Author: Wayne Loke

  • What I Left Off my CV

    I asked an AI if it could replace me. The honest answer was sitting in the two jobs I deleted.

    Back in 2024 I updated my CV for a role at Open Polytechnic, and I left two things out. ANZ. ABN AMRO. Two banks, real years, real work, missing from the page.

    I wasn’t job-hunting for sport. I was at Te Pūkenga while the whole thing was being unwound, and a good slice of the tertiary education sector was reapplying for its own jobs at the same time. The CV had to do real work, against a crowd, in a hurry.

    I’m not ashamed of them. They just didn’t fit the story I was telling that one reader. A CV is a signal. You’re pointing at one person who has to decide something about you with very little to go on, and you choose what they get to see.

    That’s the whole game. Most people never name it.

    So when I sat down a few weeks ago and asked an AI the question half my generation is quietly asking, “Can you replace me,” I already suspected the interesting answer wasn’t going to be about what I know.

    The part I’ll concede first

    Let’s be honest about where the machine wins, because pretending otherwise is how you lose the argument before you start.

    AI is better than me at most of what I used to get paid for, and faster. Drafting a strategy paper, producing three architecture options with the trade-offs laid out, writing the policy, building the dashboard, generating the risk register. These were once high-value cognitive outputs. They were what a senior person produced to prove a senior person was in the room. They are now close to free.

    And here’s the uncomfortable bit. The knowledge underneath all that was never mine to begin with. It’s out there. It’s in the books, the case studies, the frameworks, the same material anyone could find if they read more and researched harder. Gladwell’s 10,000 hours, the tipping point, Lean, Agile, service management, the whole canon I spent decades absorbing. I didn’t invent it. I accumulated it. And accumulation is exactly the thing a model does better than a human.

    So if my value were the knowledge, I’d already be replaced. I think a lot of experienced people sense this and don’t say it out loud, because the alternative feels like admitting the years didn’t count.

    They counted. Just not for the reason most people think.

    What the machine can’t see

    Here’s what I kept coming back to. The AI doesn’t know me. It knows what I’ve published, what’s documented, what’s in the public record. It doesn’t know the things that aren’t written down anywhere: my taste, what I’d refuse to do, how I read a room, why I left two banks off a CV.

    I studied economics at LSE and forgot most of it, but two ideas stuck, and they turn out to be the whole story.

    The first is asymmetric information. The seller knows things the buyer can’t see. George Akerlof wrote about the used-car market, where buyers can’t tell a good car from a lemon, so they assume the average, so they only pay the average price, so the good cars leave the market and you’re left trading lemons. When the buyer can’t tell quality apart, quality stops being rewarded.

    The second is signalling. Michael Spence’s idea, same Nobel as Akerlof. When you can’t show your real quality directly, you send a signal that’s expensive enough that a weaker player couldn’t fake it. The degree, the track record, the reputation you’d lose if you lied. The signal works precisely because it costs something to produce.

    A CV is a signal. My About Me notes, the ones I feed to an AI so it writes in my voice instead of a generic one, those are a signal too. They’re me trying to make the undocumented legible. My taste, my values, the patterns I trust, written down so the machine can see what the public record can’t.

    For about a week I thought that was the answer. Document the judgment, encode the taste, and the part of me that’s scarce becomes portable. Then I noticed the problem.

    The signal gets cheap the moment everyone sends it

    A signal only holds its value while it stays expensive to fake. That’s the entire mechanism. The day it becomes cheap, it stops separating the strong from the weak, and the market does to it exactly what it did to Akerlof’s used cars. It discounts everyone back to the average.

    A documented “here is my judgment” file is cheap. I could generate a plausible one for a stranger in a minute, full of values they’ve never lived and lessons they never paid for. So could you. So when everyone has an AI producing polished strategic thinking, polished thinking stops proving anything. The mid-level manager now sounds strategic. The junior consultant now produces partner-grade decks. The signal floods, and a flooded signal is noise.

    This is the part the AI’s own answer to me missed completely. It told me to “evolve from delivering outputs to architecting judgement systems,” to become a fractional this, an AI-era that. Reasonable, fluent, and indistinguishable from what it would tell anyone with a similar CV. A lemon. Confident, generic, average.

    The drift toward the mean

    I’ve watched this happen with my own hands, in a small and stupid way that taught me more than the economics did.

    I draw a comic strip. Bald characters, no mouths, just eyes and a nose, dry organisational humour. I trained Gemini on a few years of my strips and asked what it needed to draw in my style. After some back and forth it rendered a panel that looked exactly like mine. Then the next one had mouths. And collars. And shirt buttons, on a character that wears nothing but eyes and a nose.

    Gemini drifts. Left alone, it slides back toward its training data, toward the average idea of what a character should look like, and I have to reload my source strips and re-edit the prompt to drag it back. Five or six rounds for a strip I’m willing to publish.

    That’s the mechanism, sitting right there in a cartoon. The machine reverts to the mean unless something private and specific keeps pulling it off course. Feed it your broken process and you get a faster broken process. Feed it the public average and it gives you back a more confident average. The tool doesn’t audit what you hand it. It amplifies it, at a speed that makes the sameness harder to notice.

    So the documented signal doesn’t save you, because the documented signal is what the machine is built to average. The thing that survives is the source material, the actual strip, the actual record of what you really did, the part you keep having to reassert because the average is always pulling the other way.

    The only signal left standing

    Strip it back and one signal is still expensive to fake. The track record of real choices, made under real consequence, that you would defend out loud in a room full of people who were there.

    Look at the choices. Lifting staff engagement 64% at Stats NZ while the organisation was mid-transformation and exhausted. Taking Priority 1 incidents at Inland Revenue from 33 in one year to 3 two years later. Pulling 20% out of platform costs at Te Pūkenga while the whole institution was being disestablished underneath me. Those aren’t artefacts a model can generate. They’re decisions I owned, with my name on them, where being wrong would have cost me something real.

    And the omission belongs in that list too. Leaving ANZ and ABN AMRO off the CV was a judgment, made for a specific reader, with reasons I could explain and defend. The AI can write the CV. It cannot make the call about what to leave out, because that call needs private context it doesn’t have and a consequence it will never carry. The edited CV is the proof of the whole argument. The judgment is in the deletion, and the deletion is the part that isn’t documented anywhere.

    So what’s left to learn

    If the process work and the first-pass decisions go to the machine, and the polished output stops signalling competence, what’s actually worth learning?

    More knowledge won’t do it. The machine wins that race before you’ve laced your shoes.

    Two things, I think, and neither of them is new, they’ve just become the whole job rather than the senior part of it.

    The first is deciding what’s worth deciding. Framing the real question out of a mess of people, politics, money and history, before anyone produces a single artefact. The machine answers questions beautifully. It has no idea which question matters, and it never carries the cost of getting that wrong.

    The second is signal literacy, in both directions. Reading which signals are still real when the cheap ones are everywhere, and being able to produce one that holds. In a market drowning in fluent, confident, average output, the scarce skill is telling the lemon from the car, and not becoming a lemon yourself.

    The years didn’t buy me knowledge the machine can now match in seconds. They bought me the taste to know which ten minutes of a problem actually matter, and the scars to prove I once paid for getting it wrong.

    The machine has all my knowledge and none of my consequences.

    That’s the gap. I’m not worried about it closing.

  • What AI Exposes – Part 2 of 6

    AI inherits the operating system of the organisation.

    When I joined Stats New Zealand in October 2016, the technology staff were quick to set me straight. “Wayne, you don’t have to teach us Agile. We really know what Agile is.”

    They weren’t wrong, just years out of date about it. A senior leader had run a serious programme to introduce Scrum across the technology function sometime before March 2014, before the CDO I’d eventually report to had even started in the role. Staff were trained. They were immersed. They had the cadence, the ceremonies, the story points, the full picture. Everyone was involved, not just a small team running an experiment off to the side, but the whole technology function, committed. By any reasonable measure, the organisation had done Agile properly, at the time.

    I knew something about that period myself, from the other side of Wellington. I was at Inland Revenue from January 2013 to October 2015, and there was an active Agile community of practice running across the public and private sectors through those years, people moving between organisations, comparing notes, working out what Agile actually looked like inside companies or government work rather than in a textbook. Stats NZ’s programme would have been part of that same wave.

    And then the senior leader left.

    And Agile left with him.

    By the time I arrived, two and a half years later, the practice the staff were so proud to tell me they knew had already been gone for years. I asked, genuinely curious: how come you’re not doing Agile now? The room went quiet in the way rooms do when people know the answer but don’t want to give it. When it came, the answer was simple: leadership stopped it. The leaders above them had ended the practice, and so the practice ended.

    I found that puzzling, honestly. If the team believed in Agile, if they had the knowledge and the experience to run it well, why did they need a senior leader’s permission to continue, more than two years after that leader was gone? Why didn’t they fight for it? The answer to that question tells you more about an organisation than any strategy document ever will.

    Now contrast that with BNZ.

    BNZ Digital operated as its own self-contained unit, deliberately isolated from the traditional technology development cycles running on waterfall. They had their own product managers, their own designers, their own development team, their own mobile development team, their own leadership, their own funding allocation. They were inside BNZ but running a completely different operating rhythm, and their interaction with the broader technology function was structured around getting changes into production without the traditional organisation absorbing them back into its own slower pace.

    They weren’t waiting for the rest of the organisation to be ready. They built a perimeter and worked inside it.

    The result was visible enough that when the broader BNZ Technology function restructured, it adopted the BNZ Digital model. No mandate from above, no big transformation programme, no consultants brought in to explain why digital delivery mattered. The results made the case, and the wider organisation accepted the approach as the way forward.

    Two organisations. The same method. Completely different outcomes.

    The difference sat underneath the method, in something I’ve come to think of as the immunity system. Both organisations understood Agile, the people at Stats NZ knew it well enough to tell me I didn’t need to teach them, so knowledge was never what separated them.

    Every organisation has one. It operates independently of the official structures, the stated values, the executive messaging, the strategy deck that gets presented at the all-hands. It’s the collection of behavioural patterns, power structures, unspoken rules, historical grievances, and self-interest calculations that determine what the organisation actually accepts and what it quietly rejects. The immunity system does not announce itself. It appears in small decisions. In which proposals get funded and which get delayed until they die of inaction. In who gets consulted and who doesn’t. In which meetings matter and which ones produce nothing except a follow-up meeting.

    At Stats New Zealand, I heard a phrase more than once: “If it’s not invented here, we don’t use it.”

    Stats is a deeply technical organisation. Smart people, rigorous people, people who deal in mathematics, statistics, census methodology, where precision matters and intellectual credibility is the currency. That culture produces excellent analytical work. It also produces a particular kind of resistance to external ideas, because if the idea didn’t originate inside the organisation, it carries a faint suspicion, a sense that it might not be suited to the specific complexity of the work, or that adopting it would imply the organisation hadn’t already solved the problem itself.

    The immunity system at Stats NZ was protecting something real: the organisation’s sense of its own expertise and identity. The logic is coherent from the inside. The cost is that good ideas, including Agile, get treated as foreign objects and rejected when the circumstances that introduced them change. When the senior leader left, the immunity system reasserted itself, and Agile, which had been tolerated because of that leader’s authority, went with him. The staff didn’t fight for it because the immunity system had never fully accepted it. They knew how to do Agile. They had just never decided it was theirs.

    BNZ Digital made a different calculation. When the restructure came, the wider technology function could see clearly enough that the BNZ Digital model was something the organisation needed for its own survival. The immunity system accepted rather than rejected, because the case for acceptance was strong enough, and because the threat was positioned as a competitor in the market rather than a threat to the people inside the building.

    This is the deeper mechanism, and I think it leads back to people’s own self-interest. If something new is introduced and it affects the livelihood and power structure of people in an organisation, there will be resistance. The polite word is resistance to change. The more accurate word, and I’ll say it plainly because I’ve watched it happen, is sabotage. It’s rarely dramatic. It’s the report that doesn’t get filed, the meeting that gets rescheduled until the momentum is gone, the budget approval that keeps getting pushed to the next quarter. It happens in organisations more often than leadership is willing to acknowledge, and it almost never shows up in the post-implementation review.

    Now consider what AI introduces into this same system.

    AI redistributes cognitive work. It compresses tasks that previously required specific expertise. It changes who can produce what and at what speed. An analyst who spent years building the ability to synthesise large volumes of information is looking at a tool that can do a version of that in minutes. A manager whose authority came partly from controlling access to information now operates in an environment where information is far less scarce. These are real shifts, and the immunity system in most organisations will register them as threats to livelihood and power, because that is exactly what they are.

    Only this time, the disruption shows up on an invoice as well.

    This is already playing out, and it’s more underreported than it should be. Uber reportedly exhausted its entire annual AI coding budget by April 2026, because adoption was higher than expected and nobody had modelled what happens when engineers start using agentic workflows at scale, where a single task can trigger multiple model calls, repeated context loads, verification loops, and retries. The budget assumption was built on a SaaS licensing model. AI behaves more like cloud infrastructure: usage-based, scaling with adoption, and expensive in ways that only become clear after the bill arrives.1

    A KPMG survey found that only a minority of organisations have a comprehensive understanding of their AI costs, with many discovering the numbers after the fact. KPMG’s own global head of AI has said some of their clients exhausted annual token and cloud budgets within months, one of them watching usage climb sixfold.2 CFOs are getting surprised. This is an organisational maturity problem more than a technology one, the same governance gap that Agile exposed a decade ago, now running at the speed of token billing.

    There is a term circulating in enterprise AI circles now: tokenmaxxing. Organisations, in some cases deliberately, encouraging employees to maximise AI usage. Some built token-consumption leaderboards, rewarding the most active users. Replit’s own AI head has called these leaderboards “very dystopian,” arguing publicly that more tokens do not equal more business value.3

    The Agile comparison is almost too clean. Messy Agile measured velocity instead of value, and teams ran sprints full of story points that delivered nothing useful. Tokenmaxxing measures consumption instead of outcome, and organisations run up bills generating AI-produced work that doesn’t change anything meaningful downstream. The metric changed. The dysfunction is the same.

    The games an organisation chooses to play internally with AI will be its own demise if the path is wrong. And the path is almost always determined not by the technology but by the invisible operating system underneath it.

    In organisations whose immunity system is oriented toward protecting existing power, AI generates a particular kind of dysfunction: the theatre of adoption without the substance. Tools purchased, training announced, dashboards built, constraints untouched. In organisations with a healthier operating system, AI costs get managed the way cloud infrastructure costs eventually got managed: per-team budgets, model routing, usage governance, ROI tracking per workflow. What is now being called AI FinOps. The discipline looks familiar because the problem is familiar. Every time a new technology scales faster than the operating model around it, the organisation has to build the governance infrastructure after the fact, and the bill for that delay is always larger than expected.

    The technology doesn’t determine which path an organisation takes. The invisible operating system does.

    Two organisations can buy the same tools, run the same training, appoint the same internal AI champion, and arrive at completely different results, because the immune response they brought to the implementation was different. One accepted. One rejected. And neither the acceptance nor the rejection showed up in the original business case.

    The question worth more time, before any AI rollout, is not the technology question. The technology question gets answered relatively quickly. The organisational question is harder: what does this institution’s immunity system protect, what will it accept as necessary for survival, and who controls the informal decisions that will actually determine whether this takes root or gets quietly extinguished?

    Most organisations don’t ask it. They buy the tools, announce the programme, train a cohort, and spend the next eighteen months wondering why adoption is low and the results are thin.

    I’ve seen this before. The technology was different. The immunity system was the same.

    1. Uber capped AI coding spend after exhausting its annual budget by April 2026: PYMNTS, CryptoBriefing. ↩︎
    2. KPMG AI Quarterly Pulse Survey on AI cost visibility (only around 26% of firms report a comprehensive view), plus KPMG’s global head of AI on clients exhausting budgets within months: Implicator.ai, KPMG. ↩︎
    3. “Tokenmaxxing” and Replit AI head Michele Catasta calling consumption leaderboards “very dystopian”: AOL, CIO, Pragmatic Engineer. ↩︎

  • When the World is Changing: How to Make Sense of It

    I watched the internet arrive. It landed on my desk as a working problem long before it was a headline, and it rearranged how the work got done while we were still looking the other way. One year we were faxing change requests and walking floppy disks between buildings, and the next we were arguing about whether email counted as a real record, whether you could trust a document that nobody had signed. Nobody stood up and announced it. It seeped in, and by the time we noticed, the old way of working had already left the building.

    Twenty something years later I am watching the same thing happen again, except the word on everyone’s lips is AI instead of internet, and the people panicking and the people cheerleading are roughly the same shape they were last time, just younger and louder and faster to post about it.

    So let me say the thing this whole piece stands on. The business cycle moves in circles. We keep drawing it as a straight line, an arrow that only ever points forward, and the drawing has been wrong the whole time. Things come back around, dressed in different clothes, carrying a new acronym, and we treat each turn as if it has never happened before in the history of the world. It has. We just forgot, because in between the turns we adjusted, we adapted, we made the strange thing normal, and then somewhere along the way we mistook that normal for permanent.

    When I was made redundant at 57, that was a cycle too, though it did not feel like one at the time. It felt like an ending. A career of 30 plus years in technology leadership, a last role running platform engineering with hundreds of people reporting up through me, and then redundancy, and then a very quiet Monday morning with nowhere I had to be. I have since stopped calling it retirement. I call it portfolio life, which is partly a nicer word and partly the truth, because the world did not stop turning when my old role disappeared. It just turned, the way it always does, and left me to work out where I now stood on the wheel.

    That is the lens I want to put on the present moment. Not prediction. I am useless at prediction, and so is almost everyone selling it. The lens is recognition.

    The same river, a different season

    In 1995 Peter Drucker published a book called Managing in a Time of Great Change. Read the title again, slowly. A time of great change. He was writing about the internet, globalisation, the early tremors of the information economy, the sense that the ground under management had shifted and the old maps no longer matched the territory. People read it then the way people read the AI commentary now, as if someone had finally found words for a feeling they could not name.

    Here is what struck me when I went back to it. Almost nothing in that book was actually new in 1995. Drucker was not inventing fresh wisdom for a fresh crisis. He was reminding people of things they already knew and had stopped doing, because the long stretch of relative calm before the storm had let them get comfortable, and comfort has a way of erasing the lessons of the last storm. His advice reads as evergreen because it is. It works in 1995, it works in 2008, it works now, and it will work in whatever year the next great change arrives wearing whatever the next acronym turns out to be.

    The reason it feels new each time is simple, and a little embarrassing once you see it. We adapt. We are good at adapting, it is one of the better things about us. But adaptation has a cost. Once the new technology becomes ordinary, once the new process is just how we do things now, we file away the discomfort that taught us anything, and we go back to managing the present as if the present were the permanent state of affairs. Then the wheel turns, the norm we adjusted to stops being the norm, and we stand there blinking, certain that nobody has ever been this confused before.

    What actually does not change

    The technology changes. The application of it spurs new processes, new business models, new centres of power. The internet gave us e-commerce and remote work and the slow hollowing of the high street. AI is busy doing its own version of that right now, and the specifics will surprise us, they always do.

    But underneath the changing technology, human behaviour sits more or less still. The same fears, the same herd instincts, the same rush to either panic or worship whatever is new. The same handful of people quietly getting on with the work while everyone else argues about whether the sky is falling or ascending.

    There is an old line I keep coming back to. There are three types of people in this world. Those who wait for things to happen. Those who do not even know that anything is happening. And those who make things happen. The technology of the day does not change those three categories one bit. The internet did not abolish the people who waited, and AI will not either. All a great change does is sort people more sharply into the bucket they were already heading toward.

    So the question worth asking when the world is changing is not “what will happen next.” Nobody knows, and the confident ones are usually selling something. The better question is “which of the three am I being right now,” and that one you can actually answer, because it is about you, and you are the one variable you have some say over.

    Drucker’s list, and why we forget it

    If I strip Drucker’s advice back to its bones, the most useful piece is the first one, and the rest hang off it. Stop trying to predict the future, and start understanding the impact of what has already happened. The future is fog. But the change that has already landed is sitting right in front of you, fully formed, waiting to be read. The internet had already happened by the time most companies got serious about it. AI has already happened. The work is to metabolise what is already true and act from there, which turns out to be a different muscle than forecasting, and a much more reliable one.

    The rest of his list is the same sort of unglamorous, durable common sense. Know what you are genuinely good at, and protect it, because a changing market punishes the company that forgets its own strengths and goes chasing someone else’s. Pay attention to the people who are not your customers, because the reason they stay away usually tells you more about where the world is going than the praise of the people who already love you. Treat information as the raw material it has become. Watch how new industries quietly redraw the map of who holds the power, the way the tech firms did in the 90s and the way the AI firms are doing right now, while the rest of us are still looking at where the power used to be.

    None of that is clever. That is exactly the point. It is just true, and it stays true across every turn of the cycle. We do not fail to follow it because it is hard to understand. A twelve year old could understand it. We fail to follow it because between the storms we got comfortable, we adjusted to a norm, and we forgot that the norm had an expiry date stamped on it the whole time.

    Standing on the wheel

    I do not think AI is a revolution in the breathless sense the headlines want it to be. I think it is a bicycle. It can carry you a great deal further than your own legs, much faster, and it will not get you anywhere at all if you do not know how to ride it, and it will happily tip you into a ditch if you treat it like a horse that thinks for itself. The real question was always whether you would get on and pedal, and in which direction, and that question is as old as every tool humans have ever picked up.

    So when people ask me how to make sense of a world that is changing, I do not give them a forecast. I tell them to look down at the wheel they are standing on and notice that it has been here before. Read the change that has already happened. Hold on to what you are actually good at. Watch the people drifting away from you more closely than the ones cheering. And decide, honestly, which of the three types of person you are going to be this time around, because the wheel does not care, it turns either way.

    I have ridden two of these turns now, the internet one and this one, and I lost a career between them and found a quieter, better-fitting life on the far side. The technology was different each time. I was not. And that, more than any prediction, is the thing that has made sense to me.

    It’s worked for me.

  • What AI Exposes – Part 1 of 6

    AI doesn’t remove organisational constraints. It accelerates exposure to them.

    I’ve seen this with Agile adoption before.

    When Agile arrived in earnest, the promise was faster delivery, tighter feedback loops, smaller batches of work shipped more frequently. And to be fair, the delivery engine did get faster. Sprints ran. Stand-ups happened. Velocity metrics climbed.

    What nobody anticipated was what faster delivery would surface underneath.

    At ANZ, one of the wholesale development teams adopted Scrum to make their development work more productive. The sprints ran. The stories got written. The cadence held. And then the team hit the same wall, every cycle: the production team, the backend that actually implemented and released the code, was the bottleneck. You can’t release on a two-week cadence when the deployment pipeline is controlled by a separate team operating on a different rhythm entirely. Scrum didn’t create that constraint. It just made it impossible to ignore.

    That’s the thing about faster feedback loops. They take the problems you’d been working around and drop them onto a schedule.

    Governance bottlenecks that had been invisible suddenly became obvious, because the team was now waiting on approvals that used to take weeks, and the wait was now a sprint event. Procurement delays, executive indecision, fragmented ownership between teams, overloaded support functions, the kind of organisational friction that had always been quietly present, all of it became visible, quickly, repeatedly.

    Agile didn’t create those problems. It exposed them. The delivery engine ran faster than the surrounding operating model could handle.

    There’s a second version of this, and it’s less about the gap between teams than about a gap inside one team, a gap between the method and the work itself.

    Back then, in the early years of Agile adoption in New Zealand, there weren’t many people who knew how to run it well. The training existed. The certifications existed. What didn’t exist yet was depth, people who’d been through a full end-to-end Scrum implementation enough times to know where it actually breaks.

    So what happened, more than once, was someone attended a few days of certification training, came back with a freshly printed credential, and was treated, by the team and often by themselves, as the expert in the room. At ANZ, I watched this directly. Two staff were sent away for a five-day Scrum course, back at their desks the following Monday, expected to design how the team would work and then run it, as if five days in a classroom had given them everything a real implementation actually needed. They weren’t being dishonest about it. They simply hadn’t done it long enough yet to know what they didn’t know. It was an honest version of the blind leading the blind, well-meaning, certified, and still learning on the job in front of the people they were supposed to be guiding.

    There’s a third version too, and it’s less about people than about fit, a gap between the method and the work itself.

    Stats New Zealand’s technology function isn’t structured the way a bank’s is. At BNZ, BNZ Digital was purely a development outfit, building the future of online retail banking and parts of wholesale. Production issues were limited, and when they happened, they got folded into the backlog as a future release. One focus, one rhythm. The team ran a blend of Scrum and Kanban that worked because the work itself was singular: build things, ship things.

    At Stats NZ, each technology team was both a development unit and a production support unit for its own applications, at the same time, every sprint. Development work fits Scrum naturally, you can estimate it, size it, accumulate story points toward a sprint goal. Production support doesn’t work that way. An outage doesn’t wait for the next sprint planning session, and you can’t put a story point estimate on “the census database fell over at 2am.”

    So which method do you run? Scrum says commit to a sprint goal. Kanban says manage flow, work moves through states as it arrives, no fixed commitment. Both are legitimate. Neither one, on its own, matches a team that is doing both kinds of work simultaneously. What I saw at Stats NZ was teams trying to run Scrum and Kanban at the same time, in the same team, without anyone resolving which one governed which kind of work. That’s a fit problem. The method didn’t match what the team actually was.

    We did try to fix it. A Kanban coach came in to help shift the technology operation toward something more workflow-based, more adaptive to the support side of the work. I’m not saying it solved the problem. What it did, immediately, was create tension between the people who had built their identity around Scrum and a coach whose entire philosophy ran in a different direction, workflow versus story points, two ways of seeing the work that don’t fully reconcile. Some teams adapted well. Others carried on reluctantly, doing what they were told without believing in it, which is its own kind of cost, quieter than resistance but not really different from it.

    In the end, the team managers carried it. They were still accountable for what they’d promised the business and its customers, and that didn’t change just because the method underneath them did.

    And there was a layer above all of this that nobody at the team level controlled. Around the same time, there was pressure from outside the organisation, government agencies signalling that SAFe was the direction Agile adoption should take across the public sector. Another flavour, another framework, arriving with its own philosophy and its own assumptions about what “good” looks like, and requiring dedicated leadership to steer it properly, leadership that was already stretched thin steering the first change.

    Fixing a fit problem doesn’t happen in a vacuum. The fix creates its own friction, and external pressure arrives with its own opinion about what the fix should have been, usually without much interest in what’s already underway.

    This is where AI is heading, and faster than Agile ever did.

    AI increases analytical speed, automation, report generation, content production, decision support, all at once, across every kind of organisation, regardless of what that organisation is actually for. A bank building new digital products and a statistics agency keeping the national data infrastructure running will both get offered the same AI tools, the same vendor pitches, the same “transform your organisation” programme. Neither tool knows which kind of organisation it landed in. Neither does the implementation plan, usually, because nobody asked the question first.

    And the same expertise gap that hollowed out early Agile rollouts is already showing up here. There isn’t enough deep AI experience to go around yet, the kind built from actually deploying these tools inside a real organisation and watching what breaks. What there’s no shortage of is the credentialed version, a course completed, a badge on a LinkedIn profile, a confident voice in a steering committee. None of that is dishonest. It’s just early, the same way Agile was early once, and the gap between the credential and the experience is exactly the gap that costs an organisation real money once the rollout actually starts.

    And when an organisation does try to course-correct, bringing in an AI lead, an AI coach, a new framework, expect the same pattern. New tension between the people who’d built expertise around the old approach and whoever’s now steering the new one. Some teams adapting well, others complying without believing it. And somewhere above all of it, an industry body, a government directive, a board that read something in the Financial Times, arriving with an opinion about which AI framework or governance model the organisation should really be using, usually with no visibility into what’s already half-built underneath.

    And AI, unlike Agile, has a bill attached to every experiment. If an organisation hasn’t worked out what it’s actually for, before it starts pointing AI at its problems, and before it layers a fix on top of a fix while external pressure adds a third opinion, it will spend heavily finding all of this out the hard way. At least a wasted Scrum sprint was just time. A wasted month of AI experimentation, run at scale across a team, shows up as a number someone in finance has to explain.

    So before any AI rollout, the question worth asking goes deeper than “what is actually slowing us down,” though that matters too. What is this organisation actually for? Does the thing we’re about to deploy match that? And are we steering it ourselves, or about to hand the wheel to three parties at once?

    Most organisations skip that question entirely. They go straight to the tooling.

    I’d rather know what I’m building before I see the bill for it. Most organisations find out the other way round.