: the box the org chart is still missing. His lifecycle has a stage I did not put in the essay, restriction, which means narrowing an agent’s scope instead of switching it off. Read it next to Nesbitt: one treats agents as employees to be managed, the other shows what that framing hides.

4. The (un) Intentional Organisation 😁

5. Keeping in Touch

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A Capability, Not Culture

Almost every conversation I have had this year about AI and human work ends in the same place. Someone says that in the end it will come down to the human qualities, the judgement and the creativity and the care, the things the machine cannot do. Everyone nods. The meeting closes. Nobody asks what would have to be different on Monday morning, and nobody puts a number against it.

I think the sentiment is broadly right. My problem is with what happens next, which is nothing, because a claim that cannot be acted on cannot be checked either. “Humanity will save us” is comfortable precisely because it is unfalsifiable. If the organisation does well, the humans made the difference. If it does badly, we did not invest enough in the humans. There is no version of the world in which the statement is wrong, and that is not a strength.

You can see the shape of it in the way the argument gets made. The idea that companies choosing augmentation over automation may win in the long run is now close to received wisdom in HR circles, and it is repeated by people with a lot of authority. Read the title again, though: may. What carries it is a well-argued case about how employees’ perceptions will shape results, not a tested outcome. That is fine as an argument. It is not fine as the load-bearing wall of a business case, and it is being asked to hold up quite a lot of weight.

Part of the conversation about HR as an actor in AI implementation grows from the same tree. We feel reassured that someone is going to defend the human side against the evil of automation, and we rather like that the someone is us. It is a comfortable position for my profession, and an unexamined one, because defending the human side is not the same as saying what it consists of.

So I would like to specify what the human answer would actually consist of.

The idea has a name, and it is ten years old

In 2016 Robert Kegan and Lisa Laskow Lahey published An Everyone Culture, and gave the field the term Deliberately Developmental Organisation. The claim was simple and quite radical: most organisations run development as a programme beside the work, and a small number run it as the operating logic of the work itself. In those organisations, being visibly not yet good at something is not a risk to be managed but the ordinary condition of employment. I read it when it came out, reviewed it on my site, and gave it five stars.

I want to come back to that review later, because it has a problem.

First the useful part. What Kegan and Lahey did was specify. Not “invest in people”, which means nothing, but: name a growth edge, build the daily groove that makes people work on it, and hold enough safety that the struggle is survivable. Edge, groove, home. You can argue with all three. You cannot accuse them of vagueness.

There is, however, an element I do not agree with. Kegan and Lahey made development a property of the culture. I think that was the wrong object, and I think the last decade has shown why.

Why culture is the wrong object

Culture is where organisational claims go to become unmeasurable. This is not a complaint about consultants, it is what the evidence says: the CIPD’s evidence review finds the direct effect of cultural values on objective organisational performance sitting close to zero. Whatever culture is doing, it is not reliably showing up where the money is counted, and it shows up rather more strongly when organisations are asked to score themselves.

So a prescription that lands in the culture layer inherits that problem. It also inherits something worse. The way I use the term, culture is emergent: the pattern of how things are actually done, which nobody authors directly. That is exactly what you want when you are trying to understand an organisation. It is a problem when you are trying to change one, because an emergent property has no budget line, no owner, and no moment at which anyone has to decide anything about it.

A capability is a different kind of object, and this is the move I want to make in this issue.

1. Culture is something an organisation is said to have. It emerges from everything else the organisation does, and it is described, surveyed and aspired to. Nobody owns it and nobody pays for it.

2. A capability is something an organisation holds. It should have an owner and a development path, it competes for investment against other capabilities, and somebody can be asked why it is not better than it was last year.

The should is doing honest work there. Plenty of organisations do not run their capabilities that way either, and I have written about what happens when capability-building starts before anyone has defined where the capability is supposed to act. But a capability can be given an owner. Culture cannot.

Treating deliberate development as a capability rather than a culture does three things at once. It gives the claim a place to live in the operating model. It puts it into the portfolio, where it has to argue for its funding like everything else. And it makes it accountable in the only way organisations actually recognise, which is that a named person has to answer for it.

It also does a fourth thing, which nobody in this literature seems to want to look at.

The right nobody asks for

If development is a capability, then it sits in the capability portfolio. And a portfolio is not only a set of things you build. It is a set of things you hold, combine, and eventually retire.

Functional ownership is very good at building a capability and structurally very bad at disposing of one. No function has ever volunteered that its own capability has stopped earning its keep. So organisations accumulate. They add the new capability beside the old one and call it transformation.

Which means the missing right is not ownership. It is disposal. Somebody has to be able to decide that a thing ends.

Apply that to development and it gets uncomfortable fast, which is how you know it is a real question. When do you stop developing a capability? Who is allowed to say that a skill your people have spent fifteen years building is no longer one the organisation needs? Kegan has no answer to this, because a culture has no disposal mechanism. An organisation that has made everyone a learner, always, has no way of saying: not that, not any more.

I do not have a clean answer. I can see roughly where one would have to come from, though. Every other capability in a portfolio has a moment at which somebody is obliged to look at it: a review, a funding cycle, a point where continuing requires a decision rather than the absence of one. Development has no such moment, which is why it is never retired and also why it is never properly funded. Those two facts are the same fact. It is also why so many talent strategies end up dismissed as “the HR thing”: written with care, presented with conviction, and never given resources that anyone would have to defend in a budget meeting. Nobody is obliged to fund what nobody is obliged to review.

So if I had to sketch the mechanism, I would put it there. Not a committee for shutting things down, which would be captured within a year, but an obligation that the portfolio is re-argued on a cycle, capability by capability, with a named person who has to say why each one is still worth holding and what it is costing to hold it. Disposal then stops being an act anyone has to perform. It is what happens to a capability that fails to survive its own defence.

That is a direction rather than a method, and I would rather leave it that way than dress a sketch up as a design. What I am fairly sure of is that it is the question, and that it is the one separating a developmental organisation from a sentimental one.

Two preconditions, and they fail from opposite directions

Before any of this, there are two things that have to be true, and the literature tends to skip both.

The first I owe to Franco D’Egidio, whose La Nuova Bussola del Manager I worked on more than twenty years ago and keep returning to. Drawing on Fukuyama, he treated trust not as a sentiment but as infrastructure: a design choice made upstream of everything else, which then determines what is possible downstream. You cannot build a deliberately developmental organisation on a control infrastructure. Not because control is morally worse, but because the two are asking the same person for incompatible things. Development requires a visible version of you that is not good enough yet. Control is a system for noticing exactly that and pricing it.

The second precondition runs the other way, and Lisa Gill put it in a single question: who am I to give you feedback? Feedback is the core developmental tool. It is always difficult, and hierarchy, for all its faults, settles one thing about it: who is expected to give it to you. The answer is unattractive, but it is clear. Take the managerial structure out, flatten things, distribute the authority, and that implicit permission goes with it. Nobody is obviously entitled to tell you your work is not there yet, so the conversation quietly stops happening, and everyone reports that the culture is excellent.

This is not an argument against flat organisations. Some of them develop people very well. Buurtzorg, the Dutch home-care organisation run by self-managing nurse teams, is the case everyone cites, and what gets cited less is how much deliberate design sits underneath it: teams capped at twelve and split when they grow, coaches whose whole job is helping the teams rather than reporting on them. The peer patterns work because somebody designed them, and kept designing them. Fieldwork inside an Italian self-managing company, written up this summer by Alberto Zucca, Tine Bieber, Edoardo Ghignone and Luca Solari, shows the same thing from the inside: an organisation genuinely flat in how the work gets done, and still dependent on one person to keep holding what the freedom means. Most organisations do something else. They copy a pattern, agile being the usual one, remove the hierarchy that was quietly doing the feedback work, and pretend that development will carry on by itself.

So: too much control and development is not safe. Too little structure and it is not sanctioned. Both failures look like nothing happening, which is why neither shows up in a survey.

What the machine actually took

Now the AI part, and I want to be narrower here than the genre usually is.

I wrote in the last issue that the preparatory work that used to build competence is exactly the work that is cheapest to automate, which leaves Ben very good at checking and possibly building nothing underneath.

But there is something more specific that I think the discussion is missing, and it took me a long time to see it because I lived it without noticing.

Early in my career I spent years writing in someone else’s voice. What I took from it was not skill. It was the order in which he approached a problem: what he looked at first, what he refused to answer until later. I was reproducing that sequence for years before I noticed I had it, and nobody has taught it to me since.

That is what the gradient was transmitting. Not technique, which is quick to teach and is now being taught by a machine, but the shape in which a competent person holds a problem, arriving through long articulate work and arriving invisibly. A development system that builds competence and not articulation has restored the wrong half. Every proxy we use, the certification, the completion rate, the skills matrix, measures the half that was never the scarce one.

Ghostwriting did this by accident. A development system would have to do it on purpose. What would that look like? Think about asking someone to walk through the order in which they approached a problem before showing the result, and having a senior person say where their own order would differ. It produces nothing a client ever sees, it takes a senior person’s time, and it is exactly the kind of work that disappears first when the machine makes the output free.

And then there is Clara

I have been writing this as though more development is straightforwardly good. For a lot of people it is. I want to stay honest about the ones for whom it is not.

Think about someone who never chose this work in any meaningful sense. It was the job that was available. It was already transactional before any of this started, and the tools that arrived this year made it faster and rather more closely observed, because the monitoring came bundled with the software. Now that same organisation announces that it is becoming developmental. It would like her to name her growth edge, in a shared document, to a manager.

What is being asked of her? She is being asked to make her limitations legible to an institution that already holds a great deal of information about her and has never used any of it in her favour. The offer of development, from a party that holds the power, can land as one more extraction, and it can land that way without anybody intending it.

There is a serious version of this concern in the research literature, and it is more damning than the sceptical version I hear in practice. Emily Ruppel’s ethnography of a job-training programme describes what she calls therapeutic management, which among other things aims to transform structural problems into individual ones. Her setting is a specific one and I would not stretch the finding beyond it, but the mechanism travels. A developmental frame can relocate a design failure into a person’s growth plan. The workload does not change. The staffing does not change. What changes is that the problem now has your name on it.

I have watched a milder version of this in organisations a long way from low-wage work. An organisation-design problem gets turned into a leadership development programme. That is not necessarily a bad thing: people learn, and some of what they learn is useful. It becomes one when the entire effort is relocated onto individuals, who are asked to solve through their own growth what the structure keeps producing, and whose reward, at the next restructuring that finally “sorts” the organisational issue, is a thank-you note attached to a severance package.

Note what this is not. It is not an argument against development. It is an argument that the same intervention has different meanings depending on who holds interpretive authority, which is to say: depending on who gets to decide what your growth edge is. Operational authority can be handed over by design. Interpretive authority, as the Italian fieldwork I mentioned earlier shows, moves only through long and deliberate investment, and if it has not moved, then “we will develop you” still means “we will continue to be the ones who say what you lack.”

I do not think this dissolves. I think it is the thing you hold while you build, and I am going to leave it open here rather than tidy it away.

About that five-star review

I said I would come back to it.

In December 2019 I reviewed An Everyone Culture and wrote that the Bridgewater case was probably the strongest in the book, and that the results showed it. That review is still on my site. I am not going to quietly update it, because what has happened since is more useful than a corrected page.

Kegan and Lahey built their argument on three organisations. Here is where those three are now.

Bridgewater has been the subject of sustained independent scrutiny, and the record is not good. In 2020 an arbitration panel found that the firm had manufactured false evidence against two former employees and brought one of its claims in bad faith. Those findings became public through the former employees’ own court filing, and Bridgewater has contested them, which is worth saying because it is exactly the kind of detail a sympathetic reader would prefer to skip. The firm’s own culture page now records that, when its founder handed over in 2022, it re-underwrote several of his principles.

Next Jump still describes itself as recognised by Harvard in 2016 as one of two top learning organisations globally, which turns out to be a reference to this book rather than to any institutional recognition. Decurion’s two cinema chains closed permanently in 2021; the company itself carries on.

And the wider evidence base is thinner than its reputation. I went looking, this month, for the studies that should exist after ten years of a widely taught idea: an evaluation of the model, a validated instrument for measuring it, longitudinal work, a replication at a fourth organisation. I did not find them, in any of the journals where they would belong. I would be glad to be corrected, and please send me a feedback if you think I missed something. The book selected three organisations that were already doing well and described their practices, which is the oldest problem in management writing: you cannot tell from a sample of winners which of their habits caused the winning.

So why am I still arguing for this?

Because none of that touches the mechanism, and because the alternative is worse.

The Bridgewater story is a story about what happens when developmental practice runs on a control infrastructure, with one person holding both the interpretive authority and the recording equipment. That is precisely the first precondition, failing exactly as you would predict. It is evidence for the design argument, not against it.

And the absence cuts both ways. The critical traditions that would take this model apart, the research showing peer discipline in self-managing teams turning out harsher than managerial discipline, and the work on identity as a form of control, have not been turned on these three organisations either, at least not anywhere I could find. We are not choosing between a tested claim and an untested one. Everybody here is operating on theory.

What we are choosing between is a specified proposition and an unspecified one. “Humanity will save us” cannot be wrong. “Name the capability, allocate the work developmentally, book the cost, protect the first bad attempt, and put it in the portfolio with an owner” can be wrong, in about six places, and you would find out.

So let me state my position plainly, including the part I cannot support.

I have seen these elements work, in pieces, across twenty-five years of transformation and change work. An upskilling programme built into the build decision of a strategic workforce plan, where developing people was costed and set against buying or borrowing them, which is the argument of this issue in miniature. The operating model of a spin-off, designed from the start as a service, where people grew into roles that had been drawn around a real flow of work. A redesign of how stores serve customers at the point of sale, where the learning followed the design instead of preceding it.

What those have in common is not a culture programme. In each case development was attached to a structural decision, and it worked because it had somewhere to land. I have never seen it work as a standalone initiative. And I have never seen the coherent version either: all of it, designed, owned, funded, in one organisation, over enough time to tell. That step is an assumption, and I am making it on purpose. I think it is time somebody tried this deliberately: take development off the culture slide, put it in the capability portfolio next to the structural decisions it depends on, and give it an owner who has to defend it.

How you would know it was working

An assumption you cannot disconfirm is just a belief with better manners, so here is the test, and it cuts in both directions.

A manager who develops people is running slower on purpose. They are giving work to someone who will do it worse and take longer, at a moment when a machine would do it instantly. That is a real cost, paid now, locally, by them, for a return that arrives later and lands somewhere else.

If that cost is invisible in how they are measured, they will stop. And they will be right to stop.

So the question is not whether your organisation believes in development. Everyone believes in development. The question is whether anything in your management system currently makes it rational to pay for it. Look at how your managers are actually assessed, this quarter, and find me the line where running slower on purpose is a credit rather than a variance to be explained.

If you can find it, and someone owns the capability that line protects, you are building the real thing, whatever you choose to call it. If you cannot, then you do not have a developmental organisation. You have a developmental aspiration, and the AI wave will make the arithmetic worse every quarter until somebody with the authority to redesign the measurement decides otherwise.

Who that somebody is, is next issue’s problem.

Leave a comment

Sergio

References

1. Barends, E. and Rousseau, D. M. (2022). Organisational culture and performance: an evidence review. Scientific summary. London: Chartered Institute of Personnel and Development.

2. D’Egidio, F. (2003). La Nuova Bussola del Manager. ETAS.

3. Fukuyama, F. (1995). Trust: The Social Virtues and the Creation of Prosperity. New York: Free Press.

4. Kegan, R. and Laskow Lahey, L. (2016). An Everyone Culture: Becoming a Deliberately Developmental Organization. Boston: Harvard Business Review Press.

5. Ruppel, E. H. (2024). Therapeutic management in the low-wage workplace. Social Science & Medicine, 352, 117026. https://doi.org/10.1016/j.socscimed.2024.117026

6. Zucca, A., Bieber, K., Ghignone, E. and Solari, L. (2026). Il ruolo necessario degli artefici della libertà del self-management. Harvard Business Review Italia, luglio-agosto 2026. https://www.hbritalia.it/luglio-agosto-2026/2026/07/01/news/il-ruolo-necessario-degli-artefici-della-liberta-del-self-management-16622/

2. Site Updates

The new sergiocaredda.eu is live.

For twenty years my site grew the way most organisations do: by accretion. Every strategy left a layer behind, posts that had outlived their purpose, hundreds of tags, series that had lost their order. So I rebuilt it with the question I ask of any organisation I work with: what is this thing for, and does its structure say so?

What you will find there now:

1. The Organisation Evolution Framework at the centre, with a hub for each of its eight components. The Corporate Culture hub is the one this issue argues with.

2. 281 models of leadership, organisation and strategy, each read through the same lens, among them the Deliberately Developmental Organisation.

3. Three pillars, Organisation, Work and People, written to argue rather than to file.

4. Twenty years of archive, kept and labelled rather than rewritten. Which is also why my 2019 review of An Everyone Culture is still there, exactly as I wrote it.

The most useful moment came on the last morning. An audit found my own working notes showing on sixteen published pages: the gap between the design and what actually runs, which I have seen in every organisation I have worked in. This time I could close it before lunch.

I have also closed the separate LinkedIn page I kept for organisation design. The work and the person were always the same thing, so everything now lives on the site, in this newsletter and on my personal profile. If something on the new site looks broken, or you cannot find a piece you remember, just hit reply.

3. Reading Suggestions

• AI implementation isn’t just execution. It’s discovery. — Stuart Winter-Tear, Unhyped AI. The Substack pick. Putting AI into real work keeps revealing things the organisation did not know about itself, which makes redesign something you discover as you go rather than a step after the plan. A good companion to an issue arguing that development only works when it has a structural decision to attach to.

• Coaching and its effectiveness in organizations: Reflecting on a decade of research — Woods, Jones & Guillaume, Journal of Occupational and Organizational Psychology, 99(2), 2026. The scientific pick, from Zotero. The authors look back over ten years of research since their 2016 meta-analysis on workplace coaching. Same decade, same kind of developmental promise as An Everyone Culture, and a very different evidence trail. Worth reading side by side.

• Why AI is making organization theory even more relevant — Nicolay Worren. Agents need organising for many of the same reasons people do, and the old questions about dividing and coordinating work come straight back. Organisation design becoming more relevant with AI, not less, is a thread I will keep pulling.

• Why Job Architecture is Failing Us — Lance Haun, Beacon Turn. Job structures were already struggling before AI, and AI is now pulling roles apart in two directions: some amplify expertise, others let less experienced people do the work. Read it with this issue in mind: an architecture of jobs is not an architecture of capabilities.

• RenDanHeYi from Day Zero: Building a Startup as a Distributed Organization — Emanuele Quintarelli, Chaordian. A small startup built as a distributed organisation from its first day, with autonomy designed in and extended step by step. The opposite of copy-and-paste, and a small-scale picture of the intentional design I argue flat structures need.

4. The (un) Intentional Organisation 😁

5. Keeping in Touch

Don’t hesitate to reach out by directly hitting “reply” to this newsletter or using my blog’s contact form.

I welcome any feedback on this newsletter and the content of my articles.

Find me also on:

• LinkedIn

• Instagram

• Medium

• Refind

• GoodReads

• Readwise

1. The Missing Junior Loop

Ben has been with the firm eighteen months. He is bright, he was well taught, and he wanted this job.

His work now consists of reading what an AI Agent produced and deciding whether it is right.

He is good at it, in the way you can be good at something in your second year at work: he catches the obvious errors, the ones where the output contradicts itself or the numbers do not add up. He does not catch the other kind. The wrong figure that sits in the third paragraph looking exactly like the eleven that are correct.

Nobody blames Ben. Everybody understands, without saying it, that there was no way he could have known.

(Ben is a composite person. But I have watched this pattern in enough organisations to be confident it matches the real experience of many)

Two issues ago I described an analyst whose craft was made of the slow, irritating, preparatory work that AI has now removed, and who was left checking outputs. Her name was Sarah, and several of you wrote to me about her.

Sarah checks against thirty years of knowing what wrong looks like. Ben checks against nothing.

In some organisations they have the same job title. They appear on the same dashboard, doing the same task, at the same rate, and the organisation cannot tell them apart on any instrument it owns. But it will find out the difference when Sarah retires. And it will bite.

There was a deal, and almost nobody wrote it down

Your first job: you arrive without much to offer. What you have is time and a willingness to do the parts nobody enjoys: the first draft that gets rewritten three times, the data that has to be cleaned before anyone can look at it, the case summary, the simple ticket, the routine call, the powerpoints. In exchange, and gradually, you become someone who can do the real work.

The unattractive truth in that arrangement is that the firm was underpaying you, and both sides knew it. Economists are blunter about this than we are in HR. Luis Garicano and Luis Rayo state it plainly in a working paper: across the economy, juniors pay for training by doing menial tasks. The scut work was not the price of the job. It was the currency.

And the reason it worked, rather than being simply exploitation with a career ladder painted on, is that the work was doing something while it was being done. Cleaning the data is how you learn what dirty data looks like. Writing the summary that comes back covered in red is how you learn what a partner is actually reading for. The friction was the medium, not overhead.

I have sat through enough Organisational Development programmes to say this with some confidence: nothing in a curriculum reproduces that. What the preparatory work supplied was consequence, not content: owning an ambiguous problem where being wrong had a cost, and then being wrong. You can teach the content in a fortnight. You cannot teach consequence at all. You can only arrange for it, and arranging for it is a decision about how work flows, not a decision about learning content.

It goes by several names: experience, seniority, mastery.

This newsletter title is not mine.

Christian Catalini, Xiang Hui and Jane Wu call it the **Missing Junior Loop**, in a preprint published in February that models what happens to an economy when cognition decouples from biology. Their mechanism is the sharpest statement of the problem I have read:

The marginal cost of measurable execution falls to zero, absorbing any labor capturable by metrics — including creative, analytical, and innovative work. The binding constraint on growth is no longer intelligence but human verification bandwidth: the capacity to validate, audit, and underwrite responsibility when execution is abundant.

Which means the arrangement we are all currently sitting in, where the human stays in the loop and checks, is not a resting place. In their reading it is unstable, and it erodes from two directions at once: from below, as the apprenticeship that produced the checkers collapses, and from within, as the experts who remain codify their own expertise into the systems that will not need them. We are consuming the stock of verification capacity while congratulating ourselves on the flow.

No HR function I know of has a policy for the catch-22 in this. The judgement you need in order to supervise AI is acquired by doing the work AI has taken. How do you learn what wrong looks like from a job that consists entirely of being shown things that look right?

But Ben is an analyst, and plenty of people think we can manage without analysts

I want to take that seriously, because I suspect some of you are already thinking it. Ben works somewhere that sells advice. There is a widely held and not entirely unfair view that this layer was overstaffed, that a good deal of what juniors produced in professional services was volume rather than value, and that if AI thins it out the world will cope.

Fine. Then let’s go where that argument cannot be made.

The biologist. The doctor. The lawyer. The structural engineer.

In none of these is mastery a nice-to-have the market might reprice. They are the professions, which is to say mastery institutionalised: a transmissible body of skill, a progression from apprentice to journeyman to master, an identity bound to what you can do. They are also the one place where we already decided, collectively, that removing the preparatory work is not permitted. It is written into law. The supervised hours, the logbook, the pupillage, the residency, the years spent working under someone else’s signature. Nobody in medicine calls that inefficiency. They call it a condition of practice.

Which makes the professions the control group for everything I have just argued. And the evidence there is not soft. The relationship between how many times a surgeon has performed an operation and how many of their patients die is one of the better-established findings in health services research. Volume, and outcome. In surgery, the link between doing the work and being good at the work is not a theory about learning. It is measured in mortality.

Now apply the mechanism. AI does not remove the residency. It removes the content of the residency while the hours stay exactly where they were. The registrar still does the years. What the registrar no longer does is the hundred unremarkable scans that build the eye which catches the hundred-and-first. The trainee solicitor still completes the training contract, having never sat with the disclosure long enough to develop a nose for the document that does not belong.

And they will qualify. Should that worry you? The credential survives; the competence does not, and the credential is precisely the thing the rest of us rely on in order to stop checking.

This is the missing junior loop with a professional licence attached, and it is the same shape as Ben’s. The only real difference is that one of them has a regulator and the other has a dashboard. That changes who notices, and when. It does not change the mechanism.

But do not mistake the licence for protection. The regulated professions protect the credential, not the layer beneath it — and the preparatory work lives in that layer. Paralegals are not regulated; trainee solicitors are. So the efficiency drive arrives first exactly where the feeder sits, and the credential above it carries on certifying people whose formation has quietly been removed.

We already have the case. In 2025 a solicitor became the first lawyer in Australia to be professionally sanctioned over AI— stripped of his ability to practise as a principal, after giving the court a list of prior cases, generated by legal software, that did not exist. He had not checked them.

He was, in every formal sense, qualified.

There are objections...

Of course, there are objections to this. Two in particular.

The first is that none of this is happening.

The evidence usually cited is Brynjolfsson, Chandar and Chen’s payroll analysis, which finds a 16% employment decline for 22-to-25-year-olds in the most AI-exposed occupations, relative to older workers in those same occupations, not in absolute terms. That distinction is usually dropped in the retelling, and it matters. The number’s size matters less than its shape: the same occupations show no equivalent decline for experienced staff. Whatever is happening is happening at one end of the ladder.

Against it: Natalia Emanuel, Emma Harrington and Amanda Pallais argue from Federal Reserve data that the cause is remote work rather than AI: managers became reluctant to hire people they could not train by proximity, and juniors working remotely receive less feedback. And more bluntly, the *Financial Times* found the AI-redundancy story mostly unsupported: Carl-Benedikt Frey at the Oxford Internet Institute has seen no compelling evidence these firms are automating much of anything and thinks many announcements were made to impress investors; Oxford Economics calls the evidence patchy and notes the absence of the productivity growth mass automation would produce; executives polled by the NBER expect AI to cut headcount by 0.7% over three years. Meanwhile interest rates rose, the pandemic overhiring unwound, and the sectors most exposed to AI are the same ones most exposed to the cost of capital.

That is a serious case and I am not going to pretend otherwise. So let me make the concession properly: my argument does not need AI to be the cause of the hiring decline. It needs something smaller and much harder to argue with: where AI absorbs the preparatory work, the developmental gradient goes with it. That is a claim about the firms which did hire. It survives the attribution fight entirely, because it is not in it.

Two things are worth noting all the same. The Stanford authors did control for remote work: standard teleworkability classifications, measures of how many postings offer remote or hybrid work, and the analysis run separately for teleworkable and non-teleworkable roles. The young-worker divergence persisted, and it persisted when technology firms and computer occupations were removed altogether, which is inconvenient for the interest-rate story. And the Financial Times piece, which spends two thousand words dismantling the AI-layoffs narrative, contains one sentence conceding the opposite: more AI-exposed entry-level occupations have suffered dramatic falls in hiring. When the sceptical case and the alarmed case agree on exactly one point, why would you attend to anything else?

The second objection is better, and it comes from the same economists who described the deal.

Garicano and Rayo model AI entering the master-apprentice relationship and find it does two opposite things at once. It raises the floor, because the menial work is done free now, and the currency the apprentice was paying with has vanished. But it raises the ceiling too: a well-trained junior with these tools produces far more than one without, which makes them more worth having. “It’s a race between the two,” Rayo says. “One shrinks profits, the other one grows profits, and whichever one wins determines whether an apprenticeship is profitable.”

The optimistic reading stops there and concludes the gradient is replaced rather than removed: juniors start higher, learn faster, arrive better. It is a reasonable thing to hope. What their own law-firm illustration shows is why hoping is not enough: because juniors now begin on advanced work, they finish training sooner, and a shorter apprenticeship returns less to the partners who funded it. The ceiling rising can destroy the apprenticeship rather than save it. The good outcome and the bad one run through the same door.

They even give it a threshold, which I find both absurd and clarifying. Careers stay viable, in their model, when the ratio of what a fully-trained graduate produces to what a novice who just clears the AI bar produces exceeds Euler’s number, e (roughly 2.72). Below it, the senior’s saleable knowledge shrinks, training compresses, and what they call wholesale career collapse follows.

We have arrived at the point where whether it is worth training a human is a number, and the number is e.

Nobody is coming

Why is this different from every other engagement problem I have written about?

In the last issue I used Andrew Marritt’s finding that what actually moves engagement is reducing the rate at which people become disengaged: re-engaging the already-disengaged barely shifts the number, and where it does, it comes from structural change rather than from a programme.

Apply that to Ben. The structural change that would work is the gradient. There is nothing available short of rebuilding the thing that was removed, and no organisation rebuilds a gradient as a wellbeing initiative. So the mentoring scheme will not fix it. The learning platform will not fix it. And the graduate development programme, which is where this lands in most companies, is a curriculum, which is precisely the thing that cannot manufacture consequence.

None of it will appear in a score, either, because Ben leaves before any survey catches him.

The cost surfaces as attrition, three years later, in a cohort nobody was measuring. This is the cost that no function owns, from the last issue, showing up in its second location. It is worse here, because the first time round the cost was diffuse and this time it is a person walking out of the building. We track tokens consumed and hours saved with real precision. We hold no lead indicator whatsoever for whether judgement is forming: not correction rates, not time-to-trust, not how often a junior catches a confident and wrong output. The instruments measure the efficiency of the work and are blind to the formation of the worker.

One organisation has done the obvious thing. IBM has reportedly tripled its US entry-level hiring this year on the explicit reasoning that gutting the pipeline now buys a senior shortage in 2029, and has rebuilt those roles around validating and integrating rather than producing. Whether it works is a question for the end of the decade. That it is a contrarian bet, and reported as one, tells you where the rest of the market is standing.

What was actually being built

The word I keep reaching for is one I inherited.

Franco D’Egidio, whose book I wrote about two issues before this one, used employeeship (Claus Møller’s term) for the qualities that make someone a capable participant in an organisation rather than merely an occupant of a role: responsibility, initiative, competence. His point, and Møller’s, was that these are not taught. They form through being trusted to act, allowed to be wrong, and made to absorb the consequence.

Checking output develops none of the three.

It cannot develop responsibility, because the work was never yours.

It cannot develop initiative, because the task begins after every decision has been taken.

And it develops a strange, hollow competence: real fluency at recognising a wrong answer, in someone who has never had to produce a right one.

That third one is the oldest idea in this issue and the one I have circled all the way through it. Competence, in the sense the professions mean it and the sense the guilds meant it before them, is mastery: a standing you earn by answering to the standard of the work itself, over time, in public, in front of people who can tell. It is not a stock of knowledge you hold. It is the reason a surname like Smith or Ferrari or Schneider exists at all: for most of European history the craft was the name, because what you could do was who you were. Remove the gradient and you do not merely slow that down. You sever the only route to it we have ever had.

So the missing junior loop is an organisation design failure with a delay on it, and the delay is roughly the length of a career. It is not, in the end, a hiring statistic or a labour-market argument. We are removing the preparatory work because it was the easiest thing to justify removing, and we were right that it was inefficient. We were simply wrong about what it was for.

The next newsletter issue will ask what we want people to become. I do not think that question can be asked honestly yet.

Because before you ask what you want a person to become, you have to be straight about what you took from the ones who were already on their way, and about the fact that you took it without ever deciding to.

So, one question, and I would like your answer. In your organisation, where does a twenty-four-year-old now go to be wrong about something that matters?

Leave a comment

If you can name the place, tell me. I would like to know what it looks like, because I am not finding many.

If you cannot, then you already know what your firm will be short of in 2032.

Sergio

References

• Catalini, C., Hui, X., & Wu, J. (2026). Some Simple Economics of AGI. arXiv:2602.20946. https://doi.org/10.48550/arXiv.2602.20946

• Garicano, L., & Rayo, L. (2025). Training in the Age of AI: A Theory of Career Viability. CEPR Discussion Paper DP20634.

• Brynjolfsson, E., Chandar, B., & Chen, R. (2025). Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence. Stanford Digital Economy Lab.

• Emanuel, N., Harrington, E., & Pallais, A. (2026). Remote Work Leaves Younger Workers Sidelined. Liberty Street Economics, Federal Reserve Bank of New York. https://doi.org/10.59576/lse.20260601

• Møller, C. (1994). Employeeship: The Necessary Prerequisite for Empowerment. Empowerment in Organizations, 2(2), 4–13.

• Birkmeyer, J. D., Stukel, T. A., Siewers, A. E., Goodney, P. P., Wennberg, D. E., & Lucas, F. L. (2003). Surgeon Volume and Operative Mortality in the United States. New England Journal of Medicine, 349(22), 2117–2127. https://doi.org/10.1056/NEJMsa035205

2. Site Updates

A note on the site itself: sergiocaredda.eu hasn’t been updated in some time, and I’m aware of it. The blog rework is one of the things on my list for the next few months, and the Organisation Evolution Framework page itself will be revised as part of that work.

One thing that has stayed alive is the Leadership Models Collection, which I’ve continued to expand quietly through this period. Most recently I added entries on Multipliers, Humble Leadership, Host Leadership, Complexity Leadership Theory, and Resonant Leadership. It’s becoming a useful reference resource on its own, even ahead of the broader site refresh.

3. Reading Suggestions

1. The Certainty at the Heart of AI Anxiety — Nir Eyal. The Substack pick. Eyal’s argument is that the fear of AI comes dressed as a prediction and is really a demand for certainty, and that the useful move is to separate what you actually know from what you have decided to believe. Read next to an issue that spends half its length refusing to claim more than the evidence carries.

2. Workplace Learning and the Future of Work — Beier, Saxena, Kraiger, Costanza, Rudolph, Cadiz, Petery & Fisher, Industrial and Organizational Psychology, 18(1), 2025. The scientific pick, from Zotero. Eight authors arguing that our field has taken an organisation-centred view of development — people developed as a means to the firm’s ends — and that it needs a person-centred one. The exact argument this issue does not make, by people better qualified to make it.

3. Il part-time obbligato che colpisce soprattutto gli uomini e i più giovani — Fabio Savelli, Corriere della Sera. Involuntary part-time work in Italian retail, falling hardest on the young. A completely different mechanism from anything in this issue, producing the same outcome: an entry point that no longer carries anyone anywhere.

4. Why Change Is Outpacing Absorption Capacity — Latent Variables. On the changes an organisation never sees — local, unlogged, and cumulatively larger than anything in the change portfolio. The measurement instinct here is the right one, and it is the instinct this issue argues is missing everywhere else.

5. Management consulting as we know it is over — Jannie van Zyl, TechCentral. The deck was never the value, and now the deck is free. What is left is people who have actually done the thing — which raises the obvious question of where the next ones come from.

4. The (un) Intentional Organisation 😁

5. Keeping in Touch

Don’t hesitate to reach out by directly hitting “reply” to this newsletter or using my blog’s contact form.

I welcome any feedback on this newsletter and the content of my articles.

Find me also on:

• LinkedIn

• Instagram

• Medium

• Refind

• GoodReads

• Readwise

1. The Adoption Debt

In May, Uber’s President and COO was asked whether the company’s very large spending on AI coding tools was producing better products for the people who use Uber. His answer, on the record, was that the link is not there yet.

Uber is not a laggard. Ninety-five per cent of its engineers use AI tools every month, around seventy per cent of the code they commit is machine-generated, and the company had spent its entire 2026 budget for these tools by April. Its Chief Operating Officer still cannot draw a line from any of it to a single thing a customer would notice — which is awkward, because we are all being sold the same story, this time by people selling models instead of methods.

The metrics we are reaching for — seats deployed, tokens consumed, percentage of engineers “using AI weekly” — are all measures of input. We have built an entire reporting apparatus for a transformation, and it measures how much we are putting in rather than what is coming out. Uber, Microsoft and Duolingo have now all said versions of this out loud, which suggests something structural rather than three companies being unlucky.

Let me name what I think is happening, and then spend the rest of this issue arguing with myself about it.

The gain is booked at the task. The cost lands in the system.

Two definitions, because the distinction is the whole argument:

1. A task-level gain is real, local, and immediately legible. The analyst drafts the summary in four minutes rather than forty. It appears in the tool’s own dashboard, which is precisely why we can see it.

2. A system-level cost is real, distributed, and legible to nobody in particular. The extra coordination because three teams now produce four times the output that must still be integrated. The review load. The quiet erosion of shared context that used to make handovers cheap.

The first goes into a slide. The second goes nowhere, because no function owns it — and a cost that no function owns is not a cost, it is a deferral.

I am not describing anything new. Four years ago I wrote about Organisational Debt — Aaron Dignan’s idea that organisations pay interest when their structures and policies stay fixed while the world moves, and that the interest arrives as reduced speed, capacity, flexibility, innovation and engagement.

Adoption Debt is a species of that genus, incurred by one specific decision of this decade: deploying an extraordinary capability into a coordination architecture designed for something else. What I wrote then was that the debt is usually out of control before anyone notices, because there is no awareness of its impact until conditions make it obvious the interest cannot be paid. That is a reasonable description of a COO discovering in April that the year’s budget is gone.

Let’s see what it looks like from inside a company that is actually good at this.

Zapier is about as favourable a case as exists. Wade Foster describes what happened when the engineers started producing much more code: the bottleneck moved. It moved to code review — and fixing it was not a matter of giving reviewers better tools. They had to rebuild the review process from the ground up, get time-to-ship from days down to minutes, and it required the whole engineering organisation to agree to work differently, not one engineer to work faster.

That is the entire argument in an operator’s own words. Task-level gain, cost lands in the coordination layer, and the remedy is a design decision nobody can take alone. Zapier paid the debt down. Most organisations are still booking the gain.

Now the honest part, because the evidence is not as clean as I would like.

The obvious move here would be to reach for the engagement numbers. Gallup’s most recent report puts global engagement at twenty per cent for 2025 — a second consecutive annual decline from a peak of twenty-three per cent in 2022, and the first back-to-back fall since the series began in 2009. It is tempting to hold that against the AI adoption curve and let you draw the line.

I am not going to, because the source does not support it. Gallup’s own reading points at managers: manager engagement has fallen nine points since 2022, and the historic gap where managers were more engaged than their teams has closed entirely. That timeline starts in 2023, before most of us had deployed anything at scale.

So the honest version of my argument is weaker than the one I wanted, and I think more useful. AI did not break the coordination layer. It is arriving on top of a coordination layer that was already thinning. We spent a decade removing management layers in the name of agility, and we are now pushing an enormous volume of faster, less contextualised output through what remains. The debt is not the cause of the collapse. It is the accelerant on a fire somebody else lit.

But here is what I actually want you to take from the Gallup number, and it is not the number.

Gallup did not find the story in the headline figure. Twenty per cent tells you nothing. They found it when they cut the data by hierarchy level — and a flat, unhelpful score turned into a structural finding about where the load had moved. One cut, one question somebody thought to ask.

Andrew Marritt makes a sharper version of the same complaint, and it is the best thing I have read on engagement measurement in years. The evidence underneath it is unambiguous: across daily diary studies, about forty-two per cent of the total variance in work engagement sits *within* the person rather than between people. It moves day to day, with conditions, resources and events. Measuring it once a year, he says, is like photographing a river and calling it the flow.

A score describes the current state of the stock. The text explains the rate.

Which is exactly the distinction this newsletter has been circling. A stock is what you have accumulated. A rate is what is happening to you. And when Marritt models engagement as flows rather than a level, the practical finding is uncomfortable: what moves the number is reducing the rate at which people become disengaged. Re-engaging the already-disengaged barely shifts it — and where it does happen, it comes from structural change, a new manager, a new role, a new team, not from a programme. There is, he notes, no good evidence that organisation-wide engagement initiatives, better communication or recognition schemes produce individual recovery.

Read that next to the debt metaphor and it stops being a measurement argument. You cannot repay organisational debt with a programme. You repay it by refactoring. Marritt has the data for a claim I made four years ago from theory and experience.

And it raises the question I cannot stop turning over. If one cut by hierarchy revealed that much, how many cuts are we not making?

Different Meanings. Diversified Debts.

Because the engagement survey, as almost all of us run it, asks people how they feel about their work as though work meant the same thing to all of them. It does not.

Let’s consider three people in the same function, answering the same instrument, this quarter.

1. Sarah, our first employee built a craft. The slow, irritating, preparatory part of the job — the drafts nobody saw, the versions that failed — was not overhead. It was the medium through which she became good. AI has removed exactly that, efficiently and completely, and left her checking outputs. She does not experience this as relief.

2. Jack, our second employee, was on a gradient. Every year the work got harder in a way that made him “more”. He could feel himself becoming someone. That gradient has flattened — not because anyone demoted him, but because the rungs he would have climbed are now done by a system and nobody replaced them with different rungs. He is not disengaged. He is becalmed, which is a different thing and reads identically on a five-point scale.

3. Clara, the third, never chose any of this. The work was imposed, the relationship to it already transactional, and now it is faster and more surveilled, because the monitoring is built into the tools we just bought. AI has not removed her meaning. It has removed the last of the slack in which meaning might have been improvised.

All three answer the same question. All three land inside one number. The number falls by a point, and we write in the board pack that engagement is a concern.

The strongest objection to all of this comes from Foster himself, and I want to put it properly rather than wave at it. His view is that we assess loss far more easily than gain, that the tasks AI is taking were frequently not very interesting or valuable in the first place, and that removing them frees people for more ambitious work.

He may well be right about the aggregate.

My difficulty is that “not very valuable” is a judgement made from outside the work, usually by someone who is not doing it — and the preparatory tasks that look least valuable from a dashboard are very often the ones through which capability is actually built.

The question is not whether those tasks were worth doing. It is whether anything has been designed to replace what they were quietly producing.

So what is this a failure of?

I keep returning to a phrase from the book I wrote about in the last issue. D’Egidio’s diagnosis of Italian companies in 2003 was that they were sovragestite e sottoguidate — over-managed and under-led.

Twenty-three years on we are doing something with a familiar shape: deploying an extraordinary technology to make existing processes faster, rather than asking what value we are trying to create and what Work would have to look like to create it. That is a machine response to a living-system problem, and it is the most expensive kind of imagination failure, because it looks like progress the entire time it is happening.

Which makes it an Operating Model question. Not “how do we roll out AI” but: if this capability is real, what should the flow of value look like now — and which coordination, integration and meaning-maintenance work has to be designed in, because it used to happen for free in the layer we removed?

I do not have a tidy answer. I have a strong suspicion about where the bill lands first, and it is not on the three people above. It is on the ones who never got to build the craft at all — the juniors, whose entire development loop was made of the tasks we have just automated. That is the next issue.

For now, one question, and I would like your answer. In your organisation, who owns the cost that no function owns?

If you can name them, tell me how they got the job.

If you cannot, I think you have found your adoption debt.

Leave a comment

Sergio

2. Site Updates

A note on the site itself: sergiocaredda.eu hasn’t been updated in some time, and I’m aware of it. The site rework is currently being done, and you will see a complete new revamp at the end of August. Keep tuned!

3. Reading Suggestions

1. AI Is Moving Faster Than the Organisation Can — Richard Claydon, Leadership, Rewritten. Three clocks running at different speeds — the tool, the workflow, the institution — and the strain of their misalignment quietly absorbed by the people translating between them. The best short statement I have read of why adoption is not transformation.

2. Studying Within-Person Changes in Work Motivation — Navarro, Rueff-Lopes & Laurenceau, Revista de Psicología del Trabajo y de las Organizaciones, 38(1), 2022. The methodological companion to the argument above: if you want to see change inside a person rather than between people, you need far more measurement points than anyone actually collects. Which is a polite way of saying most of what we run cannot detect what we claim to be looking for.

3. The Analyst Who Changed Nothing — Andrew Marritt. Part two of the series that gave this issue its stock-and-rate distinction. People analytics has spent fifteen years improving analysis quality when the thing that matters is decision quality.

4. The AI Operating Model Is Discovered, Not Designed — Stuart Winter-Tear also lands in my exact same direction, i.e. of AI adoption being an Operating Model issue, that needs to be “discovered”.

4. The (un) Intentional Organisation 😁

5. Keeping in Touch

Don’t hesitate to reach out by directly hitting “reply” to this newsletter or using my blog’s contact form.

I welcome any feedback on this newsletter and the content of my articles.

Find me also on:

• LinkedIn

• Instagram

• Medium

• Refind

• GoodReads

• Readwise

1. The Book That Shaped My Thinking

Dear friend,

The first proper job I ever had came with an odd instruction: write this man’s book, but make it sound like him, not like you.

It was the autumn of 2000. I had just walked into the Milan offices of Summit as a freshly minted Human Capital consultant — a title I fell in love with the moment I first saw it published on the job-search portal. The man was Franco d’Egidio, a prolific author of management books and an adviser to a good number of the more interesting Italian entrepreneurs of his generation. Over the next few years I helped him write three of them. The one I keep coming back to is La Nuova Bussola del Manager, published by ETAS in 2003.

I want to write about that book today. Not to review it — that would be a strange thing to do to a book you helped write — but because I have slowly realised that almost everything this newsletter (and some of my previous writings) has been arguing for the past few years was already sitting in its pages, in a language I did not yet have the experience to read.

Ghostwriting does something peculiar to you. You spend months thinking inside someone else’s argument, holding distinctions you did not draw, defending positions you did not originate and might not yet believe. You learn a structure of thought before you have earned it. And then, if you are fortunate, twenty years later you discover you have grown into it — and that the ideas you were merely transcribing have been quietly running your head ever since.

Let me pick out five of them. I will be brief with each, not because they are small, but because each one deserves — and is going to get — its own treatment in the issues ahead of this Newsletter.

1. Organisations are not machines.

The first is that the organisation is not a machine. D’Egidio’s starting move was to call most companies sovragestite e sottoguidate — over-managed and under-led. Drawing on von Bertalanffy’s systems theory and Gareth Morgan’s images of organisations, he insisted that a company is closer to a living thing than to a mechanism. A machine can only optimise the present. A living system can renew itself. That distinction is the seed of everything I now believe about operating models.

2. Value is a system, not a number.

The second is that intangible value is a system, not a number. One of D’Egidio’s big intuitions was that Intangibles needed proper measurement to be “revealed”, so that a company’s real value, behind pure financials could be seen, developed fully in the other book I worked on with him: Il bilancio dell’intangibile. There you can follow the concept from D’Egidio’s original intuition through to the methodology the Summit team and I built around it. The worth of a company is based on four interdependent capitals — human, social, structural, relational — that feed one another rather than sitting in separate columns. Years later, that became, for me, the argument that organisational capability is a portfolio somebody has to hold, not a stack of unrelated assets.

3. Employees, not Leaders alone make the success of an organisation

The third is a concept that belonged to the network Summit was part of and that D’Egidio helped develop: employeeship. It came from Claus Møller and Time Manager International — TMI — and it names something we still barely have language for — that you cannot have good leadership without good followership, and that the disposition to take responsibility, show initiative and stay loyal to a shared aim is itself a designable organisational quality. We have shelves of books on how to lead. We have almost none on how to be led well contributing to the organisation’s success.

4. Trust is an infrastructure

The fourth is that trust is infrastructure, not sentiment. Following Fukuyama, the book argued that control and trust are not two ends of one dial but two incompatible operating logics, each with its own cost. Every unit of control has to be paid for — in surveillance, approvals, defensive paperwork. Every unit of trust pays you back. You cannot bolt a trust initiative onto a control architecture and expect anything but cynicism.

5. The importance of vitality

The fifth is that every organisation has a vital force that can be measured before the financials show the problem. D’Egidio called this vitalità d’impresa — corporate vitality — and it was, in many ways, his most original contribution (also set out in an earlier book: La Vitalità d’Impresa). The argument is not simply that organisations fail from the inside, which is true but not his idea. It is that the decline is legible early, if you know what to read. Long before revenue stalls or margins compress, the vital signs are already moving: energy draining from the culture, initiative retreating upward, trust converting quietly into compliance. He spent years building a diagnostic instrument — the Early Warnings Decision System, built on eight intangible indicators — to detect exactly that. The idea that you can take an organisation’s temperature, not just its pulse, is one I have never stopped finding useful. In 2026, with AI adoption curves and engagement collapse moving faster than any annual survey can track, it feels less like a management tool and more like a survival instinct.

Now, the honest objection — because I can hear it, and I have made it myself. A sceptical reader would say this is exactly the thing we do to our own pasts: we read the present back into them and call it foresight. There is even a literature for it. Eric Abrahamson’s work on management fashion describes how ideas like intellectual capital and distributed leadership move through the corporate world in waves, manufactured and sold by consultancies, gurus and business magazines. D’Egidio had the enviable capability of turning simple management ideas into business opportunities that had an impact. The twenty-three books and two thousand-odd articles he wrote are there to account for it. So, yes, I do accept the objection, but the fact that Intellectual Capital is once again coming out as an imperative in the age of AI, shows that seeds were planted back then.

But here is why I do not think it settles anything. The test of an idea is not whether it was fashionable when it was written, but whether it still cuts once the fashion has passed. And these ones cut harder now than they did then — because AI has made every one of them live again. We are being told, once more, that our organisations are machines to be optimised, this time by people selling models instead of methods. The control logic has never been cheaper to choose: the monitoring is built into the tools. The living-system question, the trust question, the lock-in question are not nostalgia. They are the questions a frightened decade is most tempted to skip.

There is one more thing I took from d’Egidio — less a chapter than a habit of mind he taught in person. He used to say a leader needs three faculties: insight, foresight and intuition — an argument he develops across several chapters of Vision and Leadership, the book of his that sold most. What follows is my own compression of it rather than a passage you could quote back to him. Insight is reading the present clearly — seeing what is actually there rather than what convention suggests. Foresight is holding several possible futures at once without collapsing too early onto one. Intuition is the compressed past: the pattern recognition that lets you act before the evidence is complete. Three time signatures, really — the now, the not-yet, and everything you have already lived. And the five ideas are really those three faculties in working clothes: to see an organisation as a living system rather than a machine is an act of insight; to build trust and capability for a future you cannot yet read is foresight; to feel the vital signs fading before the numbers show it is intuition.

It is also, I realise, the shape this newsletter has been tracing without my planning it. The last stretch of issues was an exercise in insight — trying to see the design gap clearly. What comes next leans on foresight, holding the AI-era futures open long enough to think. And the whole thing rests on intuition, the part I cannot fully account for, which is presumably why a book I transcribed at twenty-five is still the one I am arguing with at fifty.

With an added note. The line he quoted more than any other was McLuhan’s: se qualcosa funziona, è obsoleto — if it works, it’s obsolete. He borrowed it, and made it his own. A real jolt for someone like me, who has always believed transformation and change are good things.

So this is less a tribute than a confession of lineage. Twenty years on, the ideas I was once just typing have turned out to be the compass — the bussola — I never stopped using. What I keep asking myself is which of the things we are being sold today will look, in another twenty years, like the seedbed of something real, and which will look like the fashion of a frightened decade. I do not know yet. I suspect the difference will come down to whether we treated our organisations as machines or as living things.

More on each of these, in the issues to come.

Leave a comment

Sergio

2. In memory of Franco D’Egidio

Franco D’Egidio died on the first of August 2007. This issue publishes nineteen years later, in the same week.

I worked with him at Summit between 2000 and 2005, my first job. I helped him write three books, taking his ideas from conversation and whiteboard and turning them into tools managers and entrepreneurs could use (this is how he intended his books). The acknowledgements are generous about what I contributed. What they do not say, is what that process gave me.

He had a way of holding an argument the way a good navigator holds a compass — with enough confidence to commit to a direction, and enough humility to know the sea changes. He thought organisations were alive. He thought people deserved to be treated as the primary asset, not a cost line. He wrote that before it was fashionable, and he meant it in a way that many people who now write it fashionably do not. Yet, he was able to influence and leave a mark to some of the most important entrepreneurial companies in Italy still today, like Brembo, Technogym and Intercos.

He also took these ideas into places most consultants would not follow — public administration, non-profit, small entrepreneurs, sectors where the gap between management theory and daily practice was widest. He did not write for an audience of people who already agreed with him.

In the last weeks of his life he launched the Intellectual Capital Value Award — a prize for companies that had done real work on their intangible capital. The honorary committee he assembled for it ran from Confindustria and ASFOR to the president of AIAF, the Italian association of financial analysts. That he could persuade a body of financial analysts to put its name to a prize for intangibles says something about how far he had pushed the argument. He died a few days later. The award was given the following year and dedicated to his memory.

Men learn as long as they live. Organisations live as long as they learn.

He believed that. He was right.

Sergio

3. Site Updates

A note on the site itself: sergiocaredda.eu hasn’t been updated in some time, and I’m aware of it. The site rework is currently being done, and you will see a complete new revamp at the end of August. Keep tuned!

4. Reading Suggestions

1. The Uncertainty in the Room — Jan Oliver Schwarz, The Foresight Brief. The clearest short piece I have read on foresight as a discipline: not predicting one future but building the tolerance to hold several. The middle faculty of the triad above, written up for 2026.

2. Is command-and-control leadership back in fashion? — Fast Company. A neat illustration of the trust-versus-control binary returning under the cover of crisis — and of how quickly a management fashion can swing back when leaders are anxious.

3. Will AI Yield Abundance Without Purpose? — Carl Benedikt Frey, Project Syndicate. Frey asks whether automation can manufacture meaning as easily as it manufactures output. It cannot, which is precisely why the human-capital question never went away.

4. What you can learn about organisations from a controversial German philosopher — Sam Spurlin. A Heideggerian argument that an organisation lives in its relationships and purpose, not in its org chart — the living-system metaphor arriving from an entirely different door.

5. Keeping in Touch

Don’t hesitate to reach out by directly hitting “reply” to this newsletter or using my blog’s contact form.

I welcome any feedback on this newsletter and the content of my articles.

Find me also on:

• LinkedIn

• Instagram

• Medium

• Refind

• GoodReads

• Readwise

1. Capabilities in Search of an Owner

I already wrote that, in most organisations, nobody holds the brief for Work Design. Any conversation touching the topic would go quiet at exactly that point — the moment someone at the leadership table asked so who fixes this? HR looks at IT, IT looks at Operations, Operations looks back at the business, and the question dissolves into a shared project, which is another way of saying no responsibility at all. Something that is getting more acute with AI implementations and the need to look into treating this as an organisational issue, not an adoption problem.

That silence is still there. And with it, the need to design the capabilities that should underpin an organisational adventure into AI. What is happening is that a lot of individuals are practicing with these new tools, developing insights, creating agents, reviewing governance models. Every LinkedIn post recently has been framing this: AI is creating “personal productivity”, but this is not scaled at organisational level yet. This also means that skills and capabilities are getting formed in many organisations as we speak. But there is no design behind these, no architecture, no plan, no connective tissue.

It reminded me of Luigi Pirandello’s Sei personaggi in cerca d’autore. Pirandello’s six characters wandered a stage looking for an author to complete them. Similarly, these critical capabilities that we miss collectively are wandering the org chart looking for someone to hold them and nourish them.

But someone has joined them in the room. Three real executives, at three serious companies, each now holding a different hypothesis of the answer. Same question, three different structural solutions — and the reason this issue exists is that all three, to me, feel incomplete in the same way.

Three answers that are already live

I’m sure you have read a lot about them. In many ways, my reference is not only to the people themselves and the roles they are now doing, but more to the noise of commentators around these. Here the cases I’m referring to.

When Moderna folded IT into HR under Tracey Franklin — now Chief People and Digital Technology Officer — it gave one leader the brief to “architect the flow of work”, with 3,000-plus custom GPTs running alongside roughly 5,800 people and the CIO role absorbed. This is the merger answer. Its limit is that merging two functions does not necessarily settle who owns Work Design — it builds a larger silo under a single budget, and the design question survives inside it.

ServiceNow took a different route, making Jacqui Canney Chief People and AI Enablement Officer: HR identity preserved, AI enablement bolted explicitly onto the brief. This is the expanded mandate answer. Its limit is that a title can expand without the authority underneath it changing at all — it may be a signal with no substance, depending entirely on what the mandate actually lets the holder decide.

Microsoft did the most revealing thing of the three. It moved Kathleen Hogan out of the Chief People Officer role into Chief Strategy and Transformation Officer. Not a rename — an exit. This is the migration answer, and it is less a solution than a tell: the most capable people leaders are not relabelling HR, they are leaving it for strategy. Which suggests the ceiling on the brief is the function itself, not the title on the door.

The difference that matters

Each of these is an intention — a course of action, taken. What none of them formalises is the intent — the purpose the action serves, written down as a brief someone is accountable for holding. I drew that distinction a few years ago, almost as a language game; it has never been more load-bearing than it is here. Three organisations have acted intentionally, but in the weak sense. And none of them has named the intent.

The market is enlarging this scope faster than anyone is naming it. Spencer Stuart, surveying 101 European HR executives, found that 43% of CHRO roles now carry responsibilities outside the classic HR remit, and that the expanded remits come bundled with shorter tenures and higher intensity. That is a headhunter describing demand, not a consultancy describing aspiration — which is exactly why it is worth trusting. The scope is growing whether or not the org chart admits it.

The objection I take most seriously

The strongest argument against the way I am framing this comes from Phil Kirschner, who says the integration everyone keeps describing is missing its third dimension. HR thinks about people. IT thinks about tools. Nobody owns place — corporate real estate has no stable home in the org chart, drifting between Finance, HR, and the COO. The brief, he argues, is People plus Technology plus Place, and AI raises the stakes of physical space rather than lowering them: once the coordination overhead is automated, the only question left is which rooms are actually worth being in. Sharon Doherty already carries the title Chief People and Places Officer at Lloyds. Kirschner’s model for the role is deliberately modest — a Fitbit for the organisation, a pit crew — something that makes the cost of misalignment visible without trying to command it.

He is right about place (it’s a real dimension of Work Design, and one that has been changing a lot since the Pandemic and the quarrels about remote work), and is also right that this is where the framing gets dangerous. Because every time we answer a coordination failure by appointing someone to own it, we risk manufacturing yet another silo rather than removing a gap. The C-suite has been growing new titles for a decade — Chief Digital, Chief Transformation, Chief AI — and a Chief of Work that simply sits beside the CHRO and the CIO, instead of changing what they are accountable for, is one more coordinator, not one fewer gap. The proliferation of “Chief of…” titles is itself a symptom of the disease it claims to treat.

Where I land

So the answer is not a new title. It is a brief — and the discipline to hold it as one accountable mandate rather than three borrowed ones.

But naming the brief is not the same as being able to hold it. Here is where the three answers we have seen above fail. Each fixes a layer: Moderna the boxes, ServiceNow the title, Microsoft the person. But the real gap is none of those. The mandate has outgrown the model underneath it: the reporting lines, the shape of the HR function itself, the decision rights over headcount, tooling and space were never built to carry a brief this wide. Redraw the org chart and you reproduce the gap, because the thing that actually needs redesigning is the operating model.

Holding the brief, concretely, means owning that layer: the authority to rebalance the capability portfolio, not merely the accountability to describe it.

And there is a harder gap underneath even that one, the one this arc of newsletters has circled without quite naming.

Whoever holds Work Design has to hold it knowing what work actually means to the people doing it: not a configuration problem, but a source of meaning, identity, and sometimes dignity. That is where these pages will go next.

This issue closes an arc of newsletters dedicated to capabilities and configurations supporting AI, not as an objective, but rather as a “disturbance” element that is re-framing how we approach organisations. I have now asked the question of who owns Work Design, and have provided evidences and named the issues underpinning this Big Hairy Question across issues 49 to 55. And have found that I am not alone…

Work Design is becoming a more widespread context for many commentators lately.

If you sit in an organisation that has tried to hold this brief as one job — or tried and watched it fragment back into three — or maybe experimented with an alternative governance model — I would genuinely like to hear what that looked like. Send a comment or hit reply.

Leave a comment

Meanwhile, I’ll sit on stage like the Father in Pirandello’s pièce — pleading, incomplete, a little undignified by this situation — still looking for my Author.

Sergio

References

• Maguire-Vielle, C., & Wilhelm, B. J. (2026). More than HR: The Rise and Rise of the CHRO. Spencer Stuart.

• Kirschner, Phil. “Chief of Work Role and Responsibilities | The Workline.” Blog. The Workline, April 16, 2026. https://walktheworkline.com/article/what-i-now-believe-about-the-chief-of-work.

2. Site Updates

A note on the site itself: sergiocaredda.eu hasn’t been updated in some time, and I’m aware of it. The blog rework is one of the things on my list for the next few months, and the Organisation Evolution Framework page itself will be revised as part of that work.

One thing that has stayed alive is the Leadership Models Collection, which I’ve continued to expand quietly through this period. Most recently I added entries on Multipliers, Humble Leadership, Host Leadership, Complexity Leadership Theory, and Resonant Leadership. It’s becoming a useful reference resource on its own, even ahead of the broader site refresh.

3. Reading Suggestions

1. Self-Determination Theory in Work Organizations: The State of a Science — Edward Deci, Anja Olafsen & Richard Ryan, Annual Review of Organizational Psychology and Organizational Behavior. The definitive synthesis of why autonomy, competence and relatedness — not incentives — drive motivation at work. The evidence base sitting underneath the question this issue only opens: what work actually means to the people doing it.

2. TBM 430: Incubate, Compound, Refinance, Liquidate — John Cutler, The Beautiful Mess. Cutler treats a software estate as a portfolio of assets, each facing a live decision to grow it, fix it, or retire it. The cleanest external mirror I have seen of the capability-portfolio logic this arc has been building — the same discipline, pointed at code.

3. Cathedral Projects — Margaret Heffernan. On work that outlasts the people who start it, and what it takes to commit to something you will never see finished. A useful counterweight to an org-design conversation that usually stops at this quarter’s reorg.

4. The Great Talent Recalibration — Bain & Company. Shrinking workforces, tighter capital and automation are pushing the CHRO from managing employees to designing a workforce that blends humans and machines. The demand-side case for exactly the expanded brief this issue is circling.

5. The Paradoxes of the Knife — Dave Snowden, The Cynefin Co. On pruning: the hard, context-dependent judgement of what to cut so that something else can grow. Read it beside this issue’s claim that holding the brief means deciding what to shed, not only what to own.

4. The (un) Intentional Organisation 😁

5. Keeping in Touch

Don’t hesitate to reach out by directly hitting “reply” to this newsletter or using my blog’s contact form.

I welcome any feedback on this newsletter and the content of my articles.

Find me also on:

• LinkedIn

• Instagram

• Medium

• Refind

• GoodReads

• Readwise

1. The Operating Model HR Built for Itself

There is a specific kind of structural problem that only becomes visible in reverse. You look at a design and ask: what was this actually for? And suddenly the solution is obvious — and so is the fact that the problem it solved no longer exists.

That is where HR stands with the Ulrich model.

Dave Ulrich did not invent the three-box structure in 1997 out of bureaucratic instinct. He named what was already emerging because specific structural problems demanded specific structural answers. Shared Services existed to pool transaction volume — the administrative work of HR was too costly to distribute across every business unit. Centres of Excellence existed to concentrate specialist knowledge — expertise in reward design, learning architecture, or employee relations was too scarce to replicate locally. HR Business Partners existed as a translation layer — because neither of the other two boxes could reach the business without someone who understood both languages.

These were real solutions to real problems. The CIPD’s 2024 research confirms the model remains by far the most widely adopted operating model within HR. But the same report quietly notes what practitioners have been feeling for years: HRBPs often lack the time or skills for what is actually being asked of them; COEs become inflexible silos that respond too slowly. The structural strain was already there before AI arrived.

AI did not break the Ulrich model. It dissolved the conditions that made it necessary.

The volume argument is gone. AI agents handle tier-1 HR transactions — policy queries, leave requests, basic onboarding guidance — at near-zero marginal cost. The administrative work that justified pooling in Shared Services is increasingly the work that agents do without sleeping.

The scarcity argument is gone. When specialist knowledge can be embedded in a model and accessed by anyone with a prompt, the rarity of expertise no longer justifies concentration in a Centre of Excellence. What the COE was protecting — analytical depth, domain knowledge, the ability to see patterns across the organisation — is precisely what AI augments most aggressively.

The translation argument is going. AI-powered self-service is not augmenting the HR Business Partner — it is bypassing them. Managers who can query workforce analytics directly, generate first-draft job evaluations, or access policy guidance through a conversational interface have less need for a human intermediary. The HRBP was never just a relationship role. It was an information asymmetry role. That asymmetry is collapsing.

Three arguments, three boxes. The arguments are dissolving. The boxes remain.

The uncomfortable question

Here is what makes this hard. The function that is most needed to lead the redesign of its own operating model is also, empirically, one of its most consistent resistors. Pereira, Graylin and Brynjolfsson’s analysis of 51 enterprise AI deployments found:

Staff functions — Legal, HR, Risk, Compliance — were the primary source of institutional resistance in 35% of cases, ahead of frontline workers fearing replacement and ahead of end users generally (23%).

Wrong. HR is not resistant because it is backwards.

HR’s incentive architecture produces risk aversion as a rational outcome: avoid regulatory exposure, manage reputational risk, protect the function’s liability position. When an algorithm makes a hiring decision or a compensation recommendation, HR carries the accountability — and the legal exposure — for that decision. Risk aversion is not a cultural failure. It is a structural response to a structural incentive.

The governance reframe is the resolution. In the deployments where staff functions shifted from blocking to actively supporting AI adoption, the mechanism was the same: give Legal and HR a role in designing governance rather than a role in approving outcomes. Accountability with design authority produces different behaviour than accountability without it.

Which raises a question the CHRO has to hold simultaneously: you are the client of this redesign and the one who must lead it. The redesign of the HR operating model has to include a redesign of HR’s own governance reflexes — not just its org chart.

Marianne Roux frames the sharpest version of the objection: the HR function must reinvent itself before it can reinvent the organisation. She is largely right — and much of what she calls for, HR reorganising around outcomes and challenges rather than processes, is the same direction I am pointing. Where I part company is the word before. “Itself first” assumes a clock HR controls. It does not: the structural logic is dissolving from the outside in whether or not HR has put its own house in order. The question was never whether to reinvent before leading. It is how to hold both at the same time.

What the consulting mainstream has not yet said

The field has produced plenty of responses to the dissolution of the Ulrich model. Bersin’s “full-stack HR leader” is the most prominent: a strategic generalist who oversees talent advisory, AI orchestration, culture, and organisation design as the transactional layer automates away. It is a compelling image. But it is an optimisation of the current architecture rather than a redesign of its logic.

The Mercer Target Interaction Model lands in a similar place. Thoughtful, carefully evidenced — and still, fundamentally, the Ulrich model with updated role names.

Perry Timms offers something more structurally honest. His concept of the Polymorphic Organisation is built on a specific observation: most transformation programmes fail because they force a choice between legacy and future modalities, when what organisations actually need is to hold both simultaneously — in what he calls organisational oscillation. The critical implication for HR is where he locates the powerhouse of this oscillation: not in leadership, not in technology, but in OD — Organisation Design, Development and Effectiveness. Whoever holds that brief with genuine authority is the one who can navigate the oscillation rather than be consumed by it.

And we’re seeing this every day: more and more non-HR practitioners are writing about AI being not a technology adoption issue, but rather an Organisation Design challenge.

The fair orchestration of human development into intentionally designed organisational settings is what is needed to fill the gap.

That is the question the dissolution of the Ulrich model is actually asking. Not: what do we put in the boxes instead? But: who is accountable for the design logic itself?

Three questions worth sitting with

The field does not yet have a convincing answer to what replaces the three-box model at scale. I have been in enough rooms where this conversation happens to say that plainly. What I am more confident about is the shape of the questions that will determine the answer.

These are not HR questions. They are design questions. Which is the point.

What do you think?

Leave a comment

Sergio

References

• CIPD (2024). Do Current HR Operating Models Serve Future Needs? Chartered Institute of Personnel and Development.

• Pereira, E., Graylin, A. W., & Brynjolfsson, E. (2026). The Enterprise AI Playbook: Lessons from 51 Successful Deployments. Stanford Digital Economy Lab.

• Ulrich, D. (1997). Human Resource Champions: The Next Agenda for Adding Value and Delivering Results.Harvard Business School Press.

2. Site Updates

I’m currently working on a revision of my site structure, particularly reviewing some its key content items. Stay tuned for a full refresh, which I hope will be available in a few weeks.

You can however watch myself in the last “Work Matters” podcast by .

3. Reading Suggestions

1. Decision Architecture: Why Organizational Speed Is Designed, Not Demanded, by Hassan Tirmizi | Linkedin — A really interesting take on Decision Debt and the fact that Speed in organisation design cannot be earned if not taken into consideration in design.

2. Designing the Future-Ready HR Function by | Kogan Page — a solid point of view on the need to focus on operating model design.

3. Defining a Complex System by | The Cyncefin Co — After many years defining what a Complex System means, Dave Snowden experiments a definition of what it actually means.

4. How AI Is Reshaping Workflows and Redefining Jobs — Kristin Burnham, MIT Sloan. The clearest statement of the point underneath this whole issue: AI acts on workflows, not tasks, so the unit that has to be redesigned is the operating model, not the tool.

5. Designing the Future-Ready HR Function — Kogan Page. Treats HR as an engineering function that designs how work and decisions flow — the same move from organising around types of work to organising around the design logic itself.

6. Ten Reasons Why We Won’t See Productivity Improvements from GenAI by — A useful cold shower: the gains do not arrive until the work is redesigned, which is exactly why bolting AI onto the three-box model changes so little.

7. Ford Has Been Rehiring Quality Inspectors After AI Fell Short — Keith Naughton, Bloomberg. A concrete answer to this issue’s closing question about where a human still has to be in the room: the judgement that could not be encoded had to be rehired.

4. The (un) Intentional Organisation 😁

5. Keeping in Touch

Don’t hesitate to reach out by directly hitting “reply” to this newsletter or using my blog’s contact form.

I welcome any feedback on this newsletter and the content of my articles.

Find me also on:

• LinkedIn

• Instagram

• Medium

• Refind

• GoodReads

• Readwise

1. The Blind Spot

The standard HR read on AI is by now familiar. You commission an impact analysis and it comes back role by role: this job loses these tasks, that one gains these tools, headcount holds, a handful of hours are saved per person per week. The picture looks under control. The trouble is that the picture is drawn at the wrong scale.

What AI actually does to Work does not happen inside the job. It happens between jobs, and across the edges of the organisation. Production is a sequence of steps that firms continually re-bundle — some done by people, some by machines, some pushed outside the firm altogether. Work shifts to the customer who now self-serves, to the machine that now drafts the first version, to the competitor whose platform quietly absorbs a function you used to own. A job-level analysis is structurally blind to all of this, because the unit it measures — the job — is not the unit that is moving.

You can already see the gap in the data we do have. A Berkeley study of how AI actually lands on knowledge workers found that it tends to intensify work rather than reduce it: scope widens, expectations rise, the day stretches, and 83% of those studied reported a heavier load rather than a lighter one. A job-level analysis that records “four hours saved” and a system-level reality of “workload up and climbing” are describing the same deployment. Only one of them is true to the people living inside it.

This is the first half of HR’s blind spot: the wrong unit of analysis. The second half is the wrong default question.

The wrong question

Most people analytics begins with the data. What patterns are in our engagement scores, our attrition curves, our usage logs? It is the natural posture of a function that has finally been handed dashboards. But people analytics earns its keep only when it changes a decision, and starting from the data rather than from the decision is the field’s oldest failure. The right opening question is not “what patterns exist in our data?” but “what decision needs to be better, and what would we need to know to make it?”

AI makes this worse, not better. A system that finds patterns at near-zero cost will hand you an infinite supply of them. The scarce resource was never the pattern; it was the clarity about which decision the pattern is meant to serve. Expand the supply of answers and the absence of a question becomes the binding constraint.

What the instruments cannot see

Put the wrong unit and the wrong question together and you get a function that measures tasks and time-saved with rising precision while missing the thing that actually moved: accountability.

Back in 2022 I argued that accountability is not responsibility. Responsibility can be delegated through a clean process, but accountability can only be personally accepted. AI introduces a harder version of the same problem for the decision-making processes HR is supposed to govern. When an automated process mediates a decision and then fails, accountability does not disappear; it relocates to the nearest human — regardless of whether that person had any real control over the outcome. Madeleine Elish calls these moral crumple zones: the human in the loop absorbs the blame the way a car’s crumple zone absorbs an impact, to protect the integrity of the system.

That is the displacement HR’s instruments are not built to detect. A role can look untouched on a job-level dashboard — same title, same tasks, a few hours saved — while quietly becoming an accountability sink — Dan Davies’s term, in The Unaccountability Machine, for the place in a system where responsibility drains and pools — for decisions a model now makes and no one formally owns. The 2022 problem was that accountability is hard to give away. The 2026 problem is that it can be loaded onto someone who never accepted it and cannot see it coming.

A clear example is the many instances of automated processing in Rewards. Letting an LLM learn reward practices from past decisions risks deploying the same biases that modern processes are designed to avoid. And if it does, who is accountable?

The honest counter-argument

The strongest objection to all this is that the job is exactly the right unit, precisely because the job is where everything attaches: employment law, reward, headcount, skills, the contract itself. “Work shifting across boundaries” is real but too diffuse to manage, and the answer is not to abandon job-level analytics but to mature it. On this reading, AI in HR is only as strong as the people-analytics foundation beneath it, and the discipline is becoming exactly the capability that lets an organisation decide, deliberately, where human judgement stays in control.

I think the maturity case is half right, and the half it gets wrong is the dangerous one. Maturing your measurement of the wrong unit does not close the blind spot — it measures the wrong thing more precisely, and lends it more authority. A more sophisticated job-level dashboard is still a job-level dashboard.

The capability HR is building is real; it is simply pointed at the question that is easiest to answer rather than the one that matters.

What this looks like in practice

In Issue #52 I described the “Gioia for HR” pilot at Campari — a digital twin built with the HRBPs who actually do the work, on the principle that every AI use case is designed with today’s job holder. The measurement discipline is the same move turned on the analysis itself: before asking what the data shows, we try to name the decision the use case is meant to improve, and who stays accountable for it once the model is in the loop. It is harder than commissioning another dashboard, and we are early…. But it changes what you look at — not hours saved, but accountability located.

**The question worth asking**

Issue #51 named the governance gap]; Issue #52 made it human. This issue is about the instruments themselves. HR cannot redesign what it cannot see, and right now it is measuring its own transformation at the wrong scale, opening with the wrong question, and missing the one variable — accountability — that AI is quietly moving around the system.

Some are already redrawing the mandate: Richard Rosenow argues that HR is really a regulatory control function, whose real job is to decide where the variability of a workforce gets absorbed — and the accountability sink is what forms when that job goes undone. That is the operating-model question Issue #54 takes up. Before then, one question is worth sitting with.

The next time an AI-impact analysis lands on your desk and reassures you that the jobs are holding and the hours are saved — what is it not showing you, and who is now accountable for the part it left out?

Leave a comment

Sergio

References

• Elish, M. C. (2019). Moral Crumple Zones: Cautionary Tales in Human-Robot Interaction. Engaging Science, Technology, and Society, 5, 40–60. https://doi.org/10.17351/ests2019.260

• Demirer, M., Horton, J. J., Immorlica, N., & Lucier, B. (2026). Chaining Tasks, Redefining Work: A Theory of AI Automation (NBER working paper).

• Davies, D. (2024). The Unaccountability Machine: Why Big Systems Make Terrible Decisions — and How the World Lost Its Mind. Profile Books.

2. Site Updates

I wrote a new Essay : Organisational Grammar: The Atomic Elements of Designed Coordination — introduces the six atomic elements (Role, Skill, Group, Decision, Obligation, Accountability) as the underlying vocabulary of any organisation design work. If this issue’s argument — that the skill is the right unit for Work Design decisions — resonates, the Grammar piece is the structural companion.

The Laws of Organisation Design series continues on the blog: the Law of Requisite Variety and the Law of Alignment are both up. The Laws series is the slower, more structural companion to this newsletter’s arc.

3. Reading Suggestions

• ‘Can a machine do this job?’ is the wrong question — Carl Benedikt Frey (Financial Times). The sharpest statement of this issue’s first move: AI rarely deletes a job outright, it shifts the work to the customer who now self-serves. “Can a machine do this job?” is the wrong question; “can the customer do without it?” is the real one.

• The goal isn’t an AI-native organization, it’s an AI-legible one — Iain Roberts. AI fails where authority, accountability and process are not *legible* — machine-readable. A useful mirror to this issue: you cannot measure what you have not made legible, and accountability is the first thing to blur.

• When Everyone Uses AI, Companies Risk Losing Critical Skills — Charikleia Kaffe et al. (BCG). The capability cost of the residual-task problem: lean too hard on AI and judgement quietly atrophies. The antidote is design — workflows that keep people challenging the output rather than rubber-stamping it.

• Every hire is a bet made in the dark — Andrew Marritt (Working Ideas). A long, careful piece on the irreducible uncertainty in hiring. Marritt is the analytics voice behind this issue’s “what decision needs to be better?” — here he shows what taking a single decision seriously actually looks like.

• Are Apprentices an Endangered Species? — Luis Rayo (Kellogg Insight). If AI absorbs the basic tasks juniors once learned on, where does the next generation of judgement come from? The accountability question, pushed one career-stage upstream.

4. The (un) Intentional Organisation 😁

5. Keeping in Touch

Don’t hesitate to reach out by directly hitting “reply” to this newsletter or using my blog’s contact form.

I welcome any feedback on this newsletter and the content of my articles.

Find me also on:

• LinkedIn

• Instagram

• Medium

• Refind

• GoodReads

• Readwise

1. The Work Left Behind

The operating model assumption behind most AI deployments runs something like this: AI handles the routine, humans move to higher-value work, productivity follows. Issue #51 showed why the governance infrastructure for that transition is missing. But before governance, there is a more immediate question — what actually happens to the people inside organisations that deploy AI without redesigning the work?

The answer is not what the story predicts. And the majority of organisations are running the experiment without knowing it: Deloitte’s 2026 State of AI in the Enterprise found that 54% of organisations are now scaling AI projects to production — but only 30 to 34% are deeply redesigning jobs. Most are applying advanced AI to unchanged human workflows.

The Residual Task

When AI automates tasks inside a job rather than the job itself, what remains is the supervisory overhead: monitoring, checking, validating AI outputs. This is what Orie calls the Residual Task Problem — a configuration that is cognitively fragile by design.

The reason is straightforward. Hackman and Oldham established in the 1970s that three task characteristics reliably predict engagement: skill variety, task significance, and autonomy. Monitoring an AI’s outputs scores poorly on all three. The work is thin, the contribution is invisible, and the margin for individual judgement is narrow. The brain does not avoid effort; it avoids wasted effort. Removing the meaningful tasks while retaining the supervisory residual is precisely the wrong configuration.

This is the more common experience. But it is not the most dangerous one.

Brain Fry

A BCG study of 1,488 workers, published in HBR in March 2026 (Bedard, Kropp, Hsu et al.), found that AI oversight is the most mentally taxing form of AI engagement — more taxing than using AI collaboratively or as a tool. High oversight predicts 14% more mental effort, 12% more mental fatigue, and 19% more information overload. Workers who hit full Brain Fry — 14% of the sample — experience 33% more decision fatigue, 39% more major errors, and a 39% higher intent to quit.

The asymmetry is what matters. AI replacing repetitive tasks predicts a 15% reduction in burnout. Delegation and supervision are not the same cognitive operation. Organisations deploying AI at scale and measuring engagement without distinguishing which kind of AI interaction people are having are systematically misreading their own data.

It is also not primarily a tooling problem. Brain Fry is predicted by oversight responsibility, not interface complexity. Better UX will not resolve it. The task configuration is load-bearing, not the tool design.

Why No One Is Fixing This

The structural diagnosis is uncomfortable. In most enterprises, job design sits in HR and job evaluation. Process design sits in Operations or Transformation. AI governance sits in IT or Legal. Work Design — the integrated practice of deciding who does what, in what configuration, with what oversight model — has no natural owner. The gap is structural, not attitudinal.

The invisible cost problem compounds this. Pereira, Graylin and Brynjolfsson, in their analysis of 51 successful AI deployments (The Enterprise AI Playbook, Stanford Digital Economy Lab), found that 77% of the hardest practitioner-identified challenges were invisible: change management, process redesign, data quality. The technology was consistently the easiest part. Crucially, 61% of those successful projects had at least one prior failed attempt whose costs never appeared in the successful project’s accounting.

The strongest counter to this diagnosis is that AI deployment itself surfaces redesign needs that would otherwise remain invisible — making deployment a discovery mechanism rather than redesign a precondition. The evidence complicates this reading: 61% of successful projects had a prior failed attempt. Discovering the redesign need through failure is not the same as designing from the outset. The costs were paid; they were just paid by the organisation that failed, not the one that eventually succeeded.

The J-curve that Issue #50 described stays flat not because AI is weak, but because the complementary investments — chiefly, redesigning the work itself — are deferred.

This is precisely what I am driving at Campari. Across the several dozen AI use cases we are experimenting with in HR, we adopted a design principle from the start: every use case must be designed together with the job holder of today, so that work considerations are built in from the outset. We are working on this in multiple ways; the most interesting is “Gioia for HR” — a pilot HRBP digital twin where HRBPs from across the organisation are collectively building the institutional context the tool needs to work well. You could argue that it is easier to do this within HR. For me, it is vital to pilot this inside HR precisely to prove the principle where I have full accountability before it can scale elsewhere. The results are telling: Work Design requires cross-functional thinking, closer integration between HR and technology, and deliberate routines for sharing learning across organisational boundaries. And you arrive at the same conclusion: work design is the missing piece.

What Intentional Work Design Looks Like

The governance signals are accumulating. Tobi Lütke’s April 2025 mandate at Shopify — requiring teams to demonstrate why AI cannot do a job before requesting headcount — is a blunt instrument. But it is a Work Design instrument: it reframes every role design question as an automation boundary question.

Microsoft’s Camp AIR, reported by Spataro in May 2026, is the more operational model: two weeks of protected redesign time before enabling Copilot agents. “Redesign first. Then AI just works.” treats redesign as infrastructure, not overhead.

The empirical case is sharper. Across those same 51 deployments, organisations using escalation models — where humans review AI-flagged exceptions rather than approving each output — achieved 71% productivity gains. Approval models achieved 30%. The oversight model is the highest-leverage Work Design choice available in an AI deployment, and it is routinely left undesigned. This is not a claim that every organisation should use escalation logic — context, risk level, and task type all affect the right configuration. It is a claim that not deciding is itself a decision, and usually the costliest one.

The clearest operational illustration is IKEA. When its Billy AI automated 57% of routine customer support conversations, IKEA did not reduce headcount. It analysed what the remaining 43% of interactions required, found they were advisory — customers seeking spatial planning and home design guidance — and redeployed agents as interior design consultants. The redesigned function generated more than €1 billion in new global revenue. The cost centre became a profit centre. None of this happened automatically. It required a deliberate decision about what the elevated human layer should do.

The Load-Bearing Piece

Work Design has always been load-bearing — it is the mechanism, as Issue #35 set out, through which an organisation’s productivity coefficient is actually set. What has changed is the cost of ignoring it. When AI handles the routine and leaves humans with the residual, the absence of design is immediately felt: in the fatigue numbers, the error rate, the quit intent.

The useful framework here is the elevation space: as AI compresses the routine substrate of a role, it simultaneously expands the layer of contextual judgement, exception handling, and institutional accountability that humans must fill. The Residual Task and Brain Fry are both symptoms of organisations that have let AI compress the substrate without deliberately designing into the elevation space.

The practical unit for that design work is the skill — a bundle of related tasks with a coherent capability, small enough to redesign and large enough to govern. At the skill level, you can decide what AI augments, what it replaces, what oversight model applies, and who is accountable for the quality of that configuration.

Issue #51 named the governance gap. This issue makes it human. The distance between AI deployment and J-curve payoff is not filled by technology confidence, training programmes, or better tools. It is filled by the intentional redesign of work — and that requires someone to own it.

What do you think?

Leave a comment

Sergio

2. Site Updates

I wrote a new Essay : Organisational Grammar: The Atomic Elements of Designed Coordination — introduces the six atomic elements (Role, Skill, Group, Decision, Obligation, Accountability) as the underlying vocabulary of any organisation design work. If this issue’s argument — that the skill is the right unit for Work Design decisions — resonates, the Grammar piece is the structural companion.

The Laws of Organisation Design series continues on the blog: the Law of Requisite Variety and the Law of Alignment are both up. The Laws series is the slower, more structural companion to this newsletter’s arc.

3. Reading Suggestions

1. When Using AI Leads to Brain Fry — Bedard, Kropp, Hsu et al. (BCG / HBR, March 2026) The primary research behind this issue. 1,488 workers, three types of AI engagement. The oversight configuration data is the most actionable finding for anyone currently deploying AI at scale — the asymmetry between replacement and supervision is not intuitive, and the quit-intent numbers are striking.

2. The Residual Task Problem — Nico Orie (LinkedIn) Short, sharp, and immediately applicable. The framing that set me thinking for this issue. Worth three minutes.

3. The Enterprise AI Playbook — Pereira, Graylin & Brynjolfsson (Stanford Digital Economy Lab) 51 successful AI deployments, analysed for what actually made them work. The 61% prior-failure statistic is buried in most write-ups; the escalation/approval productivity split rarely gets the attention it deserves.

4. Redesign First. Then AI Just Works. — Jonathan Spataro, Microsoft WorkLab (May 2026) The Camp AIR account from the inside. The clearest practitioner case for Work Design treated as a governance decision rather than an afterthought.

4. The (un) Intentional Organisation 😁

5. Keeping in Touch

Don’t hesitate to reach out by directly hitting “reply” to this newsletter or using my blog’s contact form.

I welcome any feedback on this newsletter and the content of my articles.

Find me also on:

• LinkedIn

• Instagram

• Medium

• Refind

• GoodReads

• Readwise

1. From Diagnosis to Capability

The conversation I keep having goes like this. Someone at the leadership table raises an AI deployment that is not working — the productivity gains are not showing up, teams are frustrated, the vendor is blaming implementation. Discussion moves quickly to what’s actually wrong. *The work around the tool was designed for a pre-AI process. Nobody redesigned it.* The room agrees. Then: so who should fix this? The room gets quiet. IT points to process. Operations points to HR. HR points to Strategy. Strategy points back to the business. Nobody has the brief.

That quiet is what this issue is about.

Where we are

Issues #49 and #50 established the diagnosis: the gap between what AI can do and what AI is producing inside organisations is not a technology gap. It’s a design gap — specifically, the absence of anyone who owns Work design as a discipline, with the seniority and the cross-functional brief required to actually redesign how Work flows across the enterprise. The J-curve of general-purpose technologies — documented across steam, electricity, computing, and now AI — requires complementary investments in process redesign, organisational restructuring, and new governance before the productivity shows up in the data. Those investments require someone accountable for making them. That someone, in most organisations, does not exist.

So the question shifts. It’s no longer is there a design gap? That’s answered. The question is: what kind of capability would actually close it?

The capability that already exists

Before reaching for something new, I want to take the counter-argument seriously. A reasonable observer would say: we have had strategic workforce planning for decades. We have L&D functions, transformation offices, centres of excellence. The capability is not absent — it is distributed.

That’s right, and that’s the problem.

Strategic workforce planning is the closest institutional cousin to what I’m describing — the discipline that’s supposed to translate business strategy into capability requirements and then decide how to source them. Most organisations run some version of it. And in most organisations, it has been captured: absorbed into the Finance cycle (annual headcount, not continuous rebalancing), filed into the HR taxonomy (job families and pay bands, not capability as an asset class), and redirected toward compliance reporting (who has what licence, not who can do what work). The vocabulary of strategic workforce planning invokes portfolio thinking — buy, build, borrow — but applies it transactionally, one decision at a time, without the strategic logic the framing requires.

The capability exists in name. The discipline has been institutionally defanged. This is not an absence problem. It is an institutional capture problem — and the implication is different. The argument is not for building something new. It is for reclaiming something existing and granting it the mandate it was never actually given.

Treating capability like a portfolio

The framing that keeps returning — and I have tested several — is portfolio management. Not as a metaphor — as a discipline imported in full.

A portfolio manager cannot simply want more of everything. They allocate under constraint, make trade-offs explicit and visible, rebalance as conditions shift, and are accountable for the portfolio as a whole. Portfolio discipline is how pension funds, infrastructure managers, and R&D leaders handle assets that are large, slow to build, and strategically important. Organisational capability is all three of those things — and is almost never managed with the equivalent discipline.

Applied to capability, portfolio management imports four things most organisations currently lack:

1. Explicit trade-off reasoning. Every buy/build/borrow decision is a trade-off between cost, speed, quality, and strategic dependency. Most organisations make these implicitly, defensively, or under budget pressure — which means the trade-off gets made by whoever happens to be in the room, not by whoever is accountable for the outcome.

2. Continuous rebalancing. Financial portfolios are not reset once a year at budget time. Organisational capability needs the same continuous recalibration — especially under AI adoption, where the relative value of specific capabilities can shift within quarters. Annual headcount planning is the wrong cadence for a problem that moves at this speed.

3. Risk as an explicit dimension. Borrowing capability heavily hollows out institutional knowledge. Building it heavily creates lock-in. Buying it heavily disrupts culture. These second-order effects are predictable — but they only become visible if someone is holding the whole portfolio picture and asking about them explicitly.

4. Executive ownership at the level where trade-offs can be made. A portfolio has a manager who is accountable for the whole, not for individual line items. Organisational capability is currently split across HR, line management, Finance, and procurement — with no one holding the integrated picture, and therefore no one able to make the integrated call.

The portfolio framing does something that the existing language of strategic workforce planning does not: it creates a named accountability gap that cannot be dissolved into shared responsibility. Portfolio management requires a portfolio manager — not a committee, not a dotted-line arrangement across Finance and HR, but a named role accountable for the whole. The moment you apply the discipline in full, the question of who holds it becomes unavoidable. Strategic workforce planning never forced that question because its vocabulary always fitted inside the existing functional silos — headcount to Finance, job families to HR, licences to IT. Capability Portfolio Management does not fit in any of those containers.

This is not a speculative claim. Three decades of research on dynamic capabilities — following Teece, Pisano, and Shuen’s foundational work — has established that organisations capable of continuously sensing and reconfiguring their capability base outperform those that manage capability reactively. The portfolio management discipline is the institutional instrument that makes reconfiguration systematic rather than episodic. Without it, even organisations that understand the diagnosis will keep responding to capability gaps one project at a time.

This is the governance gap inside the design gap.

What holding the brief actually requires

If you were to design the institutional home for Work design — the role, function, or mandate that would actually hold this — what would it need?

At minimum: authority that spans HR, IT, Operations, and Strategy simultaneously, not sequentially. A seat at the executive table — not a project team, not a centre of excellence reporting into someone who attends that table. A mandate that is continuous, not episodic — the capability question does not get answered once and stay answered, any more than a financial portfolio gets set once and left alone. And, perhaps most uncomfortably, a willingness to redesign the function’s own role as the Work changes.

The direction that emerges from practice is that this sits closer to the Operating Model layer than to HR as currently constituted. The framing that is gaining ground is direct about what it requires: shed compliance and pay administration to Legal and Finance; invest the freed headroom in Work design and transformation capability. Be the architect of Work design, not the administrator of job families. This is not an HR-only argument — it is an Operating Model argument that happens to have large consequences for HR.

Other disciplines have faced analogous transitions. Infrastructure management learned the difference between repairing a road and maintaining a highway system — the former is reactive and episodic; the latter is a continuous discipline with accountable ownership, risk monitoring, and strategic rebalancing. Behind the failure to maintain is almost always a capex/opex trap: repair shows up as capital spend and is visible; ongoing maintenance disappears into operating cost and gets cut first. The result is you fix the road only when it has deteriorated past viability — and you pay for it twice. The portfolio discipline holds both in view simultaneously, which is exactly what organisational capability management currently lacks. Healthcare systems learned the difference between treating individual patients and building diagnostic capacity — the former scales linearly with effort; the latter builds something that compounds. The Work design capability is in that second category. You cannot outsource your way to it one project at a time.

One concrete signal to watch for: when an organisation requires managers to analyse task structure before requesting headcount — as the Shopify internal AI mandate did— it is treating Work design as a governance instrument rather than a departmental concern. Whether or not Shopify sustains this, the signal matters. It tells you that the question of *what kind of work actually needs doing* can be made into a governance question — that the design layer can be given enough institutional authority to reshape how resources flow.

A different signal, different register: at Microsoft, product development teams went through Camp AIR — a three-week programme with protected time, internal coaches, and a shared set of AI tools, with the brief to figure out how to work differently, not to ship the next feature. Old working patterns were deliberately suspended before new ones could take hold. The programme is work design as a governance instrument: time and authority explicitly carved out so that redesign happens before existing practices reassert themselves.

This is a necessary precondition, not a full solution. Naming the governance gap does not dissolve the political economy that created it — the functional silos, the budget cycle orthodoxies, the HR mandate set in a different era. The argument here is that portfolio management is the right framing for the capability; making that framing stick in a real organisation is a different challenge, and one this newsletter will return to.

Where this takes us

The capability has a name: Capability Portfolio Management. The mandate is available. The institutional logic — portfolio management of organisational capability, held at the executive level, with a continuous brief and a cross-functional authority — is not exotic. It exists in adjacent disciplines. What is missing is the will to claim it for Work, and a clear enough picture of what claiming it actually involves in practice.

That is where this newsletter is going next. Not as a theory — as a working account of what the discipline looks like when someone is actually holding it, what it requires from the people who hold it, and what it demands from the organisation that grants it.

Leave a comment

Sergio

2. Site Updates

The Laws of Organisation Design series is running again on the blog. One more piece added:

• The Law of Self-Similarity

More to follow. If you find the productivity argument in this issue interesting, the Laws series is the slower, more structural companion to it.

Also available the recording of a Podcast: People is Not an Option | AI and HR: Why This Is a Cultural Problem, Not a Technical One

3. Reading Suggestions

1. Redesign first. Then AI just works — Jared Spataro, Microsoft.

   The clearest practitioner argument for work redesign as a precondition of AI adoption, not an alternative to it. The Camp AIR programme described here — three weeks of protected time to map and redesign work before old practices reassert themselves — is what treating work design as a governance decision looks like from the inside of one of the world’s largest AI deployments.

2. What Is Strategic Workforce Planning?— Bain & Company.

   The clearest practitioner articulation of workforce planning as a discipline rather than an exercise. Read against the argument in this issue: the framework is correct; the institutional reality it describes is the defanged version of it.

3. World of Work Trends 2026: The Intentional Organisation — Top Employers Institute.

   The “intentional organisation” framing has entered mainstream HR research. Worth reading for what it gets right (design intentionality as performance lever) and what it leaves underspecified (who holds the brief).

4. The New Era of Workforce Planning — SHRM.

   SHRM’s survey of where strategic workforce planning actually sits in organisations today. The diagnosis of why it keeps getting captured by headcount planning is useful confirmation from a different angle.

4. The (un) Intentional Organisation 😁

5. Keeping in Touch

Don’t hesitate to reach out by directly hitting “reply” to this newsletter or using my blog’s contact form.

I welcome any feedback on this newsletter and the content of my articles.

Find me also on:

• LinkedIn

• Instagram

• Medium

• Refind

• GoodReads

• Readwise

1. The Productivity Numbers

Last issue I promised the productivity numbers, and pointed at Anthropic’s labour-market paper as the place where the evidence is starting to consolidate. Two weeks on, I want to do that piece of work properly, because the numbers turn out to tell a more interesting story than the headline summaries suggest — and the story is, again, mostly about design.

I should say this before going further: I’m writing this sitting through the same vendor pitches everyone else is sitting through. Decks full of double-digit Productivity gains, case studies that never quite specify the comparison group, ROI calculations resting on assumptions you cannot interrogate without the speaker getting visibly impatient. I am not against the technology. I’m sceptical of the numbers, and the longer I look at the academic evidence the more I think the scepticism is the responsible reading.

What the macro economists actually say

Start with Daron Acemoglu, who is not a sceptic by temperament so much as by arithmetic. His simple macroeconomics of AI estimates that AI will add no more than around two-thirds of a percent to total factor productivity over a decade — and on the paper’s own more conservative reading, closer to half a percent. A “nontrivial but modest” effect, in his phrase. Multiply the share of tasks that are AI-exposed by the share of those that are economically automatable and by the cost saving per automated task, and the macro number that falls out is close to a rounding error.

You can argue with the parameters — Goldman Sachs and McKinsey both do, with cheerier results — but the structure of the argument is hard to escape. AI does not transform every task in every job. It transforms some tasks in some jobs. The macro impact is a function of how many tasks, how much saving, how widely deployed, and the answer keeps coming back as “less than the deck implied”.

What Anthropic’s own data shows

This is the piece that landed in March and that I keep returning to. Anthropic’s economics team published a study using actual usage data from Claude — not surveys of intent, not vendor projections, but observed work being done — and built a measure they call observed exposure. The trick is the contrast. Theoretical exposure tells you what AI could do. Observed exposure tells you what AI is actually being used to do. The two diverge, and they diverge sharply.

The headline result is that observed exposure is a fraction of theoretical exposure across most occupational categories. In business and finance, in management, in computer and mathematical roles — the categories where vendor decks are most aggressive — theoretical coverage runs above 80% but actual usage is dramatically lower. The capability is there. The work, mostly, is not flowing through it.

What I find striking about this paper is who published it. This is Anthropic — a frontier lab with every commercial incentive to talk up adoption — telling the market that the gap between what their tool can do and what it is being used to do is wide and persistent. It’s not a sceptic’s paper presented as a believer’s. It’s a believer’s paper that happens to confirm what the sceptics have been saying all along.

Brynjolfsson’s J-curve, which is the mechanism

If you want to understand why observed exposure trails theoretical, Erik Brynjolfsson and his collaborators have been giving the same answer for the better part of a decade. General-purpose technologies, they argue, require complementary intangible investments — process redesign, organisational restructuring, workforce retraining, new governance and measurement systems — before the productivity shows up in the data. Productivity dips while these investments are being made (this is the bottom of the J-curve), then rises as the intangible capital starts to compound. We’ve seen the pattern with steam, electricity, computing. Same shape every time.

The honest reading of the current evidence is that we are sitting at the bottom of the J-curve for AI. Not because the technology is failing, but because the complementary investments — the design work — are mostly not being made. Companies are buying tools and hoping the redesign somehow happens on its own. It does not happen on its own. It has never happened on its own for any general-purpose technology in industrial history. Of course, with one major exception: companies that are born under AI — where the J-curve still applies but the binding constraint reorders. The design work, for them, is keeping the system legible to humans while AI does most of it; not redesigning humans’ work around the system.

The free lunch is being repriced

There’s a second piece to the productivity argument that I’ve been turning over in my head, and it is this: until very recently, AI looked like a free lunch from the buyer’s side. Subscription a few dollars per seat, generous limits, frontier capability one click away. That’s changing fast — on both sides of the market.

On the buyer’s side, the most concrete signal is Microsoft. From July 1st, list prices on most Microsoft 365 commercial suites are going up between roughly 5% and over 40% — with the high end driven by Frontline SKUs where the base increase stacks with the removal of legacy discounts — and Microsoft itself frames the increase as reflecting investments in AI features and infrastructure. Microsoft 365 Copilot stays at $30 per user per month on top — and there are now reports of 30 to 40% of Copilot licences sitting unused within the first 90 days of deployment in many enterprises. So organisations are about to pay materially more for a base productivity suite, partly to fund AI features many of their people aren’t using yet, while separately paying $30 per seat for an add-on whose own usage rate is, charitably, patchy. This is not how a free lunch is supposed to feel.

On the supplier’s side, the picture is even more interesting. OpenAI’s inference costs alone are projected to rise from around $8.4B in 2025 to $14.1B in 2026, and the company is on track to burn double-digit billions of dollars before reaching anything that resembles operational profitability. Anthropic’s path looks healthier on the unit economics, but only relatively. The market consensus, in the parts of the market that look at the actual financials, is that current API and subscription pricing is being subsidised below cost — funded by venture capital and hyperscaler cross-subsidy — and that prices will normalise upward over the next 12 to 24 months as that capital discipline tightens.

Both sides of the market point in the same direction. The thing companies have been buying as if it were cheap is becoming visibly not-cheap. And here’s the part that matters for the productivity argument: the cost is now showing up on the balance sheet before the productivity is showing up in the output. Decks talk about ROI; the actual experience inside companies, increasingly, is paying the I and waiting for the R.

What this argument doesn’t claim is that AI capability is overhyped, or that companies should slow adoption, or that the J-curve framework holds in every case. The bullish forecasts have respectable provenance: Goldman Sachs has estimated a 7% lift to global GDP over a decade, McKinsey puts the productivity opportunity at $4.4 trillion annually. There is also firm-level evidence pointing the other way to my main argument: Brynjolfsson and his collaborators have published a 14% productivity gain in customer-service operations under generative AI — a result that, if it generalises, would push Acemoglu’s macro estimate up materially. And the capability frontier is moving. The “20% of tasks exposed” parameter in Acemoglu’s calculation is a snapshot, not a constant; if the next twenty-four months produce another step change in what models can do reliably, that parameter widens and the macro number widens with it.

The honest position is therefore narrower than the headline suggests. The sceptical reading is the more defensible reading of the evidence we have. It is not the only reading consistent with the evidence we don’t yet have.

The category error that runs through the deck

There’s a confusion in the productivity discourse that I think is doing a lot of damage, and it’s one I tried to name a few years ago: the conflation of Productivity and Performance.

Productivity is a property of the system — of the Operating Model, of the way input is converted to output at the organisational level. Performance is a property of individuals and teams — how effectively they accomplish what they set out to accomplish, given the system around them.

Most of what vendor decks call “productivity gains” are, at best, individual Performance gains: a developer writing a unit test faster, an analyst getting through a first-draft memo more quickly, a customer-service agent dispatching a ticket with less typing. These gains are real. They’re also strictly local. They become organisational Productivity only if the system around them is redesigned to absorb the new constraint and reroute the freed capacity into something the organisation actually values. Without that redesign, the gain dissipates into more email, more revision cycles, more meetings about what the AI produced….

This is the move I want to keep making in this newsletter. The design layer is where Performance gains turn into Productivity gains, or fail to. The macro evidence, the firm-level evidence, the J-curve theory, and now the cost trajectory all point at the same place. The technology is not the binding constraint. The binding constraint is that nobody owns Work design as a discipline, with the seniority and the cross-functional brief required to actually redesign work around the technology — and pay back the bill that’s now arriving for the technology.

Where this is going

Next time I want to start moving from diagnosis toward capability. If “owning Work design” is the missing piece, what would it actually look like inside a real organisation? Who holds it, what authority does it need, where does it sit relative to HR, IT, Operations, Strategy? I have a working answer that I’ve been testing in practice — and I want to lay it out before the conversation drifts back into yet another argument about which copilot to deploy.

Leave a comment

Sergio

2. Site Updates

The Laws of Organisation Design series is running again on the blog. Two additional pieces so far:

1. The Law of Requisite Variety

2. The Law of Alignment

More to follow. If you find the productivity argument in this issue interesting, the Laws series is the slower, more structural companion to it.

3. Reading Suggestions

1. Labor market impacts of AI: A new measure and early evidence — Anthropic Economics Team.
   The paper this issue circles around. The methodology section is worth reading even if you skim the rest — observed-exposure-versus-theoretical-exposure is a useful frame for any conversation about AI adoption you find yourself in this year.

2. The Simple Macroeconomics of AI — Daron Acemoglu.
   The disciplined macro argument for modest gains. Long, technical, and worth the effort. The clearest demonstration I have read of why “AI will transform the economy” and “AI will produce around half a percent of TFP over a decade” can both be true at once.

3. The Productivity J-Curve: How Intangibles Complement General Purpose Technologies — Brynjolfsson, Rock, Syverson.
   The mechanism behind the modesty. If you only read one of the three, this is the one with the most leverage for organisational practice.

4. AI was supposed to save us time. It may be making work heavier instead. — Daniel Pink.

   A short LinkedIn post, but Pink names the operational consequence that the academic papers don’t quite reach: without redesigning the work system around AI, adoption doesn’t reduce demand — it intensifies it. Brynjolfsson explains why the redesign is necessary; Pink describes what happens inside organisations where it isn’t happening. The two read well together.

4. The (un) Intentional Organisation 😁

5. Keeping in Touch

Don’t hesitate to reach out by directly hitting “reply” to this newsletter or using my blog’s contact form.

I welcome any feedback on this newsletter and the content of my articles.

Find me also on:

• LinkedIn

• Instagram

• Medium

• Refind

• GoodReads

• Readwise

1. The Design Question Nobody is Asking

Long time no see. No excuses, my work simply absorbed too much time. Returning today with this issue of The Intentional Organisation newsletter feels right. May 1st, Labour Day: the right moment to reflect on Work holistically, not just as a list of tasks. I’ve been thinking a lot on this recently, being immersed daily in a flow of reflections on productivity, impacts of market dynamics on our organisation, and trying to understand the real impact of AI transformation behind the hype.

That’s why I would like to re-start this newsletter with Work itself, and the conversation I keep finding we are not having about it.

The dominant discussion about AI and Work

I’m sure you have also had the impression that the discussion about AI and Work has been dominated by two fields of analysis:

1. The productivity camp: with tools promising extraordinary efficiency gains, vendor decks full of percentages, companies advertising cuts of jobs “thanks” to AI…. although data seems to indicate these gains are in reality yet to be materialized, and most of it seems rather being a investor-pleasing AI-washing maneuver.

2. The labour-economics camp: looking at macroeconomic level what jobs survive, which ones will disappear, what speed replacement actually happens.

Both conversations are serious. Both are producing useful evidences. And both, taken together, are completely skipping the core question that actually determines outcomes inside organisations.

The Two-Speed Problem

Look at what’s actually happening inside companies right now and you see a split.

1. Speed 1 is individual productivity gain from AI tools. Most knowledge workers have access to a copilot of some sort, and use it daily. This is moving.

2. Speed 2 is workflow and operating model redesign enabled by AI. This is mostly not moving.

The data here is striking. 88% of organisations report deploying AI, yet 86% say they are not properly prepared to embed it into daily operations. The same report frames the broader shift as a move from Structure to Flow — a recognition that the traditional productivity plays (restructuring, delayering, cost cuts) are hitting diminishing returns, and that what comes next is redesigning how work moves across the enterprise.

That second piece — how work moves — is exactly what the Operating Model is about. It’s the layer where systems, processes, people, and technology are supposed to cohere. AI is precisely the kind of intervention that stress-tests this layer. And the stress test is currently being failed.

Why Work Design is the missing capability

Ask most organisations a deceptively simple question — who designs work here? — and you’ll usually get a long pause and a list of partial owners. HR owns roles. IT owns systems. Operations owns processes. Strategy owns outcomes. Nobody owns the design.

This was a tolerable arrangement when Work changed slowly. It’s becoming an expensive one now. Three things, briefly:

1. AI lands in the wrong layer. Deploying AI into a process designed for non-AI conditions produces the result you’d expect: the technology works, the productivity does not show up, and the organisation concludes the tool was over-promised. The tool is probably fine. The work around it was designed for constraints the tool removes, and nobody has redesigned the work. The 170-year-old org chart is being asked to absorb a technology that operates at the task level, not the job level.

2. The capability has no home. Redesigning work requires a working understanding of HR, IT, Operations, and Strategy at once, and the senior leaders who could in theory hold it have usually been functionally specialised away from it for two decades. Work design is everybody’s problem and therefore nobody’s job.

3. Decision authority has already migrated. This is the one most organisations have not yet noticed. Whoever controls AI system parameters, training data, and thresholds is shaping decisions that business leaders are nominally accountable for — usually IT, a small data science team, or a vendor. The leaders who own those decisions on paper often don’t know what the systems are optimising for.

This is the design gap. It’s not a technology gap — the technology is mostly available, mostly working, mostly affordable. It’s not a strategy gap either — most boards understand, at the slogan level, that AI matters. The gap is in the capability to redesign Work itself: to take a process and rebuild it around a different set of constraints, with a clear view of how the Operating Model and the Organisation Model need to move together for the redesign to hold. Without that capability, AI adoption defaults to what we are seeing now: structure remaining, flow deteriorating.

Where this newsletter is going

I won’t pretend I have the full answer. I do think the question deserves to be named clearly, and I think the next decade of organisational performance will turn on whether we get serious about it. So the next twelve issues of this newsletter are going to circle this terrain — what Work design actually is, why it has no home, what the productivity evidence really says about AI, and what a Listening Organisation might look like once you take the design layer seriously.

Next time, the productivity numbers. The evidence is more sceptical than the headlines suggest, and the gap between capability and outcome turns out to be a design problem, not a technology problem. I’ll show you why.

Leave a comment

Sergio

2. Site Updates

A note on the site itself: sergiocaredda.eu hasn’t been updated in some time, and I’m aware of it. The blog rework is one of the things on my list for the next few months, and the Organisation Evolution Framework page itself will be revised as part of that work.

One thing that has stayed alive is the Leadership Models Collection, which I’ve continued to expand quietly through this period. Most recently I added entries on Multipliers, Humble Leadership, Host Leadership, Complexity Leadership Theory, and Resonant Leadership. It’s becoming a useful reference resource on its own, even ahead of the broader site refresh.

3. Reading Suggestions

1. Why AI Isn’t Fixing Work — And Why It Can’t Until We Rethink How Work Is Designed — Donna Scarola. The single best piece I’ve read this year on the design-gap argument. Opens with the 1854 origin of the modern org chart and walks through why a structure designed for industrial coordination cannot absorb a technology that operates at the task level.

2. Working Theory: AI Work Redesign — NOBL. A long, evidence-led notebook arguing that AI deployment is a work redesign problem, not a technology problem. Published in the open and updated quarterly — a model worth more imitation than it gets.

3. The State of Organizations 2026: From Structure to Flow — McKinsey. Mainstream consultancy validation that the design layer is broken. The diagnosis is sharper than the prescription, but that’s exactly why it’s worth reading.

4. The (un) Intentional Organisation 😁

5. Keeping in Touch

Don’t hesitate to reach out by directly hitting “reply” to this newsletter or using my blog’s contact form.

I welcome any feedback on this newsletter and the content of my articles.

Find me also on:

• LinkedIn

• Instagram

• Medium

• Refind

• GoodReads

• Readwise

In today’s fast-paced business environment, productivity is often seen as a pure numbers game—doing more in less time. However, within the context of an intentional organisation, productivity takes on a different meaning. It’s about deliberate alignment, where productivity is a measure of achieving meaningful, strategic outcomes aligned with the organisation’s purpose rather than maximizing output for its own sake. This redefined perspective helps create sustainable value for the organisation, its employees, and its customers.

Redefining Productivity: Purpose Over Output

In The Intentional Organisation, productivity is reframed to serve a larger purpose. This approach means looking beyond traditional measures like hours worked or tasks completed and focusing on what truly matters to the organization’s goals. Rather than an end in itself, productivity becomes a byproduct of intentional work that aligns with the organization's Value Creation patterns. When teams are clear on what’s essential, they naturally focus their energy on high-value activities, enhancing individual and organizational productivity.

Read again what I already wrote on Performance and Productivity

👉 Actionale Insight. Ensure that your productivity KPIs are not “inward” looking but are directly linked to the value creation you want to create for your customers. For example, if you have a Customer Service Call Center, focusing on call-handling time or first-call resolution metrics might lower your customers’ satisfaction level because the KPI may trigger inconsistent behaviours by employees. Ensure KPIs are balanced with customer satisfaction metrics, as this is the only way to ensure consistency and show that productivity is a tool to improve customer satisfaction.

Enabling Productivity Through Clarity and Empowerment

The foundation of intentional productivity is clarity—each team member understands their role and how their work impacts broader objectives. Clarity fosters empowerment, as employees who understand the purpose behind their tasks are more likely to take ownership and innovate.

👉 Actionale Insight. Make it a habit to communicate the “why” behind key initiatives and empower teams to make decisions that further the organization’s objectives. This not only improves productivity but also enhances engagement and motivation.

Streamlining for Intentional Productivity

The Intentional Organisation values simplicity. When workflows are needlessly complex or tools overwhelming, productivity suffers as irrelevant tasks, or redundancies bog down employees. A fundamental principle is to streamline processes and remove obstacles, allowing employees to focus on what truly adds value. Employees are empowered to remove obstacles either directly or through suggestions. A “zero waste” approach to productivity is declared and enacted in people’s behaviours.

👉 Actionale Insight. Conduct regular audits of processes and tools, eliminating those that don’t directly support value creation. This can involve leveraging technology to automate routine tasks, ensuring that time is reserved for work that requires human creativity and problem-solving, or investment in training and upskilling to ensure that automated tasks don’t cripple back into organisational routines.

The Role of Culture in Intentional Productivity

Culture is instrumental in fostering an environment where intentional productivity can thrive. In an intentional culture, employees feel psychologically safe to make mistakes, experiment, and grow, leading to better outcomes. Productivity isn’t about immediate gains but long-term value creation that’s consistent with the organization’s purpose.

👉 Actionale Insight. Promote a culture of continuous improvement rather than focusing solely on short-term results. Encourage open feedback, collaboration, and a willingness to learn from mistakes. A supportive culture will naturally drive productivity as employees are motivated to contribute meaningfully.

Measuring Intentional Productivity

Finally, measuring productivity within The Intentional Organisation goes beyond tracking output speed or efficiency. Instead, measurement frameworks are designed around value-added outcomes, customer satisfaction, and alignment with strategic goals. This might mean looking at qualitative feedback, team cohesion, or long-term impact rather than just immediate results.

👉 Actionale Insight. Develop KPIs that reflect the organization’s core intentions. For instance, instead of tracking time spent on tasks, measure the outcomes of completed projects, the quality of customer interactions, or the development of innovative solutions that contribute to the organizational purpose.

Conclusion: Intentional Productivity for Sustainable Impact

By reframing productivity within The Intentional Organisation, you can shift from simply doing more to doing what matters. This approach helps organizations build sustainable productivity that achieves short-term results and creates long-lasting value. Intentional productivity is a cornerstone of a more meaningful, engaged, and high-performing workplace.

What do you think?

Leave a comment

— Sergio

2. Site Updates

The Laws of Organization Design

I have continued publishing my articles on The Laws of Organization Design. Here is the latest articles published:

The Laws of Organisation Design

1. Metcalfe’s Law

2. The Law of Constraints

3. The Pareto Principle

More to come in the coming weeks!

— Sergio

3. The (un) Intentional Organisation 😁

Source: PHD Comics X

4. Keeping in Touch

Don’t hesitate to reach out by directly hitting “reply” to this newsletter or using my blog’s contact form. 

I welcome any feedback on this newsletter and the content of my articles. 

Find me also on:

• X (formerly Twitter)

• LinkedIn

• Instagram

• Medium

• Refind

• GoodReads

• Readwise

Last week, I started to address the question of the impact of not applying Intentional Design to the different elements of Organisation Design identified in the Organisation Evolution Framework. I covered the first four components (Business Model, Strategy, Operating Model, and Organisation Model), all through a lens of Consistency and Congruence, which I also recently wrote about.

In this issue, I will cover the remaining four elements of the OEF, the “soft” ones, and try to summarise the impacts. As with the first part, I wanted to reinforce two reminders: all the cases below are purely theoretical, as it is difficult to identify a sole element not designed with intention. Moreover, Congruence is a dynamic attribute that changes and evolves.

So, let’s see the four last elements in more detail.

1. Undefined Leadership: The Dissonant Organisation

What happens when we don’t intentionally design Leadership for our organisation?

Leadership is the attribute that defines intentionality in an organisation's design. Not explaining what Leadership means for our organisation means creating a vacuum that affects the entire system. I define this case as dissonance: the rest of the organisation's elements will probably develop through emergent chaotic processes without harmony and agreement.

Undefined leadership will affect how the organisation addresses change; people will all feel different levels of agency, and concepts like resilience, adaptation, and innovation will fail to develop as shared attributes.

But is it possible to not have a definition of Leadership? The answer is yes. Leadership is often an emergent property within an organisation, and a lot of the discussions around founder’s mindset or founder’s mentality underline exactly this: organisations often develop Leadership as an inheritance of the leadership style and attribute of its founder(s). Without an intentional analysis if the features of that style are still congruent with the rest of the elements of the organisation.

👉 Consequences: The absence of an intentionally designed leadership causes the formation of an emergent leadership that can sometime determine a level of inconsistency or incongruence:

• Unclear governance: Although formal governance models are part of the organisation model, the way organisations take decisions is an attribute of leadership. A typical case that happens in organisations during growth phases is the formation of consensualism, where leaders of an organisation start confusing involvement and engagement.

• Issues in Innovation and Change: one of the main territories where congruence of the Leadership model of an organisation is put to test, is its ability to steer change and innovation. An emerging leadership style that is not connected to the strategy of the organisation, may often create resistance patterns that create frictions in processing the needs for change or transformation.

• Missing Intentionality: the biggest impact, though, can very well be the impossibility to drive the intentional design required by the other organisation elements. Whatever type of leadership we’ll have, this will influence directly all the other elements. It will make it impossible to intentionally design the rest of the organisation, as Leadership is the sole source of the intentionality we require.

2. Undefined Culture: The Deprived Organisation

What happens when we don’t intentionally design Culture for our organisation?

Culture is what effectively defines consistency across multiple elements of our organisation design fabric. When culture is inconsistent in its manifestations (both visible and invisible), we enter an arena where negative energy starts flowing in the organisation. This is why I call this Deprived Organisation, to underline the “missing energy” levels that drain the organisation potentially affecting its entire survival.

How often did we read about a company going bust, with dozens of people assessing this was the result of a toxic culture? These are prime examples ona radical incongruence of the culture internally to the organisation. Point is that these incongruences can be tracked and made visible well before an organisations fails completely.

👉 Consequences: the absence of an intentionally designed culture can provoke several consequences, such as:

• Toxic Workplace Culture: Simply stated, inconsistencies at the culture level may immediately be visible through toxic practices that affect workplace effectiveness. Gossiping, lack of trust, poor engagement levels, high absenteism, are all examples of signals that the culture is not working and definitely not supporting the organisational effectiveness.

• Unethical Behaviours: The biggest problem that can arise is when internal culture looses also the reference compass with the necessary ethical boundaries of social behaviours. Multiple examples come to mind when thinking of large organisation’s failures linked to an emergent culture that allowed illicit behaviours to propagate.

• Poor Results: Incongruence in culture is immediately visible in the results of the organisation, sometimes even more than other elements of organisation design, especially if couple with poor leadership (which often is the case).

3. Undefined Purpose: The Drifting Organisation

What happens when we don’t intentionally design the Purpose for our organisation?

The concept of Purpose has gained traction in recent years, particularly as organisations started facing concerns about their global impact on environment and society as a whole. The idea of a “direction”, a “raison-d’etre” of an organisation is however rooted in the past. All the first endeavours, from guilds to commercial societies, had defined some kind of ambition, often in terms that would not be accepted today, but that where relevant for their historic period.

For a long period of time, however, the world of enterprises has been hit by the distorted idea that companies had a for-profit purpose. Which is also the main reason why a lot of companies still today do not have a formalized “purpose” (something that, instead, is more common in non-profit organisations, that need a specific definition of their core ideas to attract members, volunteers or donors).

This is why purpose is, probably, the most common element that is not intentionally designed. By not acting on this, often companies see their organisational focus “drift away” towards the default idea that profit is the main objective of organisational activities. This is why I name this The Drifting Organisation.

👉 Consequences: the absence of a formal purpose will create issues in the form of:

• Short-termism: Excessive focus on profit (often measured as EPS) often leads to short-termism negavitvely affecting organisational sustainability (I recently wrote about this on the De Geus’s Law article).

• Goal Divergence: Organisations might start acting in divergent ways in terms of short-term goals, business model set-ups, strategic directions. Without the unifying effect of a purpose, employees and collaborators might also start becoming “confused” in terms of their actions.

• Misalignment with Employees: A company purpose also serves as a compass for employees to align their motivations. When defaulting to “profit”, engagement levels will lower, and activities will be highly dependent on rewards policies and incentives. People will often leave for higher salaries…

4. Undefined Ecosystem: The Detached Organisation

What happens when we don’t intentionally design the Ecosystem for our organisation?

It may sound weird to be able to “design” an ecosystem for our organisation. As a matter of fact, applying Intentional Design to an ecosystem means essentially recognising the role of our organisation into the web of relationships that exist around us. If the Business Model defines the relationship with Customers, it is at the ecosystem level that we define relationships with other key stakeholders.

Emergent design elements for Ecosystems are evident in many organisations. From compliance roles, to Public Affairs organisations, to lobbying participations and membership of industry groups, to occasional alliances, all these elements show that companies are aware of the need to build connections. But, is this done intentionally?

Too often organisations put themselves in a condition of “compliance”, they see themselves on the receiving end of environmental inputs. But very seldom they start a “design” of their value interactions.

Yes there are topics that are on the agenda of every organisation today (such as ESGs and Corporate Sustainability), that would require more intentional focus on this.

This is why I named this the “detached” organisation, because we assume a passive role in the definition of value added relationship.

Think just at the world of Investor Relations. Technically, shareholders should be one of the main asset for an organisation. Yet, most IR portals simply show what is needed from a compliance point of view. There is a tendency for managers to “mistrust” shareholders, instead of considering them allies in the company development. This disconnect is even more evident for those companies that have shareholder programmes for their employees. Very few have intentionally designed something for these people, who are not only working but also investing in their company.

👉 Consequences: the absence of an intentional ecosystem will create issues in the form of:

• Purely Transactional Relationships: the organisation reduces its interactions with other stakeholders to mere transactions, often dictated by compliance. This is especially impactful in moments of difficulty, where the organisation might give up an important source of resilience..

• Missed Networking Value: Networks within an Ecosystem are an important source of value generation. Without a clear design of its relationship, the organisation might miss all the related value creation opportunities.

• Higher Transactional Costs: Organisations will have higher overall costs associated to interactions with the external environment. For example, not developing links with schools and universities might results in higher recruiting costs.

Conclusion: The role of intentionality

Read again the last issue of the newsletter.

All of the above (plus what we have seen in the last issue of the newsletter) shows once more the need to think of organisation design in terms of intentionality applied to the different organisation elements.

As things evolve for an organisation, it also shows how important it is to develop an “intentional maintenance” practice capable of verifying and updating, over time, the level of congruence of the system.

Congruence does not require for all elements to be designed. Some can emerge over time. For example, crafting Leadership will create the guardrails for a congruent culture to emerge. Developing an intentional Purpose, will allow for Strategy and Business Model to be “synched”. Designing the key relationships in the Ecosystem will allow the emergence of a meaningful value chain that spans internal and external stakeholders.

There are four further conclusions to this analysis though:

1. Organisation Design Elements need to be seen as a constantly interacting system. Focusing on one alone without considering the others (both in terms of design, but also simply in terms of analysis and awareness) means incurring in a high risk of failure.

2. Benchmarking and “copying” solutions that work elsewhere is the best way to disrupt a system, rather than help fixing it.

3. Intentionality means capturing the interconnectedness of the various components. This ultimately means that we don’t have to design each element to the maximum level of effectiveness but to the level that is “good enough” to ensure a proper level of congruence.

4. Organisation Design Elements need to be seen as a constantly interacting system. Although I presented them here one by one, issues on one of these expands as a ripple effect to all the others. I recently discussed the Law of Constraints in an article on my blog, and have proposed a formulation adapted specifically to the Organisation Evolution Framework.

What do you think?

Leave a comment

— Sergio

2. Site Updates

The Laws of Organization Design

I have continued publishing my articles on The Laws of Organization Design. Here is the latest articles published:

The Laws of Organisation Design

1. Metcalfe’s Law

2. The Law of Constraints

3. The Pareto Principle

More to come in the coming weeks!

— Sergio

3. The (un) Intentional Organisation 😁

Source: Pinterest

4. Keeping in Touch

Don’t hesitate to reach out by directly hitting “reply” to this newsletter or using my blog’s contact form. 

I welcome any feedback on this newsletter and the content of my articles. 

Find me also on:

• X (formerly Twitter)

• LinkedIn

• Instagram

• Medium

• Refind

• GoodReads

• Readwise

In the last issue of this Newsletter, I wrote about Consistency and Congruence, which are also associated with the dualism of Intentionality and Emergence in Organisational Design components.

I received a question on the impacts of not intentionally designing one (or more) of the components listed in the Organisation Evolution Framework. I have been thinking about it for a few days, and today, I will share my initial views on what happens in reality.

“un-intentional” design: what happens?

The above chart shows a hypothesis based on a particular situation that one of the elements is not designed intentionally (thus, it is missing the “congruence” link with the rest of the components). In this Newsletter, I will analyse the first four components of the Organisation Evolution Framework (Business Model, Strategy, Operating Model, and Organisation Model), leaving the other four to the next issue.

First, two general warnings:

1. All these cases are purely theoretical: it is difficult to see a situation where only one component is not designed “intentionally”. When this happens, the component will develop as an emergent attribute of the organisation (remember, all these components are always present).

2. Congruence is a dynamic process: organisation components that are designed intentionally tend to age in line with the mutation of the context. So it can happen that an element that was designed intentionally is not anymore “fit” to the situation.

So let’s see four specific cases.

1. Undefined Business Model: The Me-Too Organisation

What happens when we don’t intentionally design a Business Model for our organisation?

This means there is no formal definition of our value proposition and no conscious identification (at minimum) of our customers or consumers.

The organisation accepts a “me-too” approach, copying similar organisations already on the market or delegating the definition of its Value Proposition to other elements of its design.

👉 Consequences: This causes the formation of an Emergent Business Model where some of the critical attributes might not necessarily be under the organisation's control. A few cases can appear:

• Unclear route to market: A start-up company could focus so much on their innovation strategy (focusing on R&D), only to realize that the products created do not have a straightforward market application, as the organisation failed to identify potential customers.

• Issues in identifying market constraints: A group of employees from an existing company could create a “spin-off”, focusing their initial attention on operating and organisational model improvements compared to their previous employer, assuming they would compete in the same market and customer base. However, they might later realize that the former employer has created a barrier for them to access its customers, thus creating issues in their route to market.

• Missing proposition for their customers: A non-profit organisation could be created with a strong focus on a prominent and ambitious purpose. Still, it could fail to identify potential donors, thus vanishing their initial efforts.

2. Undefined Strategy: The Directionless Organisation

What happens when we don’t intentionally design a Strategy for our organisation?

Many would argue that finding a company without a strategy is almost impossible. The reality is that many organisations don’t have a real strategy but rather a liturgy of processes that project their financial results in the future. The biggest mistake many make is assuming that a budget is a strategy. Wrong. There is a lot more than cost and revenue definitions into strategy definitions. Thus, the possibility that the Strategy is not designed intentionally is pretty high.

This means there is no formal definition of our Strategic Choices, which means not having clear priorities in the organisation.-

The organisation accepts a “Directionless Status,” whereby direction (or criteria) are not formalised; thus, choices are made tactically. There might be moments when being tactical can be a strategic choice, and there might also be moments when an opportunistic approach might be the best way to capture opportunities.

After all, Henry Mintzberg directly theorised the formation of an Emergent Strategy in his Strategy Safari as one of the possible (and valuable) alternatives to an intentional plan.

👉 Consequences: the absence of a formal intentional strategy can create issues sometimes, for example, when:

• Inconsistency in product portfolio: A company that is very active in M&A could develop a purely opportunistic approach, buying what is available on the market and ending up with a portfolio of inconsistent products or services.

• Incapacity to define value: A start-up that is very active in R&D could start developing a product, then continue to move in a direction in terms of prioritisation of components and results, ending in a vicious circle that doesn’t allow the company to capture value.

• Missing alignment of goals and activities: An organisation might fail to update its strategy after a disruption in the market, causing employees and managers not to understand new prioritization within the organisation. This can create hectic behaviours and erratic results.

3. Undefined Operating Model: The Random Organisation

What happens when we don’t intentionally design an Operating Model for our organisation?

In my experience, this is the most frequent case. Too many organisations focus on drawing org charts and internal policies but fail to stand before their drawing board and actively build their operating model. To me, this is akin to planning a house by focusing solely on the interior design without a clear focus on designing its structure, including the wiring and plumbing.

This means there is not a shared understanding of the Value Delivery Chain of the organisation.

There’s a shared assumption that by simply adopting a traditional organisational model (let’s say a functional hierarchy) and having a definition of our Business Model and Strategy, the rest will follow. In my experience, this assumption can work only in simple markets with no differentiating skills and limited innovation. In how many cases does this happen?

The problem with not intentionally designing your operating model is that many choices will create inconsistencies within the organisation without you expecting this.

👉 Consequences: the absence of a formal operating model will create issues in the form of:

• Bloated Systems and Technology: A company implementing a new ERP tool will often approach the implementation purely from a technology perspective, failing to tune processes to its internal requirements and finding itself with bloated systems that are not always fit for purpose.

• Unfit Structures: A new company that starts operations without a clear Operating Model in mind could find itself with way too big back-office operations compared to its front office, resulting in productivity issues that could lead to bankruptcy.

• Siloed Mentality: A company that implements a functional organisation without clarifying the other components of its operating model will quickly develop a siloed mentality and will have challenges connecting value-creating processes.

4. Undefined Organisation Model: The Chaotic Organisation

What happens when we don’t intentionally design an Organisation Model for our organisation?

This might seem remote, but many organisations fail in the initial steps of their development journey because they don’t plan their organisation model coherently. I see two prominent cases here: 1) companies that assume that a hierarchical organisation is a way to go without giving a thought to the actual needs of their business and 2) organisations that decide to explore alternative organisation models but don’t spend enough time in assessing if it fits their needs.

The first case is way too typical, often intercepting a missed focus on the operating model. We assume we need a functional/hierarchical organisation and let it grow based on the requests of individual managers. This can quickly produce an unsustainable outlook.

Unfortunately, the second case is also becoming more frequent and is one of the main reasons some self-management experiments fail. The organisation does not spend enough time assessing the best model for them, the rules of the game and the ways of working.

This means there is no shared understanding of the definition of work for the organisation.

👉 Consequences: the absence of an intentional organisation model will create issues in the form of:

• Unsustainable organisation sizing: where structures are created more to balance internal power structures than real business needs.

• Unclear governance: the organisation will have difficulties prioritising activities and goals, allocating resources, and ultimately choosing its own path to success.

• Low engagement levels: The organisation will fight to define a value proposition for its employees and collaborators regarding how they contribute to its success.

An Initial Conclusion: The role of intentionality

We have seen above a few examples of how not applying intentionality to the design of each of the organisation's design elements can create problems in both consistency and congruence of the system. In the next issue of this newsletter, we will cover the other four elements of the Organisation Evolution Framework (Leadership, Culture, Purpose and Ecosystem). Still, it is already possible to establish a few conclusions:

1. Yes, we can allow some elements of design to emerge in time. Still, it is critical to ensure we monitor each component's impact on our system's overall sustainability. We need to be able to assess the unintentional consequences of missing decisions.

2. Intentionality means capturing the interconnectedness of the various components. This ultimately means that we don’t have to design each element to the maximum level of detail but to the level that is “good enough” to ensure the required level of congruence.

3. Copying from others (also in the sophisticated form of benchmarking) is often the best way to create problems. Why? Because most of the components are not neutral to the situation in which we cascade them. This is why even the simple implementation of a system with a standardised process defined elsewhere can cause issues if it is not framed in an environment of intentionally defined boundaries, rules of engagement, and so on.

What do you think?

Leave a comment

— Sergio

2. Site Updates

The Laws of Organization Design

I have continued publishing my articles on The Laws of Organization Design. Here is the complete list of articles published:

The Laws of Organisation Design

1. Conway’s Law and Intentional Design (reviewed article)

2. Parkinson Law

3. Law of Triviality

4. Goodhart’s Law

5. Brooks’s Law

6. Hackman’s Law

7. Larman’s Law of Organisation Behaviour

8. De Geus’s Law

More to come in the coming weeks!

— Sergio

3. The (un) Intentional Organisation 😁

Source: Pinterest

4. Keeping in Touch

Don’t hesitate to reach out by directly hitting “reply” to this newsletter or using my blog’s contact form. 

I welcome any feedback on this newsletter and the content of my articles. 

Find me also on:

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• LinkedIn

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• Medium

• Refind

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• Readwise

In the evolving landscape of organization design, that I tried to capture in the Organisation Evolution Framework, two concepts—consistency and congruence—serve as foundational pillars. While often used interchangeably, these terms carry distinct implications for how organizations are structured and how they function. Understanding the nuances between consistency and congruence, particularly in the context of intentionality and emergence, can offer valuable insights on how we can best evaluate an organization’s design and its effectiveness and also guide us in assessing any issues that can result.

Consistency vs. Congruence: A Delicate Balance

Consistency in organization design refers to the uniform application of rules, practices, and processes across an organization. It’s about ensuring that every part of the organization operates according to the same principles, creating a predictable and stable environment. Consistency is often driven by a need for control, risk management, and efficiency.

Congruence, on the other hand, goes beyond mere uniformity. It’s about alignment—ensuring that the various elements of the organization, such as its business model, strategy, operating model, culture, and so on, are all aligned and work harmoniously together. Congruence is more dynamic and context-dependent, allowing for flexibility and adaptation. It focuses on the degree to which the different components of the organization reinforce one another, leading to greater overall effectiveness.

While consistency is crucial for maintaining stability, congruence is essential for adaptability and resilience. In an ever-changing environment, organizations that are merely consistent may struggle to respond to new challenges. Conversely, organizations that are congruent can adapt more easily because their elements are aligned in a way that supports change.

Relationship with Intentionality and Emergence

The two concepts are very much linked to two elements we have spoken about in the past in their application to organization design: intentionality and emergence.

At first glance, we could assume that consistency as a property could originate from emergence and would not necessarily require intentional design. In contrast, congruence would require a broader leadership engagement.

The reality is that we can have different scenarios based on the combination of these dualisms.

• Emergent Consistency—this is the case whereby some of the components of the organization’s design simply emerge by consistency with some of the other elements. If one of my declared strategic choices is to prioritize customer satisfaction, the expectation would be to observe a “customer-first” approach also at the level of the organization's culture. There are many aspects of an organization's fabric that are highly influenced by these two forces, especially in the realm of consistency between reward mechanisms and people's behaviours.

• Emergent Congruence — this is the idea that certain aspects of an organization’s design evolve organically, often as a response to changing environments. This allows different parts of the organization to evolve in contextually appropriate ways that align with the organization’s broader goals, even if they are not strictly consistent with the original design. Many of the self-management models aim at producing this congruence level, as they assume teams would constantly re-align also in the presence of external factors. This calls, however, for a level of diffused leadership that can enable this congruence factor to emerge.

• Intentional Consistency— is the most common intentional action in organisational design. It simply involves adherence to design standards and uniform application of rules. In many ways, this is what many OD practitioners have been taught in organizational work. Which often drives a quantity of documentation, processes, and job descriptions, abuse of benchmarking and of external consultants. It is very visible and observable, but experience shows that this approach has its limits, mainly as organizational complexity does not allow us to manage and design everything.

• Intentional Emergence — this is instead the combination that Intentional Leadership should be aiming for. Design the critical elements, and embrace the bottom-up development of structures and practices that ensure alignment of the organization to its Purpose and ultimate goals.

In practice, organizations rarely operate entirely on one of the combinations above. Instead, they all navigate among the dualism of these factors. Finding a balance is therefore a precious focus.

Building Intentional Leadership

Leadership plays a pivotal role in managing the balance between consistency and congruence, especially in the context of intentionality and emergence. Effective leaders recognize that while it is important to have a clear vision and a consistent approach to strategy and operations, it is equally crucial to remain open to emergent patterns and changes within the organization.

Leaders as Designers: Leaders who view themselves as designers understand that their role is not just to impose a structure but to create conditions where the organization can evolve in congruent ways. This involves setting broad guidelines and principles that ensure alignment with the organization’s goals, while also allowing flexibility for different parts of the organization to adapt as needed. This approach fosters an environment where emergence is not seen as a threat to consistency but as a means to achieve greater congruence.

Adaptive Leadership: Adaptive leaders focus on creating a culture that values learning, experimentation, and feedback. They understand that congruence often requires allowing for deviations from the norm when those deviations are contextually appropriate. This means that while they may start with an intentional design, they remain attuned to the emergent needs of the organization and are willing to adjust their approach accordingly.

Strategic Integration: Leaders must also consider how to integrate consistency and congruence at a strategic level. This involves recognizing when to enforce uniformity and when to allow for divergence. For instance, in highly regulated environments, consistency might take precedence to ensure compliance. However, in fast-moving industries, congruence might be more important as different teams adapt to new challenges.

Conclusion: Achieving Harmony in Organization Design

The interplay between consistency and congruence, and between intentionality and emergence, is at the heart of effective organization design. While consistency provides the foundation for stability and efficiency, congruence allows for the adaptability and resilience needed in a complex and ever-changing environment. Leadership is the key to balancing these forces, guiding the organization through a landscape where both intentional design and emergent development have their place.

Ultimately, the most successful organizations are those that can harmonize these elements—leveraging the strengths of both consistency and congruence, and navigating the dualism of intentionality and emergence with agility and insight. This balance not only enables organizations to achieve their strategic goals but also to thrive in the face of uncertainty.

Leave a comment

— Sergio

2. Site Updates

Updated Version of the Organisation Evolution Framework

I have recently updated the Organisation Evolution Framework model that I proposed in 2020, to its release 3.0.

You can find it on its new page directly on my site, where it is also possible to download a pdf version.

Leave a comment

The Laws of Organization Design

I have started reviewing some content of “Organization Design” Laws. Far from being real laws, these are often the fruit of observations and experience of academics or practitioners. But are very useful to drive discussions, avoid hidden spots and complement our focus on Organization Design applied in company.
Here the first article published.

• Parkinson Law

• Law of Triviality

• Goodhart’s Law

• Conway’s Law (this was already published in the past)

— Sergio

3. The (un) Intentional Organisation 😁

Source: Pinterest

4. Keeping in Touch

Don’t hesitate to reach out by hitting “reply” to this newsletter directly or using my blog’s contact form. 

I welcome any feedback on this newsletter and the content of my articles. 

Find me also on:

• X (formerly Twitter)

• LinkedIn

• Instagram

• Medium

• Refind

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• Readwise

Welcome back!

Many of you have probably been enjoying some time off this summer, resting, relaxing, and exploring new areas of the world (And some of us may also have learned a new word this year: OverTourism).

For me, this is, however, a slightly different return. I’m reactivating this newsletter after a two-year hiatus that probably lasted too long. For those who know me, writing has always been an essential process in my life. But also a very discontinuous one.

Yet, here I’m back, at the edge of the season that many of us label “return-to-work”. My commitment this time is to issue this newsletter every two weeks. Why? The last two years, with the additional experience and the additional curiosity about what’s happening around me, showed me that there is still a lot of need to reason in terms of Intentionality.

So, welcome back. I hope you enjoy reading this newsletter again!

— Sergio.

2. Intentionality and Emergence in AI

Artificial intelligence has definitely received more attention globally since the last time I wrote this newsletter. The concept of Generative AI is becoming widespread across organizations (and not only). The images used in this issue, for example, are all AI-generated.

Interestingly, this field is a perfect area to examine the impact of Intentionality and Emergence, two foundational concepts at the heart of the Intentional Organisation concept.

Intentionality and Emergence in the Context of AI

Intentionality, in the context of AI, refers to the directedness of a system's operations towards a specific goal or purpose. In AI, intentionality is typically encoded by human designers who create algorithms with specific objectives, such as recognizing patterns, making decisions, or optimizing specific outcomes. Intentionality implies that the AI system operates with a purpose, even if that purpose is externally defined and not inherently "understood" by the AI itself.

Emergence, on the other hand, refers to the phenomenon where complex patterns or behaviours arise from the interactions of simpler elements within a system. In AI, emergence is not explicitly programmed but arises spontaneously from the interactions of numerous components, such as neurons in a neural network or agents in a multi-agent system. Emergent behaviours can be surprising, unpredictable, and often more complex than the sum of the elements involved.

The two elements can create reinforcing loops, especially when emerging behaviours are captured by AI systems' self-learning patterns. We have read a lot of “emerging” unintended consequences in the world of Generative-AI. But how do these aspects play in an organizational context? Let’s reflect a bit on this.

AI Intentionality vs. Emergence in Organizations.

There are at least five areas where the impact of the intentionality vs. emergence dichotomy in an organizational context.

1. Decision-Making Processes

We often implement AI systems with specific, intentional goals, such as improving efficiency, enhancing decision accuracy, or optimizing resource allocation. These goals shape the design of AI tools that assist or even make organizational decisions. For instance, we might deploy AI to intentionally streamline customer service operations or automate financial transactions to reduce costs and improve response times.

However, as AI systems are integrated into decision-making processes, emergent behaviours can arise, influencing decisions in ways that were not explicitly intended. For example, the same AI intended to streamline customer service operations might identify new patterns in customer behaviours that were previously unnoticed. This can potentially lead to AI-generated decisions that are not in line with the original “intentional” design.

We need, therefore, to be able to intercept, analyse, and comprehend these new emergent properties by ensuring that the decision-making structure does not transform itself into a “black box” led solely by an unintelligible algorithm. This might involve creating cross-functional teams that can rapidly respond to emergent insights, or establishing feedback loops where human oversight can intervene if AI-driven decisions lead to unintended consequences.

2. Workflow and Task Management

Another area where organizations typically design AI systems is the intentional goal of automating routine tasks to improve efficiency and reduce human error. This intentionality shapes how workflows are structured, with AI tools often taking over repetitive, data-intensive tasks, allowing human employees to focus on more complex and creative work.

However, as AI systems are deployed, emergent behaviours can alter the dynamics of workflows in unanticipated ways. For example, an AI system designed to optimize task allocation might start redistributing work in a manner that inadvertently creates bottlenecks or disrupts established team dynamics, thus deteriorating results.

Organizations need to design workflows that are not only optimized for AI's intentional capabilities but also resilient to emergent changes. This could involve developing flexible work structures that can adapt to new patterns and behaviours as they emerge, ensuring that the organization remains agile and responsive.

3. Innovation and Organizational Learning

A third common area in AI implementation is driving innovation, whether through product development, market analysis, or customer engagement. AI systems are designed to analyse large datasets, identify trends, and generate insights to inform strategic innovation initiatives.

Here, Emergent Behaviours can be a powerful catalyst for organizational learning if the AI system and the Human supervisory teams are built in a way that is capable of capturing these elements. Unfortunately, we have seen a lot of cases where AI reflects the same type of bias in data analysis that their human programmers possess, thus reinforcing negative emergent behaviours rather than identifying opportunities.

4. Ethical Governance and Compliance

A hot topic for discussion today is the embedding of ethical principles into AI systems with the intentional goal of ensuring fairness, transparency, and accountability. Despite these intentions, there are many examples of emerging behaviours in AI that present ethical challenges. For example, an AI system intended to improve hiring practices might develop biases based on the data it was trained on, leading to discriminatory outcomes. Or it could use data that should be protected to drive decisions about individual performance. We need to be seriously conscious of this and the increased scrutiny that employees and public stakeholders are taking on this topic.

5. Organizational Culture and Change Management

A broader impact (yet to be fully grasped) is the effect of embedding AI in organizational processes for any of the reasons already mentioned. In many organizations, leaders are promoting AI adoption as part of a broader digital transformation strategy, aligning the organizational culture with the goals of technological advancement.

But, as AI is embedded into daily operations, it can contribute to emergent changes in organizational culture. For example, the increasing reliance on AI for decision-making might lead to a shift in power dynamics, where data scientists and AI specialists gain more influence within the organization. Automating specific tasks might change employee roles and identities, potentially creating resistance or altering organizational climate.

Conclusion

The interplay between intentionality and emergence in AI has significant implications for organizational design. Organizations that fail to recognize and manage this relationship risk facing unintended consequences, ethical challenges, and disruptions to their processes and culture. To effectively leverage AI, organizations must design flexible, adaptive structures that can accommodate both the intentional goals of AI implementation and the emergent behaviours that arise. Only this way, can we harness AI's full potential while remaining responsive to the complexities and uncertainties of the technological landscape.

Leave a comment

— Sergio

3. My Blog Refresh

Just before summer, I fully refreshed my blog, moving away from WordPress and into a new structure that is more content-focused, based on Notion and a web interface solution provided by Super.so.

As part of this, I started reviewing a few articles that still drive attention today:

• Leadership Models: The Theory and the Practice [June 24, 2024]. I have moved the Leadership Models collection into a Database format, allowing for easier usage and more flexibility. I will continue to add content in terms of models (there are more than 145 models currently listed).

• The Ultimate Quest for the Meaning of Work: [July 1st, 2024], I have done a quick edit of this series of posts, ensuring links are consistent in the new platform and correcting a few issues (including some missing images and outdated links).

• Business Models: The Theory and the Practice [August 21, 2024]: I have added a few models, checked spelling, and embedded feedback.

I have reviewed the entire site's look & feel and navigation. My goal is not to review all of its content but to keep it as a reference, focusing on the essay part and adding more relevant content as needed.

4. The (un) Intentional Organisation 😁

Source: The Seattle Times

5. Keeping in Touch

Don’t hesitate to reach out, either by hitting “reply” to this newsletter directly or using my blog’s contact form. 

I welcome any feedback on this newsletter and on the content of my articles in general. 

Find me also on:

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A subtle line ties in large organisational transformation projects and the extensive discussions that are happening today about the return to work in physical offices. This line is about the role of accountability in an organisational setting.

1. Many significant organisational transformations fail because individuals don't feel accountable for change results.

2. The issue with remote work is that too many organisations (or rather, many managers) have not yet come to terms with handling the concept of individual accountability.

A big part of the problem in handling accountability in an organisational setting is linked to the fact that we tend to use the word interchangeably with the word responsibility. Even when run with a different meaning (like in a RACI matrix), there's still the underlying idea that responsibilities and accountabilities can be part of the same delegation process of organisational processes to individual roles.

Why is Accountability Different?

Accountability is different in a critical aspect.

Accountability is the acceptance, good or bad, of your personal actions that contributed to attaining or failing to meet an intended goal.

Following the above definition, we can see a critical differing aspect. Responsibility can be attributed to a person through a process of delegation. Accountability is instead about personal acceptance: individuals need to take an intentional act of acceptance of being accountable.

Traditional hierarchical power handling has created organisations where responsibilities are architected and planned carefully through formal delegation processes.

Yet, accountability is often much less present in most of these organisations. People tend to refuse responsibilities that are not formalised, actions that are not explicitly demanded initiatives that their line managers do not plan, and tasks that are not detailed in job descriptions.

The entire pandemic caused working-from-home experiment is essentially an Accountability Revolution. Why? Because it demonstrated how micro-management approaches don't work in that context, and many managers had to rely on the accountability of their teams to get things done. Almost a quantum leap for many.

Accountability is built on a foundation of trust and support, where employees are motivated to do the right thing and take responsibility when they do not. Something that can only come from a mix of self-interest and external push.

Building on such an Asset

Seeing management through the lens of accountability means having the tools to build new managerial processes from scratch. An example? Performance Management. Self-selection of goals and individual commitment makes it easier for employees to feel accountable rather than receiving them in a top-down model.

Another example is the OKR Model. Again, one of the key differences with the traditional MBO models is the team accountability that it tries to stimulate.

A third example can be Agile methods applied not only to software development but also to other areas of management: they all work thanks to ingrained levels of accountability.

Accountability as an Intentional Capability

It is easy to see why Accountability becomes such a critical capability for an Intentional Organisation. Clarity of goals and consistency of organisational assets constitute a fertile ground to develop accountability in individuals and teams.

But how do you nurture accountability? I believe this has become a key component of modern management and the basis for any self-management experiment. Feedback becomes a foundational element in creating constructive accountability, which you want to nurture as an organisation.

A Mindset Shift

What is critical to understand is that the world of "holding people accountable" is gone. The entire concept of developing accountability through command and control has never been working and shows an essential failure of traditional managerial models.

We need to honestly think of accountability as an enabler of intentional self-expression of the contributors to an organisation's success. In a world where people are constantly looking for a new meaning of Work in terms of self-realisation, accountability is what ties them to organisational purpose, becoming the basis of the organisational contract.

Sergio

Cover Photo by Adi Goldstein on Unsplash

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In this post, we reflect on the difference and convergences between DAOs and Entrepreneurial Ecosystem Enabling Organizations, following some preliminary thoughts that have been shared in our 3EO community. We also take the chance to review some insights coming from our recent webinar with Ben Mosior during which we reflected on the evolution of organizing that Web3 could bring, through the lenses of Wardley Mapping

4. The (un) Intentional Organisation 😁

Source: Pinterest

5. Keeping in Touch

Don’t hesitate to reach out, either by hitting “reply” to this newsletter directly or using my blog’s contact form. 

I welcome any feedback, both on this newsletter and, in general, on the content of my articles. 

Find me also on:

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When I first read Douglas Adams' The Hitchhiker's Guide to the Galaxy, I felt the author was an absolute genius in describing the scene of the Deep Thought supercomputer spitting the number 42 as The Answer to the Ultimate Question of Life, the Universe, and Everything. But unfortunately, the answer seems meaningless even to the computer creators because the beings that architected the supercomputer never really knew what the actual question was.

As I started composing this newsletter and reading the number 42, I had to write this. Why? Because appealing to my "nerdy" side, I have collected, over time, several moments where I have witnessed organisational processes experimenting with authentic 42 moments.

A result appeared out of a process that nobody expected. And some people started investigating what the question was.

• How often have you seen an Analytics team creating dashboards to discuss how to use the data results? A clear sign that the dashboard is not answering questions.

• How often have you seen a company run a lengthy budgeting process involving all functions, producing a final number that the current situation has already made "outdated". What is the question the budgeting process is trying to answer?

• How often have you witnessed a recruiting team do its best to find new talent, get out in the market, run interviews, finally get the right person, onboard the person, and just a few weeks later, the HRBP is having a conversation with the Hiring Manager asking why that role was hired for.

• How often have you seen a company launch a transformation process, hire a consultant who develops a lengthy process, landing a Target Operating Model presentation that people wonder what is this for?

• How often have you seen somebody propose a "Best Practice" to be applied to a company, to see the puzzled faces of other managers wondering what precisely the problem you are trying to solve is?

• How often have you heard people talk about "agility" in a business context, just letting you wonder what the issue is here?

The 42 Syndrome

All rhetoric questions, of course. Yet many organisations face issues like these.

I call this The 42 Syndrome. It happens when companies put in place considerable transformation efforts that produce outputs of negligible (if any) value after a long time and where initial value assumptions (usually much higher) tend to be forgotten.

(There's another expression I learnt in France that has a similar echo: Usine à gaz. This focuses more on the difficulty of understanding the system).

Why does this happen?

1. We don't spend enough time defining the problem we want to solve. Instead, transformations are carried out simply because there's a best practice somewhere outside. Or because agile is a trendy methodology.

2. We build business cases that don't have much relationship with reality, focusing too much on intangible benefits with dubious value. Plus, we don't put complete monitoring systems during the project.

3. We create projects that try to address too many issues at once, trying to find the ultimate solution.

4. We run projects in silos (an element that is worsened when external providers are involved using waterfall processes), not allowing enough visibility on interim results.

5. We involve end-users late in the process. For example, often, testers are involved without real visibility on why a system or method was designed that way.

6. Ultimately, there's no actual governance on results measurement.

What's the answer?

I believe the answer is intentionally defining problems at the early stage of any change process and monitoring that the problem definition doesn't change as we go along. Only this way, we won't be surprised by the answer.

Or?

Did you also have a 42 moment in your company? I'd love to hear from you.

Sergio

Cover Photo by mohammad takhsh on Unsplash

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4. The (un) Intentional Organisation 😁

Source: Pinterest

5. Keeping in Touch

Don’t hesitate to reach out, either by hitting “reply” to this newsletter directly or using my blog’s contact form. 

I welcome any feedback, both on this newsletter and, in general, on the content of my articles. 

Find me also on:

• Twitter

• Linkedin

• Instagram

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I've been asked a fundamental question. How do you evaluate investing time in developing an Intentional Organisation?

I thought for a long time around this. Unfortunately, it's very improbable that standard Return on Investment measures would work. Why?

Return on Investment, calculated using traditional metrics, is an important performance measure. However, it is very much limited by the ways we measure inputs and outputs, particularly as we tend to assume most financial measures.

When we create business cases, the list of assumptions tends to be very long to ensure a proper measure of ROI. Yet, results often differ from expectations, essentially because there is a very scarce understanding of value chains in organisations.

Without understanding how value is created, how can you estimate a return on investment? That's why the measures are, at most, limited, if not entirely incorrect.

Return on Imagination.

In an Intentional Organisation, I believe we should be looking at a different kind of ROI the Return on Imagination (ROIm).

As suggested by Tom Wujec in his 2008 book by the same title, the idea is that the actual value of design can only be assessed by the level of imagination it carries.

Imagination, after all, means the action of forming new ideas, which is exactly what we are trying to do with our organisation design efforts. We want to architect a future organisation imagining its complex consistency system.

Very often, organisation design effort is marked by defining a Target Operating Model, measuring the gap with the current state, and then estimating the investment needed to fill the void. Returns, however, are primarily dependent on reaching that future state.

Reasoning in terms of Return on Imagination means also taking into consideration the variegated inputs of the implementation process and the design process itself.

Imagination is associated with a general sense of positivity. When we imagine something, we tend to associate the value of an improvement with our destination design. This positive energy flow is already an important asset to be considered.

But ROIm is not just something that "happens": it's a way to assess the permanence of several organisational capabilities, as well as a way to control the build-up of organisational debt.

Building Specific Organisational Capabilities.

Ideally, several organisational capabilities need to be fostered, based on individual solid competencies:

• Active Sensing

• Learning Agility

• Applied Learning

• Envisioning

• Innovation

The idea is the following: the organisation needs to constantly and actively listen for internal and external inputs about change necessities (Active Sensing), especially at the edges of the ecosystem. It needs to translate these mostly "weak signals" into adaptive organisational practices through a cycle of Learning Agility (learning something new everyday) and Applied Learning (application of the new learnings). Joined with the capability of Envisioning Future State Results it will help shape the true Innovation for the company.

Enabling true innovation.

In the context of The Intentional Organisation, innovation takes a key significance:

Innovation is the capability of the entire organisation to become an adaptive part of the ecosystem, able to deliver distinctive and sustainable Value(s) to all its Stakeholders for prolonged periods of time. 

This is achieved by constantly innovating products, services and the organisation itself.

Imagination is the "fuel" that enables the innovation engine, which, as seen, is not just about products or services, but also the organisation itself.

ROIm becomes a critical concept, as it moves the focus from one of the value flow (financials) to a more "holistic" view. It will consider the impacts of investment that the organisation does in time dedicated to learning, to experimentation and agile prioritization.

Conclusion.

Thinking in terms of Return on Imagination means considering a number of factors that are part of the intentional design of the future of an organisation. It means understanding that a big part of the work is building on the capability to imagine the future, and building the consistency elements necessary to get there.

This is particularly relevant when we think at the impacts on Leadership and Corporate Culture components: their inconsistency with new design is often the biggest laggard element for successful change.

ROIm is particularly relevant as we build Ecosystems, simply because we can't control all actors of the environment. And the concept itself of sustainability is not easily measurable with sole financial values.

This topic needs further study, and I will be putting some more effort on this. Feedback are warmly welcome!

Sergio

Cover Photo by J. Balla Photography on Unsplash

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