If you’re new here, I’m Brennan McDonald and I write about the people side of AI transformation. This newsletter grows through word of mouth and your recommendations. If you enjoy this, share it with a friend today. It’s always appreciated. If you have any feedback for me, you can reply to this email.

- Brennan

In today’s newsletter:

• Everyone is being asked for an AI story

• What AI does to the work you already do

• Where AI change comes unstuck

• Where to start, and what to do next

Everyone is being asked for an AI story

All your stakeholders have an opinion about AI. Give everyone access to Claude, get a consulting firm in to map out all of your existing processes, outsource your operations overseas, or vibe code a new CRM.

In 2026, you definitely need an AI story. It can’t be about what other people are doing. It has to be what suits you and your business and the industry you operate in, inside all of the regulatory constraints and industry practices you have to get right.

There’s a gap between what you’re doing today and what is possible with the help of AI. I often see comments like “I can’t think of ways to use AI in my business” or the like.

This sort of thinking is a tragedy. If you’re struggling to come up with use cases, all you need to do is turn on the voice command. Dictate into your favourite AI tool, tell it about your business and ask about basic use cases.

It really is that simple. For example, one client did not know how to connect one application with another. They were still copying and pasting. It turns out all they needed to do was ask for developer access, get given an API key, and then suddenly they could easily query and access their data which had previously been locked up.

There are efficiencies lying on the floor in every business in the world at the moment. A lot of hardworking and clever business owners just don’t have the time to even think about which questions will get the most out of an AI agent if they want it to partner with them.

AI is a people problem, not a technology problem. What this means is that if we’re leaders and we’re business owners, we can’t just delegate and outsource the thinking around what AI change needs to look like in our business. We need to put in the work.

The risk when you have the pressure to tell a story about how you’re using AI in your business is that you fall prey to what I’ve called cosmetic AI. Everyone gets a ChatGPT Enterprise licence.

Maybe you put in some workflows which reduce manual effort on repetitive tasks. You might even connect system one to system two, which means you don’t need a team which used to manually reconcile the two anymore. A lot of this stuff could have been done even before the AI era.

I think a lot of this AI washing is partly people finally realising maybe we could use technology to help us solve problems. The intelligent firm is somewhere a lot of companies are quite far from on their journey.

They need to do a lot of deep thinking about their operating model as it stands today and where it could be with the fullest application of the multiplier effect of AI before they are in a position to really break through.

What is your AI story going to be if vast parts of your operating model as they stand today have no reason to exist in the AI era?

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What AI does to the work you already do

An operating model is people, processes and platforms. As we move into the AI era, firms where more of the thinking and execution is done by AI agents are building on top of the foundations of what has gone before.

If you remember the cloud migration era, there were two ways that businesses dealt with this. The first approach was called lift and shift, where you’d take an application and just deploy it in the cloud with limited reflection on what refactoring or redesign was required to optimise things like cost, security and efficiency.

The second was by doing a process to make what was being deployed to the cloud more suitable. This involved reflecting on the overall architecture of the business to make sure that you didn’t have absurd situations that existed in on-premise data centres being perpetuated into the cloud era.

For those of you who’ve worked on projects like this, yes, I’m simplifying. For the purposes of this general business audience, I think it is a fair reflection of what the two main approaches were.

The first approach of lift and shift led to outcomes like security breaches. Bill shock. And a level of complexity which did not use a lot of the native functions of the cloud platforms like AWS.

The second approach of redesign led to some of the first movers achieving much more efficient and cost-effective operating models. These were not only more resilient, but were in a position to take advantage of all the incremental additions to cloud platform functionality as they were introduced.

We can think of AI as a multiplier on the operating model you already have today. You already have strengths and weaknesses, where the problems are in your business, and where the pain points are.

You need to be open with yourself if you’re thinking about AI transformation in your business. If you have a weakness, maybe you never put enough into cybersecurity, or maybe you never put enough into data governance and data quality.

If your operating model is already this brittle, AI will widen every crack you have been ignoring. Where to start matters more than which tool you pick.

Where AI change comes unstuck

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In today’s newsletter:

• The Anthropic and SpaceXAI deal

• Why the theory of the firm needs an upgrade

• The playbook for building an intelligent firm

The Anthropic and SpaceXAI deal

If you’ve been using Claude, Claude Code or Claude Cowork over the last few months, you know that at times it just doesn’t work.

The rapid growth in use of Claude has led to enormous pressure on Anthropic’s ability to service that demand with the supply of compute it has access to.

As you can see below, in an industry where multiple nines is considered good uptime, Anthropic’s product suite has not had good uptime.

Dario Amodei revealed that in the early part of 2026 they were experiencing 80x growth when they had planned for 10x growth.

Times like this are completely insane. You have to give them some indulgence. OpenAI is not in the same position. Nor is Google. From the perspective of enterprise customers, uptime at that level would immediately disqualify most vendors from even being in contention or getting past procurement hurdles.

Fortunately for Anthropic, they are selling one of the best implementations available of general intelligence. They can be consumed across a wide range of products or via API endpoints. They charge a premium. This is especially true for their top model, Opus 4.7. Their recent marketing of the Claude Mythos model has caused a panic in governments, corporates, and regulators around the world.

I think a lot of people forget that if you go beyond the headline, a lot of the stories that get written about AI miss the wood for the trees. ChatGPT 5.5 is already benchmarked to have similar or greater capability on cyber than Claude Mythos.

For enterprise customers, especially big ones, despite the preference of developers and non-technical users alike for Claude, what we’re seeing here is a need for a multi-vendor strategy.

How would you feel if the chart below was powering most of your operations? You would be pretty filthy. This is especially true if you are in a regulated industry. Any outage at all needs to be reported to the regulator. It also comes with a lot of issues. This includes the consequences of making those sorts of notifications.

All this background is important to understand what’s actually going on when Anthropic does a deal with SpaceXAI. They are growing so fast that they need compute and they do not care where they get the compute from. Almost all of the deals they have announced so far in 2026 are for capacity. That capacity is yet to come online with other partners.

SpaceXAI effectively had an under-utilised capital asset that they had spent a fortune building and using, sitting ready and as a “real option”. The deal Anthropic struck gives them access to the entire Colossus 1 data centre with something like 220,000 GPUs of various types originally focused on training earlier Grok models.

I was astonished to see how bad so many of the takes were on X around what this means for SpaceX AI, what this means for Anthropic, and what this means for Grok. Colossus 2 is an even bigger and better data centre where more Grok models are still being trained. Some analysts have estimated that the $5 to $6 billion per year in GPU-rental revenue that Anthropic is likely to be paying SpaceX AI will roughly offset the annual costs of SpaceX AI itself.

So what’s actually happened? Anthropic gets a short-term immediate boost to their compute capacity. This is why they are able to slightly ease the limits. They are still facing limits, though, due to the enormous continued growth of their business.

SpaceXAI is part of SpaceX. It is going to IPO shortly. It will get billions of dollars in high-margin revenue. We will soon know through the SEC filings roughly what Anthropic are paying. SpaceX will have to disclose a whole new category of hyperscaler revenue.

This will attract a different multiple from other parts of SpaceX revenue. It will also give more debt servicing capacity to build things like the TerraFab. Or more data centres for SpaceXAI.

What does this mean for AI transformation? You need to understand what is happening at the top of the supply chain. This is important if you are going to build a business that is sitting on top of Frontier Labs building the AI models and an ecosystem of data centres serving the compute that you rely on.

Just like you would expect your chief technology officer to be well-versed in every single little thing that is critical and moves the dial for your AWS, GCP, or Azure reliance, leaders in business need to know what is happening under the hood of whatever API endpoint you are relying on. Who is serving their model, and where are the dependencies?

Why the theory of the firm needs an upgrade

Understanding the physical reality of this supply chain is the first step, the next is restructuring your firm to actually capitalise on it.

An operating model is just people, processes and platforms. The coordination of all these factors used to be a heavily human task. The theory of the firm is a concept that I have written about extensively over the last year.

As part of that research, some of the thinking I have done is about what changes as AI capability rises. The need for verification and accountability rises, and a firm faces a choice between allocating tasks to humans, humans using AI, AI agents themselves, or external suppliers.

There are a number of economists doing good work in this space. One important thing to remember is that there is a lag between how fast AI is changing and the pace at which widely held beliefs in a profession like economics update through the process of journal articles, peer review, and conferences.

The intelligent firm, where more and more decisions are removed from human oversight to decision-making by AI agents inside guardrails placed around them, presents a real dilemma.

All of this might seem a bit over the top and not worth thinking about if you’re running a function, running a business or an AI project. Yet, I think what is being revealed when people have simplistic reactions to the SpaceXAI-Anthropic thing, saying things like “Grok is over” or “Anthropic shouldn’t have given money to SpaceX,” is the next level down of thinking about how the ecosystem of your supply chain works.

We’re all building on AI API endpoints, and as part of the duties we owe, we need to be going deep. We cannot be cosmetic in our thinking and application of AI.

In the next section, I am going to share some more of my thinking. This thinking is around all of the different levers that a firm has in its operating model. The goal is to become more intelligent. It is moving that decision-making and strategic layer from human to machine, and getting the absolute most value from AI and technology.

The playbook for building an intelligent firm

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There is a mood I see on social media at the moment. You can see it in the defensive reactions to Coinbase’s 14% headcount reduction yesterday.

It’s an attitude which I’m calling, the desperation for AI to not be real.

The CEO of Coinbase, Brian Armstrong, announced layoffs of 14% of Coinbase employees. It’s not pleasant for those impacted. Hopefully they are able to find something suitable and soon.

In the X post below, he outlined some of his thinking. I have not seen this level of attempted pushback to an AI announcement. Even the Block AI layoff announcements that were made a while back did not get this level of shade.

Why simple stories about AI change don’t work

As humans, we love to tell simple stories. CEOs of publicly listed companies have incentives to tell simple stories that are easy for investors to understand. That doesn’t mean they’re wrong. It doesn’t mean that when they talk about how they want to change their operating model, that they’re not serious.

One of the more simplistic stories going around today is this is all just another case of company underperformance and over-hiring in the low interest rate era. They’re jumping onto using AI just to cut costs and look better.

I think the story is getting played out by events in the real world as they unfold. When you take a step back and actually think about how things work, you know that many different things can be true at the same time.

The simple story is often glossing over lots of other components which need to be at least considered and evaluated on an equal footing with your preferred simple story before you really start drawing conclusions about what’s really going on.

When I read what Brian Armstrong is saying, what I am hearing is a lot of focus on centring the people strategy around people who are already or can use AI to do things better, faster and cheaper.

It also reads as if they’re doing quite serious experimentation with exactly what their future operating model and organisation chart is going to look like. That’s exactly what you want to be doing right now.

One aspect of his post that has had a lot of pushback is the no “pure manager” comment. I think he is being reasonable. Why? As I’ve written about many times over the last year, the operating model of the future is moving away from human coordination to AI agent coordination.

You need your management layer to be technical. They should take advantage of, guide, and apply their taste and experience to the work being done. They also need to curate and coach the remaining human workforce.

This means people who just want to do their one-on-ones, send emails and sit in on endless meetings don’t really have much of a role to play. When CEOs are merging PRs, we should be expecting everyone to be much more hands-on.

The other part of the post which is getting a lot of pushback is the comment around non-technical people now vibe coding things to production. Coinbase is a publicly listed company. It is also regulated.

I would expect what is happening under the hood to be much more controlled: a non-technical person can create a PR, but it only ships after CI/CD, testing, security and code review gates.

Brian Armstrong has already responded to some of the comments with this:

Why do people want to put AI back in the box?

People don’t like change. One of the central lessons of change management is that if people don’t feel like they have a clear sense of where they fit in and how they benefit from a change, they’re going to fight it.

I can’t help but notice that a lot of people still have a reflexive desire to minimise what AI is changing. Any news item or announcement or improvement in the state of the capability of these tools is met with so much negativity.

The reality is that our world moves forward through the actions of live players. The CEOs of public companies have chosen primarily to lean into AI and use it for work and for achieving their other capital markets goals.

In the game they’re playing, it doesn’t really matter what people on the timeline think. They are choosing to take action. This is based on up to dozens of underlying reasons or results from their AI experiments.

Many public company CEOs, especially founder CEOs, have realised something. They need an AI story. This story must align with how they use AI. It should show how AI makes things better, faster, and cheaper.

If they don’t have a story that aligns to how they are using AI to do things better, faster and cheaper, their access to and support from the capital markets is at risk.

Public company restructures rarely happen for no reason at all. A restructure like this would almost certainly have been tested, planned, workshopped, brought up at board level, and thought through carefully.

This is just the latest instalment in something that we’re going to see more and more. What I find interesting is that there’s even an argument to be made that the 14% headcount cut versus Block’s 40% headcount cut is perceived as not aggressive enough by the street.

What do people strongly rejecting this framing and claiming that it’s the end of Coinbase think is going to play out over the next few years?

There is still a need for skilled, capable people. The leverage of AI tools means they can do more things. They can do them better, faster, and cheaper. It also means that anyone not pulling their weight is more exposed.

There are definitely some potential downside risks here. Things need to be managed and mitigated. Every business can’t just cut without making sure critical tasks are transferred, or automated, or removed from the operating model.

What I would caution is thinking you can go slow and implement some form of gradual and staggered incremental change. You need to start off with the experimentation. Learn from mistakes. Bring people along on the journey.

At some point, you are going to hit a wall. I think this is where some of the stories that experts tell around new jobs created and old jobs destroyed start to come off the rails.

Many big companies likely still have excess headcount, but AI changes the calculation around what can be automated, simplified, transferred, or removed. You might be telling a story that AI isn’t the reason for the layoffs, it’s just excess hiring from the pandemic times. If you take into account AI’s capability to improve efficiency further, even more excess headcount could realistically be cut.

This is why the simple story is a real challenge. We can’t undo what is already happening in a hyper-competitive marketplace. If we aren’t thinking about how to radically reorganise and redesign our operating model to take the maximum leverage from AI, we’re at risk.

We also need to leverage our skilled and experienced people. They bring taste, curation, relationships, knowledge, and expertise. There is still a transition period. A lot of these people are not who you want to be including in your “AI layoffs”.

Below, I break down Armstrong’s memo line by line and translate it into operating-model questions and actions for your business.

How should I think about this in my business?

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If you’re new here I’m Brennan McDonald and Getting AI To Work is about the people side of AI transformation. Because it’s not a technology problem, it’s a people one. Paid subscribers get access to a library of diagnostics and playbooks to apply to their own business and deeper insights.

In today’s newsletter:

• Enterprise AI spend will keep rising and needs to pay off

• Why change management matters more than ever

• So what’s stopping you from getting AI to work?

Reply to this email or drop a comment or DM with feedback.

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Enterprise AI spend will keep rising and needs to pay off

How much is your business spending on AI each month? What is your AI budget for 2026?

Last week I shared the story of how Uber has already “blown” the budget it allocated for 2026 because of the insatiable demand for AI tokens especially from engineers.

When you add that to the enormous numbers of CapEx guidance that the Big Tech hyperscalers are planning, over USD700 billion for 2026, it’s clear that an enormous amount of money is going into enterprise AI spend across the whole stack.

Revenue from providing AI via the cloud platforms to enterprise clients is growing rapidly. The change in the details of the agreement between Microsoft and OpenAI has led to AWS Bedrock now offering the OpenAI models.

You can see the demand management strain in premium-model pricing, tighter subscription limits, and providers policing heavy or non-compliant use of flat-rate plans.

All of this combined is why my claim is that this isn’t a bubble. This is a massive infrastructure build-out. If you read any of the Wall Street research reports or independent research it goes into immense detail. It’s not just the AI labs. It’s the data centres, the power suppliers, and the chip manufacturers. It’s all of the associated products and services with servicing one goal: more compute.

This is all progressing much faster than any other technology. What this means is that some of the mental models we had around previous tech waves are probably wrong.

There’s another aspect of the cost of AI use which I think is massively underexplored. And that is the use of open source models that are much cheaper than the closed source models. My own experimentation has enabled me to build workflows that use cheaper models and get better outcomes through more structured prompts and context management.

That ability to much more granularly allocate a specific model for a specific subtask in a process means that the opposite of this enterprise AI spend is that a lot of people are going to be able to do more granular cost management to be able to keep up.

Another example is my recent testing of DeepSeek V4 Pro. It’s nearly at the frontier. If you read anything in the news claiming that Chinese open source models are years behind US models, you’re getting an outdated picture. The most powerful US models are still the best, but that margin is shrinking. For most daily business tasks, you probably don’t need Opus 4.7 or ChatGPT 5.5 Pro.

Again, what is your AI monthly spend or AI budget? Think about that number.

Why change management matters more than ever

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This week’s article wraps up a 12-week focus on building out the 5C framework. There’s definitely a gap in much of the AI content I see and read around the human side of AI. That’s one of the reasons why I refocused this newsletter last year around the people side of AI transformation and change.

There’s more to an AI transformation than getting all the ChatGPT licenses and controls setup or making sure that everybody does their e-learning modules on time. The longer I’ve been in this space, the more I realised that there’s another area of content or education I need to share with my audience around what is possible with AI from an operating model perspective.

This will build on the change management focus here. It will give more usable diagnostics, playbooks and templates for you to unlock the next chapter of your AI transformation.

Start here if you are new to this series: Your AI Training Isn’t Working. Here’s Why.

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The Pattern Behind the Quiet Failures

Somewhere in most organisations, an AI initiative is stalling. Not in a dramatic way. Not with a public failure. It is just slowing: launch energy gone, timeline slipped three times, the team meant to own it pulled onto other things, and nobody willing to say out loud that it might not work.

Most of the time, technology is not the part that breaks. The technical capability is increasingly available. Something simpler is missing: people have not agreed on why this matters, what should happen next, who gets to decide, or what success would look like.

The ones that succeed run adoption as a system rather than a project. Over twelve weeks, this series has mapped that system through the 5C Adoption Friction Model: Clarity, Capability, Credibility, Control, Consequences. Ignore any one of them and it pulls the initiative off course in a way the dashboard will not show.

Find the friction. Remove it. Move to the next. This week, the system.

The Five Friction Points Condensed

Clarity is about whether everyone is working from the same idea of what this is for. Your CFO thinks it will save headcount. Your Chief People Officer thinks it will lift the team’s skills. Your product team thinks it will ship new features. Everybody below them gets a different story and picks whichever version keeps them safe. You end up with a pile of projects pulling in different directions.

Capability comes down to skills. You have an accountant who has spent ten years in Excel, and you cannot expect them to start building advanced AI workflows tomorrow. Not without training. Not without coaching. Not without some permission to slow down while they learn. People try anyway, fail, and blame the tools. Adoption stalls, confidence drops, and your would-be advocates stop volunteering.

Credibility runs on trust, which is fast to lose. Leaders talk transformation but never put a budget behind it, set deadlines nobody can meet, and quietly pull someone off the project the second things get hard. Each move tells the team this does not really matter, and eventually your people stop believing anything you say about change.

Control sits at the level of power. People worry about their jobs, role changes, how their data is used, whether anyone will ask how this should work. They will not say any of it out loud. So if nobody brings it up, the pushback shows up later, slow and quiet, and your most influential people end up blocking the work.

Consequences turn on the stakes for people. When adoption is optional, early adopters get no recognition, and there is no real cost to waiting or reward for moving, the rational thing is to do nothing. Six months on, you cannot point to any results, justify what you spent, or hold onto the budget for what comes next.

What All Five Have in Common

Even the skill problem is not only a skill problem. People are not refusing the tools. They are protecting who they are at work: their role, their authority, their standing. Underneath, you are looking at identity rather than just competence. Train through it and the training does not stick. Mandate around it and you make the resistance worse.

Most leaders need shared purpose before anything else holds, not because Clarity is always the deepest friction but because the other four cannot work without it.

The Diagnostic Sequence Principle

Most leaders go for the loudest friction, not the one that is actually in the way, and that is how things get worse.

Fix Capability before you have Clarity, and you train people for work that does not matter. Fix Credibility with more communication and you make the trust problem worse. Throw incentives at Control and people hear a threat. Fix Clarity without Consequences and everyone nods, then goes back to what they were doing. In the wrong order, the problem grows. In the right order, each fix makes the next one easier.

The Condensed Diagnostic

This is not enough to fix the initiative on its own, but it will show you where to start. For each friction point below, ask whether the description fits your situation.

Clarity

• Can three different people in your organisation explain the purpose of your AI initiative in the same way?

• Do your frontline teams know what they are expected to do differently, or are they waiting for clearer direction?

• Has anyone said explicitly what problem this initiative solves, and for whom?

Two no answers means this is an active friction point.

Capability

• Do the people expected to use AI tools have time and structured support to learn how to use them?

• Has anyone measured the skill gap between what people have now and what they need?

• Are people learning through trial and error, or through formal training and coaching?

Two no answers means this is an active friction point.

Credibility

• Have leaders protected this initiative when it became inconvenient, or only supported it when it was easy?

• When people express doubt, do leaders address it or dismiss it?

• Have the people driving change made themselves available to listen rather than only to broadcast?

Two no answers means this is an active friction point.

Control

• Have you explicitly named the decisions that will change, and who will make them?

• Are the people whose authority is shifting involved in designing how that shift happens?

• Are people saying “this is a good idea” in meetings but failing to change behaviour afterward?

Two no answers means this is an active friction point.

Consequences

• Are there immediate consequences for adoption, and is it optional or required?

• Are early adopters recognised and rewarded, or does the organisation treat adoption as invisible?

• Is there a real cost to waiting, or can someone do nothing and face no consequence?

Two no answers means this is an active friction point.

Run the diagnostic and write your primary and secondary down. Whichever friction is blocking the others is your primary. The rest follow in sequence. Diagnosis matters. Get the order wrong and it does not work.

From Diagnosis to Action

By now you can see the pattern, and what happens next depends on whether it makes it into your next planning meeting or just sits in a document while another quarter passes.

Below is the Complete 5C Adoption System: the sequencing logic, the 90-day reset, and the conversation framework for the hardest friction.

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If you’re interested in AI, it’s important to remember that most people on the planet don’t care about it. They actively hate it. The people who do care are about to learn the next lesson, and it isn’t a technology lesson.

Last year I wrote a number of pieces on this newsletter about lessons from economics, the theory of the firm, and how they might apply in the AI era. One of those was on operating model compression. The thinking at the time was that a lot of companies would realise the human coordination cost could be replaced through AI agents making better, more efficient decisions.

How is that turning out so far? It turns out many barriers to getting results from AI have nothing to do with the technology itself. It is all about the people and culture within the organisation trying to roll out an AI transformation. Many processes, controls, and ways of doing things that worked well in the pre-AI era are anti-patterns or obstacles in the moment we now find ourselves in. This means a proportion of AI projects are essentially doomed from inception.

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Take Uber.

In April 2026, Uber’s Chief Technology Officer Praveen Neppalli Naga said publicly that the company had exhausted its entire 2026 AI usage budget. Not because of GPUs, hiring, or new data centres. Almost entirely because of inference costs on AI coding tools, mainly Anthropic’s Claude Code and Cursor.

Uber gave around 5,000 engineers access to these tools in December 2025. By March, 95% of engineers were using them monthly. 70% of committed code was AI-generated. 84% were using agentic features. Per-engineer monthly costs ran from $500 to $2,000 in API tokens alone. The CTO described the situation as being “back to the drawing board” on AI spending plans. Nvidia executives have made parallel comments about teams where compute costs now exceed employee salaries. Uber became the poster child because it was honest about it, but it is not alone.

Forget the overspend. The point is what it proves. When the tools are useful, the consumption curve does not match any planning artefact you have. The bill is large because the savings are larger. The budget was the visible symptom. The org chart was the actual problem.

Why? When you think about what a business is and why a business exists, it exists because it is better to put together a bundled operating model of people, process, and platforms in one firm than to contract for every single output. The bundle is worth more than the sum of its parts because of coordination overhead. That overhead is the variable AI eats.

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Start here if you are new to this series: Your AI Training Isn’t Working. Here’s Why.

The hardest part of a reset isn’t the planning. It’s the moment of saying out loud, in a room where it matters, that the current approach isn’t working.

That moment is uncomfortable for a reason. The training programme has a budget owner. The platform has a steering committee. The roadmap has been signed off. Naming that the direction is wrong puts earlier decisions on the table, including some you made. It feels personal in a way most business decisions do not.

Name the discomfort. It is what keeps most initiatives running long after they should have changed course.

The cost of not resetting is rarely visible on a dashboard. It shows up as another quarter of the same plateau, another round of training that does not stick, and the people closest to the work going quiet. You lose momentum, energy, and the trust of the people whose adoption was supposed to make this real.

The Cost of Continuing

The word “reset” carries weight. In technology it usually means something broke and had to be turned off. In leadership it can feel like an admission of error.

A reset is not an admission that the AI initiative failed. It is an admission that the response has been aimed at the wrong friction point.

A reset is the decision to stop investing in a friction point that is not in the way, and to start investing in the one that is.

Training does not fix cultural friction. Tools do not fix a credibility problem. Mandates do not fix a clarity gap. When the response and the friction are mismatched, the response can grow forever without moving anything. What it costs is the time, money and attention that could have gone somewhere else.

Resets that work tend to start in a similar way. A leader stops defending the current approach in the rooms where decisions are made. They name what is actually getting in the way. They redirect resources at it. The political cost of that moment is real, and it is finite. The political cost of avoiding it grows for as long as the wrong response keeps being funded.

What to Say in the Room

The reset needs a clean sentence, not a speech.

“We have been investing as if this is a training problem. The evidence suggests the friction is somewhere else. Before we spend another quarter expanding the current approach, we are going to diagnose what is actually blocking adoption.”

A sentence like that names the mismatch, avoids blaming the people who built the current plan, and creates permission to look at the problem again without pretending the previous work was worthless.

Do not over-explain. The more a leader defends the reset, the more it sounds like a failure. Say what has been learned. Say what will change. Move the conversation to evidence.

Phase One. Diagnosis. The First Four Weeks.

A reset starts with clarity. Not planning. Not solutions. Clarity about which of the five friction points is the one in the way.

The 5C framework names them: Clarity, Capability, Credibility, Control, Consequences. They ask whether people understand the change, can do the work, believe the priority, feel agency over it, and have a reason to move. The work of Phase One is finding which one matters most for this organisation, right now.

Week one. Map the signals.

There is more data already in the organisation than most leaders realise. Which teams turned the tools on. Which groups finished the training. Which departments responded to the strategy email and which did not. Where AI is being used informally, even in pockets the central programme has not noticed.

The work of week one is collecting, not interpreting. The signal is more specific than “people do not care.” The sales team is using the assistant. The operations team never logged in. One product squad has built a workflow nobody asked for. That texture is what matters.

Week two. Interview the friction.

Talk to people who adopted and people who did not. Not a focus group. A short, specific conversation. What would have made this easier. What got in the way. What was tried and abandoned.

Adopters describe what removed friction. Non-adopters describe what created it. Both are useful. The pattern to listen for is which of the five Cs surfaces in almost every conversation, even when nobody uses the word.

Week three. Analyse the pattern.

After eight to twelve conversations and a map of where adoption sits, a pattern emerges. It will not be “everyone struggles with X.” It will be more specific. The teams that adopted are high-agency teams in early-stage work. The teams that did not are in compliance-heavy functions where there is no cost to waiting and no reward for moving. The friction is Consequences, not Capability.

Or a different pattern. The people using AI tools have had a conversation with their manager about it. The people who have not, have not. The friction is Credibility, not tooling.

There is usually one primary friction point. Secondary ones exist. Moving the primary one tends to create space for the others to move with it.

Week four. Confirm and commit.

Take the diagnosis to the leadership team. Show the evidence. Do not propose the solution yet. The work of week four is agreement on the friction point itself. Once that agreement is on the table, the conversation about response becomes much easier, because the question has narrowed.

At the end of Phase One there is a named friction point, evidence behind it, and a decision from leadership that this is what the next phase will tackle. Everything after this depends on that foundation.

The full 90-Day Reset Sequence continues behind the paywall: weekly activities, deliverables, leadership prompts, and checkpoints for Phases Two and Three. Subscribe.

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I used to waste a lot of time with lots of different browser tabs open, trying to stay across everything. Now I just get custom briefings on whatever I am interested in, delivered on a schedule. When I need to go deeper on something, I go off and run a deep research tool to produce a detailed report, drawing on a wide variety of sources including non-English ones. It is a much more targeted use of time than spending hours browsing the internet.

What is happening here is not a productivity hack. It is a shift in how information moves. For most of my corporate career, work was push-based. Slack pings, email notifications, CC lists, forwarded articles, the endless stream of things-possibly-worth-reading. Personal agents invert this. You pull what matters when you want it, and you are interrupted only when something actually requires a decision or an action, and get pushed what you built for your own needs.

Even a year ago this was hard to set up. Now it much easier with tools like OpenClaw or Hermes Agent. But pretty much no one I know in corporate roles actually works this way yet. They are still drowning in the push stream, still manually copying and pasting across systems, still doing work an AI agent could have done better, faster and cheaper twelve months ago.

The reasons are not technical.

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The gap is not where most people think it is

The standard narrative about corporate AI adoption is that companies are behind because the technology is moving too fast, or because they lack the right tooling, or because they have not figured out their data strategy. I do not think any of that is the real story.

The real story is that AI adoption is bottlenecked on people. Specifically, on how people think about technology, what they are economically free to say and do inside their organisations, and what reference points they have for what is actually possible. Many decision-makers in large companies are still forming their views of AI through locked-down, restrictive corporate deployments. Even when those tools are using current models, the experience is often several steps behind the frontier. Their frame of reference is limited.

This matters because the gap between what is possible right now and what most organisations are actually doing is enormous, and it widens every month. Meanwhile, mainstream media coverage still oscillates between bubble framing, labour panic, and tool-of-the-week commentary, which misses the operational change already underway.

The symptom of all this is what I call cosmetic adoption: AI implemented visibly, but without changing how work is actually allocated, reviewed, staffed, or governed. Real adoption changes the work. Dashboards only record activity. The 5C Adoption Friction Model I have been developing on diagnoses why organisations end up here, and which of the five friction points, Clarity, Capability, Credibility, Control, or Consequences, is actually blocking progress in a given team.

The uncomfortable part is that many organisations stall not because nobody knows what to do, but because the people with enough authority to change things often have strong personal reasons not to take visible risks. There are three structural reasons the gap persists, and they compound.

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Reason one: the technical middle ground almost everyone skips

One of the more persistent myths in the AI discourse is that you do not need to be technical to get value from these tools. The counter-myth, equally unhelpful, is that unless you can code professionally you are going to be left behind.

My take is more nuanced, and it matters for how you invest your time.

You do not need a software engineering degree to work effectively with AI tools. But you do need a specific set of working behaviours that most people in corporate roles have not built yet. You need to be able to inspect a failure when something goes wrong, read error messages and logs, understand what context the model has and does not have, break a task into testable steps, check outputs against reality, and recognise when a tool is producing plausible-looking output that is actually wrong. Terminal comfort is a useful proxy for most of these skills. A lot of them are easier to build if you are not afraid of a command prompt.

I know this because I lived it. When I was a student I ran a Linux laptop for a few years. That period built a comfort with the command line that I did not recognise as a strategic asset until much later. When I started using Claude Code, I was immediately comfortable experimenting with it. The learning curve was not steep for me because I had already paid the tax years before.

The people getting the most leverage out of AI tools today are not the non-technical people, whatever the “anyone can use it” marketing says. They are people who can form a hypothesis, run an experiment, read the output critically, debug when something goes wrong, and iterate quickly. That is a technical posture, even if it is not a software engineering posture.

If you do not have this posture, you hit a ceiling quickly. You will build a few impressive-looking demos, automate a couple of tasks, and then plateau. The compounding benefits go to people who can go deeper, and the gap between the two groups is widening every quarter.

The fastest way to build the middle-ground posture, realistically, is to experiment on your personal devices, in your personal time, on your own side projects. Corporate AI deployments are locked down for good reasons, but they give you a misleadingly limited frame of reference. You have to go wider on your own.

Reason two: the personal risk calculus behind corporate caution

This next point is the one I think is most under-discussed in the AI adoption conversation, and it needs to be raised carefully because it applies to a lot of folks.

A lot of what shows up as “we need to be careful about AI” or “let us wait and see” or “compliance will need to look at this” is not purely about risk management. It also reflects the personal risk calculus of the people making the calls. Senior corporate roles often come with asymmetric downside: the personal cost of backing the wrong transformation can be much higher than the personal reward for backing the right one early. Caution in that environment is not irrational. But some of what gets labelled corporate risk management is career-risk management wearing the same clothes.

Executives who are otherwise experienced and capable can find themselves more cautious about sponsoring uncertain work than their formal authority would suggest. When the personal cost of being wrong in public is high and the personal reward for being right early is modest, the rational move is often to wait for consensus.

This matters for AI adoption because substantive transformation requires sponsorship from people willing to advocate for work that might not succeed. That is a harder ask of anyone whose personal circumstances make public failure costly. I do not think this is a character failing. It is a structural feature of how senior corporate careers are constructed, and it is one of the quiet reasons progress tends to stall at visible, defensible activity rather than substantive change. The standard brakes on new work, cost discipline and compliance review, can then provide cover for inaction without anyone having to name what is actually going on.

The honest move is to separate two types of caution. Caution that reflects a considered view of the specific risks to your organisation is strategic, and it deserves respect. Caution that is more about protecting your own position is a different thing. Both are understandable. Only one is actually strategy, and the distinction is worth naming to yourself even if you never name it out loud.

Reason three: what actually blocks AI adoption

The 5C Adoption Friction Model I have been writing about in this newsletter identifies five friction points that block AI adoption inside real organisations. Each one is a question your people are silently asking:

• Clarity: What am I supposed to do differently tomorrow?

• Capability: Do I know how to do it?

• Credibility: Has leadership actually changed how they work, or are they just endorsing?

• Control: Do I have agency over how AI changes my role?

• Consequences: What happens if I simply do not engage?

Until those questions have real answers, the initiative does not change how work gets done, no matter how much is spent on tools and training.

The order matters. If Clarity is missing, training will not help. If Credibility is missing, communications will not help. If people feel no Control, adoption stays performative. If there are no Consequences, the old operating model wins by default. Fix them out of order and you compound the problem. Fix them in order and each one makes the next easier.

The pattern I keep seeing is that leaders diagnose a Capability gap, prescribe training, and grow frustrated when nothing shifts. Wrong problem. Technical leaders default to capability because capability is what their role trains them to see. Communications leaders default to clarity. The actual blocker is usually Credibility, Control, or Consequences, and those are harder to address because they require leaders to examine their own behaviour rather than commission another programme.

When those friction points sit unaddressed, what you get is cosmetic adoption. The dashboards show usage. Training completion ticks up. The leadership team reviews the numbers and approves the next phase. Meanwhile, the actual deliverables look the same as they did six months ago. The organisation has spent real money to produce theatrical return, and worse, is about to scale the next phase on a foundation of compliance rather than change.

A lot of the so-called “AI layoffs” reported in the press fit this picture. When you look closely at what the company has actually done on AI, the answer is often “rolled out Copilot, integrated one SaaS tool, reduced headcount, attributed the change to AI.” Calling that an AI transformation is a stretch. It reads closer to a cost exercise presented through an AI lens.

The pressure that produces cosmetic adoption is real. Leaders are getting analyst questions on every earnings call. Institutional investors come in and ask what the AI strategy is, how costs are going to be cut, how margins are going to improve. Shareholders expect movement. Stakeholders expect direction. The path of least resistance is to produce visible motion without restructuring anything important.

Cosmetic adoption provides exactly enough cover to delay the real work, which is addressing the 5C friction points in the right sequence. Operating model compression, the idea I have written about before, where an organisation becomes systematically faster, better and cheaper because AI agents coordinate work that human middle managers used to coordinate, does not happen by accident. It requires sustained, uncomfortable effort from sponsors willing to restructure their own organisations and, in many cases, their own roles.

Most leaders know they need to move. Very few have worked through the 5C friction points in the right order. And cosmetic activity buys time without closing the distance.

The counterintuitive bit: large firms can win

The consensus narrative in tech is that AI-first startups will eat incumbents in every sector, the way cloud-native startups ate on-premises incumbents in the 2010s. Parts of this will be true. AI-first entrants will take products and services that incumbents thought were locked down, and they will do it faster than most incumbents realise.

But there is a counterintuitive flip side, and I think it is underappreciated.

Some very large companies are already doing an enormous amount of serious work in this space, leveraging the economies of scale and resources they have in ways a startup cannot match. If you are a truly large company and you commit properly to AI transformation, the return on being first in your sector with the best capability, the cleanest data, the most automated workflows and the deepest tooling leverage is much bigger than the equivalent return to a startup. You have more processes to compress, more data to clean, more workflows to automate, and more people to redeploy. The arithmetic is simply different at scale.

The competitive picture over the next few years will look less uniform than the “startups always win” framing suggests. The incumbents that get serious will pull further ahead. The incumbents that stay cosmetic will get eaten. The spread between these two groups inside the same industry will be wider than anything I have seen in my career.

For a concrete example of where this gets interesting, think about Claude’s new design capabilities, which compete directly with tools like Figma and Canva. The strategic question for a large company is not “which tool do we buy.” It is a question about operating model. If AI tools make acceptable first-pass design work available across the organisation, what should the design function actually become? A production queue? A standards body? A brand governance layer? A centre of excellence for high-value creative judgement? The head of design role does not disappear, but what it governs shifts from producing every asset to setting standards, protecting brand coherence, and deciding where human craft is still worth the cost.

These are not small questions. They affect headcount plans, budget allocations, career paths and, ultimately, cost structures. The companies asking these questions seriously right now will have a very different cost base in three years from the ones still debating whether to licence the enterprise version of a particular SaaS tool.

What this means for you, this week

If you are a senior person in a corporate role, there are three moves you can make before Friday.

First, build the technical middle ground. Not a computer science degree, but genuine comfort with the terminal, debugging instincts, and a personal reference point for what these tools can do at the frontier. You cannot build this through corporate deployments alone. You have to experiment on your own devices, on your own time, on your own side projects. This is part of the unpaid permanent part-time job we all now have, alongside staying healthy, exercising and sleeping properly. It is not optional if you want to remain competitive.

Second, run your own cosmetic adoption test. Open the last three deliverables your team produced. Not the dashboards, not the training completion metrics, the actual work. Can you see where AI changed the output, or was it used as an input and then smoothed back into a work product essentially identical to what the team would have produced six months ago? If the work looks the same, the adoption is cosmetic. That is your first honest number.

Third, ask one diagnostic question in your next one-on-one with someone on your team. Not “how is AI going.” Instead: “what would you do with AI if nothing was stopping you.” Whatever they say tells you which of the five friction points is live. A task they do not know how to approach points to Capability. A fear of doing something they are not supposed to do is Clarity or Control. A shrug and “no one really expects much” is Consequences. A comment about what leadership is or is not doing with AI points to Credibility.

Those three moves cost almost nothing and will tell you more about where your organisation actually is than another quarterly adoption report.

I keep developing the full diagnostic toolkit here. Paid subscribers get the 5C Decision Tree, the Compliance Theatre Diagnostic, and the playbooks for each of the five friction points. If you want to move from diagnosing to changing, that is where to go next.

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Do you have a frame of reference for what is possible? Are you close enough to the frontier to make decisions that will still look sensible in twelve months? Most people cannot answer yes to these, and most organisations cannot either. That is the opportunity, and it is also the risk.

Not a bubble. A buildout.

The media’s default frame for AI is “bubble.” I do not think that fits what is actually happening.

The major AI labs are rate-limiting their users rather than cutting prices, which is what you do when demand is outrunning supply. Performance and latency are nowhere near what mature cloud services deliver. The hundreds of billions flowing into the underlying infrastructure each year do not go to an industry in terminal decline.

Bubbles do not look like this. Early-phase infrastructure buildouts do, where demand is real, supply is constrained, and the people paying closest attention are positioning themselves for what comes next.

The question is whether you are going to be one of them.

Best regards,

Brennan

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Start here if you are new to this series: Your AI Training Isn’t Working. Here’s Why.

Enterprise AI adoption stalls in five predictable places. The most expensive of them is not the one most leaders watch for. It is about meetings. Which ones AI reaches, and which ones it does not.

None of these are failures of competence. The default pathways in organisations push toward these exact choices. That is what makes them worth examining: they are structural failures, and structures have authors.

Most leaders are not making all five. But the one that is active is usually costing more than it appears, and the next one in the sequence is often close behind.

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Mistake One: Treating Adoption as a Technology Problem

Leaders invest in tools first and ask why the adoption is stalling later. They buy enterprise AI platforms. They build security architecture. They negotiate vendor agreements. And then they watch the adoption curve flatten after three months.

This happens because they have solved the wrong problem.

The friction in enterprise AI adoption is rarely a technology problem, at least not by the time it shows up. Once the platform has been bought, approved, and deployed, the binding constraint shifts from the model to belief, permission, trust, workflow, and incentives. The blockage has moved. Most leaders have not. People do not understand why they should change. Or they understand it and they do not trust it. Or they trust it and they do not have permission to use it. Or they have permission and they do not know how.

Better diagnostic questions start with the people. Who needs to believe this is valuable? Which conversation needs to happen before the technology matters? What would make a sceptical person in this room less sceptical? The work is there, in conversation rather than in procurement.

You end up with a fully deployed technology that no one actually uses, and the erosion of trust that comes from investing in tools no one wanted.

Mistake Two: Choosing the Wrong Champion

The second mistake follows quickly from the first. You need a champion to drive adoption. That is correct. But most leaders choose the wrong person.

They choose the enthusiast. The person who got excited about AI before anyone else. The person who already uses it in their inbox, who has seen the value, who wants to talk about it constantly. They think: this person will convince everyone else.

What actually happens is the opposite.

The enthusiast raises fear in the room, not interest. Because the enthusiast is not like the rest of the team. The enthusiast absorbs the learning curve more easily, bears less reputational risk in being early, and is not representative of the median employee. The rest of the team sees someone whose skills and confidence are already high and thinks: maybe I need to be someone else to use this.

The person you actually need is the trusted sceptic. The person everyone respects. The person who does their job well without AI and has asked hard questions about why this matters. When that person says, “I tried it. I was wrong about this,” other people listen. When the enthusiast says it, the room assumes the enthusiast is selling something.

The cost of choosing the enthusiast: you alienate the very people whose adoption would prove the technology works. You turn it into a special interest instead of a normal tool.

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Mistakes one and two stall adoption. Three, four, and five teach the organisation to hide that it is stalling.

Paid subscribers get mistakes three, four, and five, the compounding effect, the full Recovery Protocol map, and the attached protocol document with the three specific actions for each mistake. Subscribe.

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Not everyone cares about technology. And that is fine.

I spend most of my day connected to a screen, actively trying to keep up with what is happening in AI, and I still miss things constantly. If that is true for me, you should not feel bad about it. You have a business to run, you have people to manage, you have a life to lead. Nobody should be expected to stay across all the technical detail, and honestly I think the expectation that you should is part of the problem.

What I have come to believe, after leaving corporate life and spending a frankly unreasonable amount of time and money going deep on this subject, is that AI adoption is a change management problem before it is a technology problem. If you want real transformation, you have to start with people. Not with models, not with tooling, not with benchmarks. People.

Once you accept that framing, the resistance you are seeing starts to make sense. The pushback, the scepticism, the blank stares in meetings, the quiet foot-dragging. Most of it comes from fear, and the fear is rational, because nobody in any position of authority is offering a clear and honest story about what this means for ordinary workers and households. How they benefit. How abundance actually reaches them. How their lives get better rather than more precarious. There is no believable transition story on the table. No positive vision being articulated by anyone. None.

That absence is the backdrop to everything I am about to describe. The race between labs, the geopolitical competition, the capital flooding in. All of it is happening at pace, and almost none of it is being translated into language that helps real people understand where they stand. So every piece of news below, I want to filter through a single question: what does this mean for how you lead your people through what is coming?

Leaders who engage now will outpace those who wait

I keep coming back to a simple observation: too many leaders are still treating AI as something to monitor rather than something to use. They read about it, they attend the conference sessions, they nod along in board meetings. But they are not using the tools themselves, and it shows.

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Start here if you are new to this series: Your AI Training Isn’t Working. Here’s Why.

The Quiet Failure Mode Nobody Talks About

Last week’s essay explored why leaders avoid the hard question about their AI initiative: is it actually changing how work gets done? This week asks what happens when they keep avoiding it.

There is a kind of AI initiative that is harder to spot than the ones that fail visibly. It has not been cancelled. It has not been declared a success. It is still running. The conversations have not changed since month two. The pilot has not moved past pilot.

Nothing has visibly broken. Nothing has visibly worked either.

This is the quiet failure mode. Far more common than spectacular failure, and far more costly, because it consumes time and goodwill without showing that anything needs to change.

The pattern is recognisable. Six months of invisible stagnation, followed by a crisis that should have been a conversation three months earlier. The 5C Adoption Friction Model maps the forces that cause this. Stagnation is what happens when those forces sit ignored long enough that the initiative stops generating friction at all.

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The First Three Months

In month one, there was energy. You ran a launch meeting. You explained the why. You announced the first pilots. You took questions. You watched people’s faces. Some excited. Some sceptical. Most paying attention. The conversation was forward-looking.

By month three, the conversations were widening. New roles were asking questions. Integration challenges were surfacing. Real friction was appearing, the kind that comes from people actually trying to use the tools in their work. It was messy, but it was movement.

What Month Six Looks Like

If progress is real, month six looks untidy. New people have entered the conversation. The regular check-ins are asking different questions than they asked in month two. The budget has shifted, because the reality changed. People are finding edge cases nobody anticipated and using the system in ways the designers did not expect.

When stagnation has set in, the opposite is true. In an exploratory programme, too much stability is usually not a sign of control. It is a sign that nothing is moving.

Same people, same questions, same tools, same budget, same answers.

Nothing in the operating reality has forced adaptation. The budget, line for line, matches the original plan, which in exploratory work usually means no one learned anything worth adjusting for. The energy is gone. Not the dramatic kind of failure that forces a decision. The kind that fades quietly until nobody believes it will actually work.

The Three Data Points

These do not require a consultant. They require a calendar, a set of meeting notes, and ten minutes.

Data Point 1: How many new names are in the room? If the same small group is still doing the work in month six, adoption has stagnated. You cannot scale something that is not moving. Check your meeting attendance. Check your channel membership. Count the names that were not there in month one.

Data Point 2: What new friction has shown up? Friction is how you know people are actually using something. No new friction in month six that you did not have in month two means no one is using this enough to find new edge cases. Look at your support tickets, your feedback channels, your integration logs.

Data Point 3: What are your regular check-ins asking? A working initiative asks different questions in month six than in month two. If the conversation has not changed, someone is not learning. Pull last month’s agenda next to the one from month two. Compare.

Check your calendar. Check your notes. Write the numbers down. This is your diagnostic.

The Cost of Waiting

By month three, stagnation is recoverable. The team has not yet burned out. A pivot looks like learning.

By month six, the cost rises. People have invested time and attention. Some have made bets on this direction. A pivot now means admitting those early months did not lead where they should have. The people who believed in the original direction will need a reason to believe in the new one.

By month nine, the cost is very high. The initiative has history. Stakeholders have positions. Some people have been promoted because they “led the AI transformation.” The change is no longer a learning conversation. It is political.

By month twelve, you are in crisis mode. “If we had made this call in month three, it would have been a course correction. If we had made it in month six, it would have required a difficult conversation. In month twelve, it requires an apology.”

If you are between month six and month nine, the decision window is narrowing.

The Decision That Changes the Trajectory

If those three data points confirmed what you suspected, the next question is harder. Push harder? Change the approach? Or acknowledge that six months of effort has not produced the progress it was supposed to?

This is uncomfortable. The sunk cost is real. The stakeholder expectations are real. But the cost of delay is also real. Every week the decision waits, the initiative sinks deeper. Not into success. Into habit. Into expectation. Into the calendar.

You now have the diagnosis. The decision comes next: push forward, pivot, or reset.

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About fourteen months ago, I quit a corporate job. I’d spent over 12 years in financial services working in technology and change, delivering projects across Australia and New Zealand. I had a young family. It was a calculated bet, not a leap of faith.

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This publication, Getting AI To Work, is about why AI initiatives stall inside real organisations, and what to do about it. That focus took me a while to find.

What pushed the decision was something specific. I had been experimenting with AI models in my own time, asking them questions about domains I knew well, areas where I had enough experience to judge the quality of the answer. The responses were so much better than anything a Google search could produce that it shocked me. I needed the space to dedicate real time to learning, experimenting, and building. That was not going to happen inside a corporate role.

So I left. And then I was sitting at a desk that was mine, in a house that was not quiet at all, writing into a newsletter that few at that point read.

The first few months were deliberate exploration. I wrote about vibe coding, about Gemini, about Studio Ghibli, about whatever caught my attention that week. If you go back through the archive, you can watch me think out loud. Some of those posts hold up. Some don’t. That is what exploration looks like when you are doing it in public.

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What I did not expect was how quickly my technical range changed. I’ve always been reasonably technical: some SQL, some Python, comfortable with the command line. But I was not a developer. And there was no pathway to experiment with this technology at work.

Since leaving, I have been building with AI coding tools almost every day. An AML risk assessment readiness tool for Australian businesses. An analytics platform that automates analysis of complex regulatory disclosures for clearinghouses. Several data and analytics tools to support my research.

None of these existed fourteen months ago, and I did not have the technical ability to build them. That direct, practical understanding of what AI tools can do informs everything I write about adoption. The rate of capability improvement still astonishes me.

By mid-2025, I had landed on a set of big frameworks. Operating Model Compression. The Liquid Organisation. The Triple Boundary Framework. I thought that if I could describe the structural forces clearly enough, the right people would find it useful. And many did.

But something was off.

What I got wrong

The frameworks described the world. They did not tell anyone what to do next.

I was writing for leaders who had already decided AI mattered. They did not need another explanation of why the world was changing. They needed a way to figure out why their specific initiative was stuck, and what to do about it on Monday morning.

The technology was moving so fast that the bottleneck had shifted. By mid-2025, the tools were good enough. The models were good enough. The constraint was no longer technical. It was organisational. Bureaucracy, governance theatre, change management failures, the politics of getting people to do something different when they are not sure it is safe to try. That was the real constraint. And my frameworks were not addressing it.

I did not have a single moment of revelation. It accumulated. The posts that got traction were the practical ones. The posts that got silence were the theoretical ones. The readers who reached out were not asking “What does the future look like?” They were asking “My team is not using the tools and I do not know why.”

That question became the 5C Adoption Friction Model, which became the foundation for everything I have written since December.

What this publication is now

If you are new here, particularly if you have come across from Global Custody Pro, here is where things stand.

The core of the publication right now is a diagnostic series on AI adoption friction. Each article names a specific friction point that blocks AI adoption and walks you through how to identify it, why it persists, and what to do about it. But I also write about whatever else I am thinking about, and that will not change.

The underlying framework is the 5C model: Clarity, Capability, Credibility, Control, Consequences. Five friction points. A specific sequence. Fix them out of order and you compound the problems. Fix them in order and each solution makes the next one easier.

For those coming from Global Custody Pro: this is different content. Global Custody Pro focused on AI transformation in custody and post-trade operations. This publication is industry-neutral.

The patterns I write about here show up in financial services, yes, but they also show up in healthcare, in professional services, in government, in any organisation where AI has been introduced but not adopted. The friction is human, not technical. The industry is incidental.

Every article stands on its own, and the foundational article that introduces the full framework is here. Start there if you want the complete picture, or start with whichever title sounds like your problem.

What paid subscribers receive

Each article includes a diagnostic tool for paid subscribers: conversation scripts, audit frameworks, action plans, checklists. They are practical tools designed to be used in a real conversation with a real team, not filed away. The library grows with each article, and paid subscribers have access to the full archive.

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Start here

AI Adoption Is Stalling. More Training Won’t Fix It. introduces the full 5C model. Every article since sits on top of it.

How to Unblock Your AI Roadmap in One Conversation gives you a single conversation to figure out what is blocking your team.

Operating Model Compression: A 2025 AI Year in Review is the bridge between the earlier framework writing and the diagnostic series.

Why Smart Leaders Are Still Hesitant About AI names the patterns behind executive hesitation that most commentary misses.

If you follow the Substack Notes or my other social media channels, you already know I think out loud there regularly. I am going to start doing more of that in the newsletter itself. Alongside the main articles, I will be publishing a shorter weekly notes post: what I am reading, what I am building, observations that do not fit neatly into a diagnostic framework but are worth sharing. The kind of writing that got me started in the first place.

If you have been reading along, thank you. If you are new, welcome.

I would like to hear where your own team is getting stuck right now: Clarity, Capability, Credibility, Control, or Consequences?

Hit reply to this email or send me a DM.

Normal service resumes on Wednesday.

Best regards,

Brennan

The initiative has been running for weeks. Or months. The adoption curves are visible. The dashboards are reviewed. The governance meetings happen. The budgets are approved, the timelines signed off.

And yet the question has not been asked.

Time is not the obstacle. Neither is knowing where to start. Something else is in the way. And that something is worth understanding, because the moment a leader stops measuring and starts asking is usually the moment things begin to shift.

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The Five Patterns of Avoidance

The question does not go unasked because leaders do not care. It goes unasked because something gets in the way. One of these five patterns.

Pattern 1: Fear of Hearing Something You Cannot Fix

This one is recognisable: the calendar is full of governance meetings. Process meetings. Steering committees. Vendor reviews. Project checkpoints.

The calendar is empty of conversations.

The governance meetings are about what needs to happen. The conversations would be about what is happening. There is a difference. And the instinct is to schedule more meetings about the thing instead of asking about the thing.

What this costs: the team sees a leader who measures but does not listen. Over time, they stop offering the real answer.

Pattern 2: Fear of Political Exposure

This one looks like delegating the question.

The conversation gets scheduled with the HR business partner. The transformation consultant does the listening. The programme director runs the one-on-ones about impact. You are not in the room when the team answers.

Understandable, as instincts go. Hearing feedback directly carries vulnerability. But the team notices who asked and who did not.

What this costs: the initiative loses executive credibility. The team assumes the leader does not want the real answer.

Pattern 3: Substituting Metrics for Conversation

The adoption dashboards get reviewed every week. User adoption rates. Feature utilisation. Time to competency. Session frequency. Charts. Trend lines. What never gets asked is: “What is this actually like?” The metrics tell you the what. The conversation would tell you the why. The longer the abstract version substitutes for the real one, the wider the gap grows between the numbers and the team’s experience. Eventually the dashboards stop measuring what matters.

Pattern 4: Outsourcing the Conversation to HR

This one hides inside a process that looks like listening: the survey.

You send a survey. Your teams complete it at their desk. Anonymously. Distance is the point. You get feedback without having to respond in real time. You get data without having to be present. You get information without having to sit in the silence that follows a hard answer.

The survey is the conversation you do not have to have.

What this costs: Response rates drop with every survey. Your teams learn that feedback is filed, not acted on. The words become sanitised. No one tells you what is hard because they have stopped believing it will change anything.

Pattern 5: Waiting for “The Right Time”

The conversation gets scheduled for after the next quarter closes. After the big announcement. After the next milestone. After the change programme matures. After things settle down.

The right time never arrives. There is always something else: another milestone, another phase, another reason the organisation is “too busy” for a real conversation. And every week the question waits, the window hardens into a norm. The team stops expecting the conversation. They stop preparing for it.

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The Cost of One More Week

Each week of avoidance deepens the gap between what the data says and what the team is experiencing.

The adoption numbers may look fine on paper. The feature rollout may be on schedule. The budget approvals and stakeholder alignment may be in place.

What is missing is the truth underneath. The truth that lives in the conversations that are not happening. In the one-on-ones that keep getting postponed.

By next month, that gap will be wider. Teams will have made their own peace with the initiative, whether it works or not, and the distance between the dashboard and what is happening on the ground will have hardened into something no one questions any more.

Every friction point in the 5C model gets worse the longer this conversation does not happen. Clarity fades. Capability gaps go unaddressed. Credibility erodes. People quietly revert to old workflows, or use AI only for low-risk tasks while telling leadership adoption is going fine. Consequences remain absent: there is still no cost to waiting and no reward for moving.

It all starts because one conversation did not happen.

You recognised yourself in at least one of those patterns. The Avoidance-to-Action Playbook below gives you the micro-move for each one: the exact sentence, the exact meeting, and what to do with the silence that follows. Paid subscribers get the Playbook plus every diagnostic from the series.

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Last week explored why your AI sceptics are right about more than you think. This week, the lens turns inward.

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The person stalling adoption approved it

This is the hardest conversation in any transformation programme.

The pattern is recognisable. Budget approved. Tools selected. Training delivered. Three months later, the team is barely using any of it. The leader asks why. The answer is usually in the leader’s own calendar, but nobody will say that out loud.

The leader who commissioned the initiative is often the person whose behaviour is stalling it. Not through malice. Through signals they cannot see. Signals are not the only friction point, but they are the one leaders have the most direct control over, and the one they are least likely to notice.

The gap between what you say matters and what you actually do with your time is visible to everyone around you. That gap costs months.

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Five signals tell your team AI does not matter

Teams are perceptive. They notice what you prioritise, what you ask about, what time you protect, and what you admit uncertainty about. From those observations, they form a view about whether AI adoption is actually important.

Most of these signals are sent without any awareness. That is what makes them worth examining.

The Attendance Signal. Present at the announcement, the budget approval, the launch event. Absent from the actual work. When the initiative was commissioned but the leader does not use the tools, the team reads endorsement without trust.

The Language Signal. In board meetings: “We are implementing AI.” In actual conversations with the team, AI never comes up. Teams hear both registers and draw the obvious conclusion: AI is something you announce, not something you do.

The Meeting Signal. AI is on the agenda for meetings the leader skips. When they attend, they do not ask about adoption. The team reads this simply: the leader does not want to know.

The Capacity Signal. Adoption is a stated priority, but the people assigned to it have no protected time. Their day jobs are unchanged. They are squeezing AI work into the margins. When the calendar remains packed with pre-AI tasks, behaviour says optional while words say essential. The contradiction is visible.

The Uncertainty Signal. No mention of a failure, a question, an experiment that did not work. Teams interpret silence as discomfort. If a leader were using AI tools, they would have something to say about it. The silence is the signal. And it is the hardest one to see in yourself, because the thing you are not doing feels like nothing. It is not nothing. It is a choice your team has already noticed.

Your team will not tell you

This is the uncomfortable part. Your team sees these signals, understands what they mean, and will not tell you about them.

The power dynamic makes honesty risky. You evaluate their performance. You adjust their scope, recommend their promotion, decide where they sit in the resourcing queue.

So they observe, they decide, and adoption stalls.

The strategy did not fail in execution. It failed because the team read the signals as the real priorities.

Five questions expose the gap

These are worth sitting with for a moment.

1. When did you last use an AI tool to do part of your actual work? Not a demo. Not a presentation. Your real job.

2. In your last team meeting, did you ask about someone’s AI experiment or result? If not, what got your curiosity instead?

3. Open your calendar from the last fortnight. Find one block where you used an AI tool to do the work, not just talk about it. If you cannot find one, your team cannot either.

4. Have you shared an AI failure of your own with your team? A tool that did not do what you expected? If not, what do you think your silence communicates?

5. Describe the most recent decision you made using an AI tool. Did anyone on your team see it happen?

Those five questions are a self-score on the five signals. Without an outside view, those scores stay comfortable estimates. The real data comes next: how your team would score you. Paid subscribers get the Leadership Signals Comparator, which pairs your self-assessment with a colleague score, plus every diagnostic from the series.

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Every AI initiative has someone who pushes back. You know who they are. They ask the uncomfortable questions in the rollout meeting. They point out the gaps in the pilot data. They do not attend the optional training session.

The instinct is to overcome the resistance. To bring them around. To wait them out.

But what if they are seeing something the dashboard cannot show you?

If this is your first article in this series, the framework behind it is here. I have been writing about the friction points that block AI adoption, from misdiagnosis to compliance theatre. This week is about the friction point that talks back.

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Three types of resistance. Only one is a problem.

Not all scepticism is the same. Most organisations call it “resistance” and try to manage it away. That is a mistake. It costs you the most valuable data your initiative will produce.

There are three types of resistance, and they require three entirely different responses.

The first is productive scepticism. The person sees a specific risk: implementation gaps, workflow disruption, data quality concerns, the gap between what was promised and what is being delivered. This is not resistance. It is intelligence. The productive sceptic is naming the things nobody else is willing to surface. They are doing the work the dashboard cannot do.

The second is rational opt-out. The person has calculated that AI does not help with their specific work. They may be right. Not every role benefits equally from current AI tools. A financial analyst whose work depends on proprietary data sources may find that AI adds a step without adding value. Call it pragmatism, not resistance. And the organisation that forces adoption on someone whose opt-out is rational creates compliance theatre, the exact pattern I wrote about last week.

The third is destructive inertia. The person is resisting all change, not just AI. They resisted the last system migration. They resisted the process redesign before that. This is the only type that is a problem. And it is rarer than leaders assume.

Most leaders never run the diagnostic. They treat all three as type three because it is the easiest to dismiss. “They are resistant to change.” That label saves the leader from having to listen. It also ensures that productive scepticism and rational opt-out are ignored. The intelligence is lost. The risks go unexamined. And the initiative proceeds on assumptions that have not been tested.

In the 5C framework, this is Credibility friction. The sceptic sees a gap between what was promised and what is delivered. It is also Consequences friction: they are naming what nobody else will say about what happens to roles, to quality, to the work itself. What happens when the tool gets it wrong and they are accountable. And it is Control friction: they were not involved in decisions that affect their work. These are not objections. They are questions the initiative should have answered and has not.

The most valuable conversation is not with the enthusiast. It is with the sceptic. The enthusiast tells you what you want to hear. The sceptic tells you what you need to hear. Are you listening? Or waiting for them to come around?

The cost of not listening

Dismissing a productive sceptic has a double cost. First, you lose the intelligence they carry. The risks they see are real. The gaps they name exist. Ignoring them does not make the risks disappear. It makes the risks invisible until they surface as failures.

Second, you create an adversary. The sceptic who is ignored does not disappear. They become more entrenched. And the team watches how the organisation treats dissent. If the person who raised legitimate concerns was labelled as resistant? Excluded from the conversation? The rest of the team draws a conclusion. The conclusion is: do not be honest. Perform compliance instead. The connection between dismissed scepticism and the compliance theatre I described last week is not a coincidence. It is a mechanism.

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The Sceptic Interview Matrix below gives you the branching conversation for each type of sceptic, with different questions, follow-ups, and next steps. The Risk Mapping Template turns what they tell you into the action plan your initiative is currently missing. Without them, you have a taxonomy of sceptics with no way to act on it. Both are available to paid subscribers, along with the 5C Decision Tree, the Leader Selection Criteria, the Pilot-to-Practice Checklist, the Meeting Rhythm Audit, the Compliance Theatre Diagnostic, and the full 5C playbook archive.

Identifying which type you have

The distinction is simpler than it seems. Three questions will tell you.

Can they name a specific risk? Not a vague complaint. A specific gap, a specific concern, a specific scenario where the initiative is making an assumption it has not tested. If yes, you are looking at productive scepticism. The risk they name is worth more than the dashboard they are ignoring.

Can they explain why AI does not help their specific work? Not “I do not like change.” A specific explanation of what they tried, what happened, and why it did not improve their output. If yes, you are looking at rational opt-out. Their explanation may reveal a genuine limitation that applies to other roles too.

Do they resist all change, not just AI? Think about the last three organisational changes. Were they resistant to all of them? If yes, the pattern is positional, not analytical. This is destructive inertia. But it is rarer than you think. Check before you assume.

Find the sceptic you identified while reading this. Ask them one question: “What risk are we ignoring?” Not “What is your concern?” The framing matters. “Concern” sounds like a feeling to be managed. “Risk” sounds like intelligence to be captured. Ask the question. Do not defend. Write down what they say.

You know which type of sceptic you are dealing with. The Sceptic Interview Matrix gives you the branching conversation for each type. The Risk Mapping Template turns what they tell you into an action plan. Both tools plus six weeks of diagnostics and the full 5C playbook archive are waiting for paid subscribers.

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Your team completed the AI training. They logged into the platform. Your dashboard shows adoption trending upward. Training completion: 94%. Weekly active users: growing. Reported usage: on track.

Now open the last three deliverables your team produced. The presentations. The analyses. The client work. Can you see where AI changed the output? Not where it was used as an input. Where the output is actually different.

If this is your first article in this series, the framework behind it is here. I have been writing about the friction points that block AI adoption and why leaders consistently start with the wrong one. This week is about the friction point that hides behind your best metrics.

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The adoption pattern that looks like success

There is a pattern that appears in most mandated technology changes. The team complies. They attend the training. They log in. They report usage. Everything the organisation measures looks positive. But if you examine the actual work, nothing has changed. The deliverables are produced the same way. The decisions are made with the same data. The workflows are identical to six months ago.

This is compliance theatre. And it is the most dangerous adoption failure because it produces the exact data leaders want to see.

Compliance theatre is not laziness. It is not resistance. It is a rational response to a specific set of conditions. When the organisation measures adoption by activity, the team produces activity. When nobody has addressed what AI integration actually means for the work, the team finds the lowest-effort way to satisfy the metric. They log in. They run a query. They paste something into a prompt. Then they do the work the way they have always done it.

Three conditions produce compliance theatre reliably.

Adoption metrics that track activity rather than integration. Logins, training completions, reported usage. Easy to measure. Easy to perform.

No space for the team to negotiate how AI fits their actual work. When adoption is mandated without input, compliance is the rational response.

Unnamed consequences. Nobody has addressed what AI means for roles, performance expectations, and workflows. Genuine adoption feels risky. Performing adoption feels safe.

In the 5C framework, compliance theatre sits at the overlap of three friction points. Capability: the team lacks access, time, or support to learn how AI fits their actual workflows, so they substitute visible activity. Control: nobody has addressed what AI means for roles, job security, or how decisions will change, so they comply on the surface. Consequences: there is no cost to waiting and no reward for genuinely adopting, so performing adoption is the rational choice.

Compliance theatre is the hardest failure mode to diagnose. It produces the metrics leaders want to see. The dashboard trends upward. The training is complete. The team reports usage. But walk through the actual work products. Open the deliverables. Check the data sources. If the work is being produced the same way it was six months ago, the adoption is cosmetic. The metrics are measuring performance, not change.

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The cost you cannot see on a dashboard

The cost is double. First, the organisation invests in tools, training, and infrastructure for AI adoption that produces no return. The work is unchanged. The investment is real. The return is theatrical.

Second, and worse, the organisation concludes that adoption is succeeding. Decisions about scaling, additional investment, and strategy are made on data that reflects performance, not reality. The leadership team reviews a dashboard that says 87% adoption and approves the next phase. The next phase is built on a foundation of compliance. It will produce the same result at greater scale and greater cost.

The organisation is making real decisions based on theatrical data. It does not appear on any dashboard.

If you suspect your adoption metrics are measuring activity rather than integration, that gap is what the AI Change Leadership Intensive is designed to surface. $500, 90 minutes, and you will leave knowing which of your metrics are tracking real change and which are tracking performance. The diagnostic questions below will get you started. The Intensive tells you what to do with the answers. The Intensive includes a full refund guarantee if you do not leave with at least one insight you can act on this week.

Two questions you can answer this week

The diagnostic is simpler than you expect. It does not require a survey. It does not require a new dashboard. It requires looking at the work.

Pick three deliverables your team produced this month. Open them. Can you identify where AI changed the output? Not where AI was used as an input. Not where someone fed data into a tool. Where the output is different because of AI. If the presentations look the same, the analyses follow the same structure, and the client work uses the same sources, the adoption is cosmetic. The team is using AI as an addition to the workflow, not an integration into it.

Ask a team member to walk you through their process for a recent task. At what point did they use AI? What would they have done differently without it? If the answer is “not much,” the AI step is decorative. It exists to satisfy the metric. Remove it and the workflow is unchanged. That is compliance theatre in its purest form: a step that exists for the measurement system, not for the work.

These two questions cannot be answered with words. They require looking at real outputs and walking through real processes. A team that is adopting AI will produce deliverables that look different. A team that is performing compliance will produce deliverables that look the same with an AI step bolted on.

You have looked at the deliverables. You have walked through the workflow. But there is a third question. It is the one that asks whether your own behaviour is producing the compliance you just diagnosed. That question, the Compliance Theatre Diagnostic, and the Accountability Design Template are below, along with every diagnostic tool from the series so far.

You have seen the pattern in your team. The third question below is the one that asks whether you are producing it. The Compliance Theatre Diagnostic takes ten minutes in your next team meeting. The Accountability Design Template gives you four specific replacement metrics your team cannot perform their way through. Paid subscribers also get the full archive: the 5C Diagnostic Decision Tree, Leader Selection Criteria and Redirect Script, Pilot-to-Practice Transition Checklist, Meeting Rhythm Audit with manager scripts for all three meeting types, five playbooks across every friction point, and every tool from every future week. That is a complete diagnostic suite for $19 per month or $199 for the year. Most readers expense it as professional development. The pattern changes when the structure changes.

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Think about your meetings this week. The Monday standup. The project check-in. The one-on-one. How many of them mentioned AI? Not a dedicated AI session. A regular meeting. A passing reference. A question. Anything.

If the answer is zero, your team already knows where AI sits on the priority list. They did not need a memo. They inferred it from what survived contact with Monday. Silence was the message. And silence, in a team that watches everything the manager does, is louder than any strategy deck.

I have been writing about the friction points that block AI adoption and why leaders consistently start with the wrong one. This week is about the friction point you cannot see because it is not there. AI is absent from the conversations where adoption is actually decided, and that absence is doing more damage than most leaders realise, because it looks like nothing.

When I say “AI” here, I mean the practical stuff: the drafting, analysis, synthesis, and automation that either shows up in your Monday conversation or does not. Not a separate innovation programme. The work.

The signal you are sending every Monday

Teams take their cues from what the manager talks about in routine conversations. Not from the strategy presentation. Not from the training schedule. From what comes up on Monday.

A new initiative is announced. There is a launch. A training programme. An executive sponsor. The team hears the message: this matters. But that message has a half-life, and it decays with every routine meeting where the initiative is not mentioned.

In many technology change programmes I have worked on, the pattern is recognisable. Week one after launch, people mention the goal in meetings. Week two, fewer mentions. Week four, silence. Week eight, it is as if the initiative never happened. The half-life of initiative momentum, measured in meeting cycles. The meeting rhythm is the clock. And the clock runs whether the manager notices or not.

In my experience, the Monday meeting is where adoption lives or dies, not the quarterly review or the training session. The moment a manager does not mention AI in a routine meeting, the team receives a signal: this does not matter here. That signal compounds weekly.

This is not resistance from the team. It is local optimisation. They have limited bandwidth. They will focus on the things the manager asks about. If the manager asks about client deliverables, compliance deadlines, and project milestones but does not ask about AI, the team will prioritise client deliverables, compliance deadlines, and project milestones. The system is working as designed.

Two meetings to audit right now

You can test this in five minutes.

Take two meetings from the past week: a team standup or planning meeting and a project review. For each, answer two questions.

Was AI mentioned at all? Not a dedicated AI discussion. A mention. A reference. A question. Anything. If AI was not mentioned, the meeting told them what they needed to know.

Did anyone on the team raise an AI-related question, concern, or observation? If not, the meeting did not provide space for it. The team may have concerns but no forum to voice them.

If AI was absent from both meeting types, the meeting rhythm is actively working against adoption. Not passively. Actively.

If AI appeared in one but not the other, the coverage is inconsistent. The team gets a mixed message: AI matters when the manager remembers to mention it. That inconsistency reads as low priority.

The results will tell you more about your adoption trajectory than any dashboard. The dashboard measures what people did with the tools. The audit measures whether the organisational environment gives them a reason to.

Why the silence compounds

In my 5C Adoption Friction Model, adoption stalls when teams do not trust the priority (Credibility), do not feel agency (Control), or cannot see incentives (Consequences). The absence from routine meetings creates all three at once. The leader’s behaviour does not match the stated priority. The team has no forum to discuss AI on their terms. And nobody is addressing what happens if they move or if they wait.

The cost compounds in a specific way. Silence kills peer sharing, which kills experimentation, which kills the small wins that would generate more mentions. The loop closes on itself. And each rotation does not just add a week to the adoption timeline. It deepens the team’s conviction that AI is not a real priority.

After a month of that, reintroducing AI feels like a restart, not a continuation. The launch momentum is spent. And the first sign of trouble will not arrive as a question in a standup. It will arrive as a stalled metric in a quarterly review, months after the problem started.

This is another form of the misdiagnosis pattern. The leader sees adoption stalling and diagnoses a Capability gap. More training. A better tool. But the actual friction is structural. The meeting rhythm does not accommodate AI.

If you are not sure what signals your meetings are sending, that uncertainty is what the AI Change Leadership Intensive is designed for. $500, 90 minutes, and you will leave knowing which of your routines are driving adoption and which are quietly killing it. The Intensive includes a full refund guarantee if you do not leave with at least one actionable insight.

You have audited two meeting types. But there is a third conversation where your team would actually tell you the truth about AI. The one-on-one. That is where people say the things they will not say in a group: that they do not understand the tools, that AI is making their work slower, that they are worried about what this means for their role. The audit for the one-on-one, and the manager scripts for all three meeting types, are below.

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You are right to be pleased. You are wrong to be confident.

I have been writing about the friction points that block AI adoption and why leaders almost always start with the wrong one. Last week was about the wrong person leading the initiative. This week is about what happens after the good news.

The pilot succeeded. Of course it did. It was designed to.

Why pilots succeed

Think about how your pilot was set up. Every condition was designed to produce a positive result. And every one of those conditions will disappear when you try to scale.

I have seen this pattern in every technology change programme I have worked on. The pilot is designed to succeed. It has resources, attention, and a team that was selected because they were willing. Scale has none of those advantages. And nobody plans for that gap.

The pilot proved something real. It proved that the technology works, that people can learn it, that under the right conditions AI produces results. In the language of the 5C Adoption Friction Model, that is Capability. One friction point out of five.

What about the other four? Whether the broader organisation trusts the people behind this initiative. Whether people feel they have agency in how AI changes their work. What happens to roles and career paths when AI is no longer a pilot but a permanent fixture. The pilot team never needed to worry about any of that. They were willing. They had support. They were protected from the questions that scale will force into the open.

And here is what makes this pattern so persistent. The pilot’s success does not just leave those questions unanswered. It makes them harder to ask.

“But it worked.”

That is what you hear. Every time. Someone raises a concern about trust, or control, or what happens to their role, and they get pointed at the dashboard. The metrics. The board presentation. Their question does not get answered. It gets overruled by numbers from a group of people who look nothing like the people you are about to roll this out to.

This is the same misdiagnosis pattern from Week 1, wearing a different face. The leader looks at the pilot and sees a Capability success. They assume the remaining friction will resolve itself. It will not. It never does. And the pilot’s success is the reason nobody notices until the scale attempt has already stalled.

The cost you are not calculating

The cost of a failed scale attempt after a successful pilot is not the money. It is the precedent.

“We tried AI. It did not stick.”

That sentence, spoken once in a leadership meeting, can set adoption back by a year. It becomes the organisational narrative. It is the sentence your sceptics will quote for the next three budget cycles. It is the sentence your middle managers will use, quietly, to justify not prioritising AI in their teams.

A failed scale attempt after a successful pilot is worse than no pilot at all.

Without the pilot, the organisation has no opinion. After a failed scale, the organisation has a conclusion: we gave it a fair go, and it did not work. The reality is different. You gave the pilot a fair go. You never planned the transition. But the nuance will not survive the next quarterly review.

The pilot’s success becomes the alibi for the scale’s failure.

If your pilot succeeded and you are not sure whether that success will transfer, that gap is exactly what the AI Change Leadership Intensive is built for. $500, 90 minutes, and you will leave knowing whether your pilot data supports scale or masks the friction that scale will expose. If you do not leave with at least one actionable insight, I will refund you in full.

Two questions before you scale

You have the pilot results. The pressure to scale is real. Before you commit, start here.

Was your pilot team self-selected or representative? If the team volunteered, or if they were hand-picked for their enthusiasm, their success tells you about willing adopters. It tells you nothing about your organisation. Scale requires the people who did not volunteer. The people who are busy, sceptical, or simply focused on other priorities. If your pilot did not include them, it did not test what scale will encounter.

Did your pilot have dedicated resources that will not exist at scale? Protected time. A dedicated budget. Executive air cover. A project manager whose only job was making it work. Ask yourself which of those will exist when you roll out to three hundred people across four departments. If the answer is fewer than you had, the conditions that produced your pilot’s success no longer apply. And you are planning as though they do.

There is a third question. It is the most important one, and it is the one most leaders have never asked about their pilot. It is in the paid section below, along with the three gaps between your pilot and your scale, and the Pilot-to-Practice Transition Checklist that tells you what order to close them in.

Your pilot succeeded under conditions that no longer exist. Did you plan for that, or did you assume the conditions would follow?

You know the pilot does not predict scale. But you do not yet know which gap will stall your rollout first. Below: the third diagnostic question, the three gaps between your pilot and your scale, and the Pilot-to-Practice Transition Checklist. This is your third new diagnostic tool. Combined with the Decision Tree, the Leader Selection Criteria, and the five 5C playbooks already in the library, you now have a complete diagnostic and action toolkit for your first quarter.

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You were grateful. You should not have been.

Last week I wrote about the five friction points that block AI adoption and why leaders almost always diagnose the wrong one first. This week is about the person you appointed to fix it.

Every organisation has one. The enthusiast. The champion. The person who puts their hand up before anyone else and says, “I will drive this.” They are visible. They are energetic. And in most of the transformation programmes I have worked on, they are the person the rest of the team quietly resents.

That resentment is the signal most leaders miss.

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The gap between enthusiasm and influence

Here is the problem. Enthusiasm is visible. Influence is structural. They are different qualities. They do not correlate the way you think they do.

When a leader looks for someone to drive AI adoption, they reach for the person who is already moving. It makes sense. You have a team of people who are cautious, sceptical, or simply busy, and here is someone who is keen. Of course you give them the role.

But willingness to lead is not the same as ability to lead. The enthusiast volunteered because they are excited about the technology. The team needs to be led by someone they trust. Those are different things.

The enthusiastic champion often has lower peer credibility precisely because of their enthusiasm. The rest of the team watches someone who seems more interested in AI than in the actual work. They see someone who has opinions about everyone’s workflow but has not demonstrated that they understand the pressures, the constraints, the reality of what it takes to get through a Tuesday.

And so the team does what teams always do when they do not trust the messenger. They go through the motions. They attend the training. They activate the tools. They tick the boxes. And nothing changes.

The champion reports progress. Tool activation is up. Training completion is strong. The dashboard looks healthy.

The leader sees momentum. The team sees theatre.

Every transformation programme I have worked on has had this person. The volunteer. The enthusiast. And in most cases, the person the rest of the team quietly resents. Enthusiasm without influence does not drive adoption. It drives compliance theatre.

This is the same misdiagnosis pattern I described last week, wearing a different face. The leader looks at the stall and sees a Capability gap: the team needs more training, more support, more resources. But the actual friction is Credibility. The team does not distrust AI. They distrust the person representing it. And no amount of training will fix a trust problem.

The cost compounds. Every month the wrong champion is in place, the team’s association between “AI adoption” and “that person” deepens. By the time the leader recognises the problem, AI adoption has become culturally linked to the champion’s reputation. Replacing the champion does not reset the association. It just removes the most visible symptom.

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If you are not sure whether your champion is helping or hurting, that uncertainty is the signal. The AI Change Leadership Intensive ($500, 90 minutes) gives you an outside perspective. If you do not leave with at least one actionable insight, I will refund you in full.

Three questions that reveal the answer

You probably have someone in mind already. Here is how to move from instinct to diagnosis.

Does this person have standing independent of the initiative? If you removed AI adoption from their role tomorrow, would their colleagues still describe them as influential? If the answer is no, their influence is positional. It comes from the role, not from them. And positional influence does not transfer.

Does the team bring problems to this person, or does this person bring solutions to the team? This is the direction of trust. Champions who push solutions without being asked are experienced as salespeople, not colleagues. The people who drive genuine adoption are the ones others already go to when something is not working.

Does this person represent the team’s concerns accurately to leadership, or do they filter out resistance to maintain momentum? A champion who reports only progress is not a bridge between leadership and the team. They are a wall.

If you completed the 5C Diagnostic Decision Tree from last week, these three questions sit in the Credibility branch. This is where that branch leads in practice.

If the answer to any of these questions is unfavourable, the champion is not the problem. Your selection process is the problem. You optimised for enthusiasm. You should have optimised for trust.

Think about the person you chose. You gave them the role because they were keen. Did you check whether the team trusts them?

Below: the AI Adoption Leader Selection Criteria and a conversation script for redirecting an enthusiastic champion without demoralising them. Use the criteria in your next planning session.

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The problem was never the one you diagnosed. It was the one your role trained you not to notice. And the pattern that led you there is so consistent, and so costly, that it deserves a name: misdiagnosis by default.

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When an AI adoption initiative stalls, the instinct is to diagnose the problem quickly and act. That instinct is sound. The problem is where the diagnosis lands.

Technical leaders tend to see capability gaps. Their teams do not know how to use the tools, so the response is training, upskilling, centres of excellence. Communications leaders tend to see clarity gaps. The message has not landed, so the response is more internal comms, town halls, updated intranet pages. People leaders tend to see control gaps. The team feels unsettled, so the response is listening sessions, wellbeing check-ins, culture initiatives. Finance leaders tend to see consequence gaps. There is no incentive structure, so the response is KPIs, accountability frameworks, performance metrics tied to adoption.

Each of these diagnoses may be accurate. But in most cases, the leader has not diagnosed the actual problem. They have diagnosed the problem that matches their expertise. They have defaulted, not diagnosed.

This is not a criticism. It is how human cognition works. When you have spent a career developing a particular lens, that lens becomes the first one you reach for. A technical leader does not see credibility problems because credibility is not their domain. A communications leader does not see control problems because control sits outside their function. The diagnosis is shaped by the diagnoser, not the situation.

I wrote about the five friction points that block AI adoption in the article that introduced the 5C Adoption Friction Model: Clarity, Capability, Credibility, Control, and Consequences. Each is a genuine category of friction. The model is not the problem. The problem is which one you start with.

In twelve years of delivering transformation projects across financial services, I have watched this pattern repeat more times than I can count. A leader identifies the friction point that matches their skillset and starts there. It is not carelessness. It is human nature. And it is the most expensive mistake in AI adoption.

The cost is not limited to the wasted investment. When a leader spends six months addressing the wrong friction point, they consume the organisation’s patience. The team participated in training that did not solve their actual concern. They attended town halls that did not address what was really holding them back. By the time the real friction point is identified, the organisation has developed a tolerance for initiative fatigue. The next intervention, even if correctly targeted, faces a harder audience.

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Diagnosing the primary friction point from inside is like trying to see your own blind spot. The AI Change Leadership Intensive ($500, 90 minutes) gives you an outside perspective: your primary friction point, and the moves most likely to shift adoption. If you do not leave with at least one actionable insight, I will refund you in full.

The question, then, is how to diagnose without defaulting. Because the default is not random. It is predictable. And if you can see the pattern, you can interrupt it.

There is a triage process that narrows the field. It requires two questions, not five. It will not give you a definitive answer in isolation, but it will eliminate the wrong starting points. And eliminating the wrong starting points is more than half the work.

The first question separates the knowledge problems from the trust problems. Is the friction that people do not know what to do, or that they do not know how to do it? If the answer is “what to do,” you are in Clarity territory. If “how to do it,” you are in Capability territory. If the answer feels like “both,” use a forcing function: if you had to bet your next quarter’s budget on one, which would it be? That bet reveals which friction you actually believe is primary. If the answer is neither, if your team knows what to do and broadly knows how to do it, then the friction sits elsewhere.

The second question separates the belief problems from the safety problems. Does your team trust the direction? Do they believe leadership is committed to this, that it will not quietly disappear in three months? If the answer is uncertain, you are in Credibility territory. Do they feel threatened by the change, seeing AI as a risk to their role, their autonomy, or their standing? If so, you are in Control territory.

These two questions will not resolve the diagnosis entirely. But they will narrow your field from five possibilities to two. That is a significant reduction in wasted effort, and it changes the quality of every conversation you have from this point forward.

If neither knowledge nor trust nor safety explains the stall, the remaining possibility is Consequences. There is no cost to waiting and no reward for moving. The rational response, in that environment, is to do nothing.

Think about your current initiative. Which friction point did you diagnose first? Now ask yourself: did you diagnose it, or did you default to it?

Below: the 5C Diagnostic Decision Tree. Walk your leadership team through it in your next meeting and you will know which friction point is primary before you leave the room. Paid subscribers also have access to five playbooks, conversation scripts, and audit templates across all five friction points.

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