AI can read what’s written down. It can’t read the room.
I was naive the first time I got close to real power.
I was CIO for the Malaysian operation at ABN AMRO, and it was the first time I’d been anywhere near the corridors where decisions actually got made. Up to that point, I’d assumed that what happened in meetings was mostly what was happening. I learned quickly that wasn’t true. There were deals behind the scenes, agreements reached in rooms I wasn’t in, what I’d call horse trading, the kind of give and take between senior people that never appears in any paper, any minutes, any business case.
I didn’t have anyone showing me the ropes before that. No mentor pulling me aside to explain how things actually worked, the way some people get. Most of what I learned, I learned by getting it wrong first, reading senior leadership moves badly, more than once, because I’d assumed, the way management textbooks teach you to assume, that everyone in the room was working toward the organisation’s stated goals and that personal interest sat somewhere well below that. I went looking for better teachers wherever I could find them, suppliers, older employees who’d seen more cycles than I had, and through my MBA, where I sought out senior executives at other listed companies who could tell me what the textbooks left out. Some of them taught strategy and financial management at business school themselves. What they gave me wasn’t in the assigned reading. Looking back, those were my most formative years, the MBA and ABN AMRO running at the same time, each one teaching me something the other couldn’t.
What you see in the room may not be what it actually means. And if you raise something, even something real, something you’ve genuinely identified as a problem, it can go nowhere. Not because anyone disagrees with you. Often nobody disagrees with you at all. You get lip service, you’re told it’s a good point, worth considering, and then you’re kept busy, genuinely busy, with work that feels important, until at some point you’re told to stop. No budget. Leadership doesn’t approve it. Other priorities. Whatever the reason given, it’s rarely the real one.
I used to think that was wasted effort. I don’t think that anymore. I think it’s closer to camouflage. Keeping you occupied with something real enough to feel meaningful means you’re the one focused elsewhere, not the one asking questions, not the one creating friction around whatever the actual direction of travel is. You become part of the cover, without ever being told that’s the role you’re playing.
Here’s what that looked like in practice. The Managing Director issued a directive, get rid of personal colour printers across the operation, replace them with three leased colour copiers instead. Sound economics, better cost control, easier to monitor consumption. I followed it. Then the Asian Financial Crisis hit, and in the middle of it, Treasury delivered the bank’s entire year’s profit target in a single week. The Head of Treasury came to me asking for a colour printer of his own. I declined. We had a directive from the MD, and as far as I was concerned, that was the end of the conversation.
I hadn’t read the room at all. I was reprimanded for it, called penny wise and pound foolish, for refusing to sign off something so small against the scale of what Treasury had just delivered for the bank. The policy was real. The directive was real. None of that mattered once a senior leader had just made the year for the entire Malaysian operation. That’s when I understood what equity, fairness, and meritocracy actually meant inside an organisation, and what they didn’t. The dots I needed to connect weren’t in the policy document. They were in who had just earned the standing to ignore it.
That was ABN AMRO. The lessons came one humiliation at a time, and I was new enough to all of it that I didn’t yet see the whole pattern underneath.
Years later, at Te Pūkenga, I understood it completely.
We had real work underway, a digital transformation to bring 25 separate polytechnic and vocational education platforms down to one, a programme that would have cut close to a third of the operating cost across the network. The case for it was strong. And it kept hitting roadblocks, one after another, each with its own reason, no funding yet, no approval yet, other priorities ahead of it, despite earlier agreement that it would proceed.
By that point I’d learned to read the room differently. Human nature doesn’t change much, whatever era you’re in. When the people closest to the centre, the ones who hear things in rooms you’re not in, start quietly updating their CVs, start having conversations that sound like they’re already planning their next move, that tells you something the org chart never will. It’s a filter of a filter. They’re reacting to information you don’t have access to, and their behaviour is the signal.
At Te Pūkenga, a change in direction was coming for the organisation, well before it was formally announced. I’m not going to comment on whether that decision was right or wrong, that’s well outside anything I have a view worth sharing on, and frankly it was beyond anyone at my level to influence either way. What I will say is that the roadblocks we kept hitting, the funding that never quite arrived, the approvals that kept slipping, made a different kind of sense once you understood that the organisation’s future was already an open question at a level none of us could see directly. The work wasn’t being blocked because it was a bad idea. It was being held in place because committing real money to a transformation that would take years to deliver didn’t fit with a future that might not include the organisation in its current form.
Nobody told us that. Nobody could have, probably, even if they’d wanted to. We worked it out by watching what the people around us did, not what they said.
I think this is what sense-making actually is. Not analysis, not data, not even particularly intelligence in the way that word usually gets used. It’s connecting what you observe, what’s said directly, what’s implied, how people’s behaviour shifts, against everything you already know about how organisations and the people running them tend to behave, until a shape emerges that nobody has confirmed but that turns out, later, to be correct.
Not every signal points toward self-interest, though. I learned that at ANZ too, watching a different kind of decision get made.
Graham Hodges joined ANZ New Zealand as CEO in November 2005. The decision to merge the ANZ and National Bank mainframes and systems had already been sitting in analysis since 2003, two rounds of review, a mountain of documentation, no call made. During his tenure, he made it, the kind of decision a more cautious leader might have let sit for another review cycle. Then he did something nobody had budgeted for. He proposed ANZ lead the development of a Snapper card for public transport payment in New Zealand, something like London’s Oyster card, or the Octopus card he’d have seen used in Hong Kong. Nothing in the existing programme of work accounted for it. He asked for budget to be pulled from other initiatives to fund it. I was in the room at a leaders’ update when he told the whole leadership team why, that New Zealand needed it, and that ANZ’s brand belonged at the front of building it, not because any individual business unit had asked for it. Snapper went live in July 2008.
Nobody saw that coming. Every CEO ANZ HQ had sent out before him had played it safe. This read differently to me, closer to conviction than caution, the kind of bet a leader makes when belief in an idea outweighs the safety of staying inside existing budget lines. The bet didn’t cost him either. He left ANZ New Zealand in May 2009, promoted to Deputy CEO of the ANZ Group. Reading the difference between someone protecting themselves and someone genuinely backing an idea they believe in is its own kind of sense-making, rarer than the other kind, and worth getting right, because mistaking one for the other costs you something different each time.
And here’s where I think AI sits in all of this, and where it doesn’t.
AI is extraordinarily good at the documented layer of an organisation. The papers, the reports, the meeting minutes, the business cases, the dashboards. All of that, AI can read, summarise, generate, analyse, faster than any of us could. Parts 3 and 4 in this series were both, in a sense, about that documented layer, the steering committee pack, the business case waiting for approval above a financial threshold. AI makes all of that faster and more polished.
But the documented layer was never where the real decision lived. The real decision lived in belief and fear, sometimes tangled together the way they were at ABN AMRO, sometimes belief running clean on its own, the way it did with Hodges and his Snapper card. Either way, it sat with a small number of people at the apex of an organisation, employees too, whatever the org chart said about their seniority, their real stake nothing more than their position, their income, their title. And almost none of that gets written down. It can’t, often, because writing it down would make it real in a way that creates its own problems. So it travels through behaviour instead. Through who goes quiet. Through which topics never quite get scheduled. Through the slight change in tone in a room that, on paper, hasn’t changed at all.
AI has no access to that layer. It was never going to. The information doesn’t exist anywhere AI can read it, because it was never put anywhere in the first place, deliberately.
So here’s what I think happens as AI takes over more and more of the documented layer. The documented layer gets faster, cleaner, more abundant, and it becomes less and less where anything is actually decided, because it never really was. What’s left, the undocumented layer, the belief and fear sitting with a handful of people, doesn’t shrink. It becomes more clearly the only layer that ever mattered, just harder to see precisely because everything around it now looks so comprehensive and so well-documented.
AI is a product, like every other product that’s come through an organisation before it. New tools change what gets produced and how fast. They don’t change who holds the real decision, or what that decision actually runs on. That’s been true of every wave of technology I’ve worked through, and I don’t see why this one would be different.
What’s changed for me, I think, is that I stopped expecting the documented layer to tell me anything useful about where things were heading, a long time ago. I learned to watch the room instead.
It’s served me well. Whether it’s something AI can ever do, I genuinely don’t know. I suspect not.