AI speeds up everything except the constraint. And the constraint is rarely the technology.
Every executive board I’ve sat in front of, whether at Te Wānanga o Aotearoa, Te Pūkenga, or earlier in my career, had the same shape underneath it, regardless of what the organisation actually did.
Papers had to go in weeks ahead of the meeting. There was a format, usually one set by governance requirements flowing down from a central government agency, and the format asked the same kind of questions every time. What were the procurement options. What competing quotes did you receive. How did you arrive at this recommendation. What’s the financial case. As a senior leader, you’d spend real time, real effort, getting that paper into shape, because the format mattered as much as the content, sometimes more.
And here’s the thing I noticed, the same thing in more than one organisation. Below a certain financial threshold, things moved. A CEO’s financial delegation might cover spending up to a certain amount, and inside that boundary, decisions happened at a reasonable pace, weeks, not months. Above that threshold, technology spending had to go to the board, or to the chairperson, or to a steering committee specifically constituted because the programme had crossed, say, a million dollars. And the moment something crossed that line, the pace changed completely. Not because the work got harder. Because the decision now belonged to a different cadence, board meetings, steering committee meetings, a calendar that ran on its own schedule regardless of how ready the work was.
Once you got past that approval point, interestingly, things sped up again, often quite quickly. Which tells you something important. The slowdown wasn’t about complexity, or capability, or even risk, not really. It was about which side of a financial line the work happened to sit on.
That’s the constraint. Not the technology, not the team, not even, most of the time, the people sitting on the board. The constraint is the threshold itself, and the cadence that governs anything above it.
Now think about what AI actually speeds up in this picture.
AI is very good at producing exactly the kind of paper that format demands. Procurement options, comparative analysis, draft business cases, the research that goes into “how did you arrive at this recommendation.” All of that gets faster. A paper that used to take a team two or three weeks to assemble properly could, with AI doing a lot of the drafting and research legwork, come together in days.
So picture what happens. More papers, better researched, arriving faster, all queuing for the same board cycle, the same steering committee cadence, the same financial delegation threshold that hasn’t moved an inch. The constraint doesn’t get any faster just because the things arriving in front of it got faster to produce. If anything, the queue gets longer, because now it’s cheaper and quicker to generate a business case, so more of them get generated, and they all still have to wait for the same monthly or quarterly slot.
This is the part that I think gets missed in most conversations about AI and productivity. Speeding up the non-constraint doesn’t speed up the system. It just means the constraint now has more sitting in front of it, dressed more convincingly, on the same clock as always.
At Te Pūkenga, this played out in a very specific way. There was a real appetite, especially in technology, to move fast, to make decisions and adjust later if needed. Seek forgiveness rather than permission, the old phrase. But that approach, taken too far, breaks the agreement between the organisation and central government, the governance arrangement that the financial delegation and steering committee structure exists to protect. So the choice sat with whoever held the role, often the Chief Digital Officer, and it wasn’t really a technology choice. It was a political one. Move fast and risk the relationship with the centre, and your own position along with it, or stay inside the governance lines and accept that the pace is set by someone else’s calendar.
Most people, reasonably, chose the second option. And the work that had been done, the research, the supplier engagement, the commitment built with development teams, much of it sat there, not wasted exactly, but not moving either, while the business division absorbed the cost of effectively standing still. Spinning the wheels, consuming budget, with nothing to show for it on the outside, because the thing that needed to move was waiting on a decision that hadn’t been made.
And here’s where it gets sharper still, because indecision isn’t neutral. Indecision is itself a decision, usually the decision to keep the status quo running for as long as possible.
At Inland Revenue, I started in January 2013. My first day on the job, we were hit with a Priority 1 incident, the kind that takes taxpayer-facing systems down entirely. My senior manager introduced me to the floor and told everyone I was the one who’d sort it out. That was day one. By the end of that year we’d had more than thirty Priority 1 incidents, on top of everything lower in severity that never made it into anyone’s headline numbers.
The infrastructure behind it told you why. The WebSphere Application Servers running our channel systems were three versions behind current, and they buckled under unusual load, especially during the tax peak season running April through August, exactly when the system needed to hold up the most. The Business Platform Services Channels team I was part of handled roughly 70% of IRD’s systems through its public-facing portal. Alongside that, the document management platform, an IBM Enterprise Content Management system that every tax form and every piece of taxpayer correspondence ran through, was already out of vendor support. We were paying IBM a premium just to keep getting support on a platform it no longer wanted to support, because the realistic fix, an upgrade to the lowest version still covered, never quite made it to the front of the queue.
Every outage got noticed. Leadership attention, public criticism, press coverage, a tax agency whose own systems taxpayers couldn’t access to file their returns, accountants scrambling to lodge clients’ returns through whatever workaround they could find. None of that is a small thing for an organisation whose entire mandate depends on people trusting the system enough to use it.
Keeping that infrastructure at 99.9% availability through tax season took real ingenuity, because the investment to actually fix it properly wasn’t coming. Leadership knew it. The plan, such as it was, was to sweat the assets for as long as they could be sweated, until the broader Business Transformation programme eventually arrived to replace everything properly. In the meantime, the onus sat with the people in Business Platform Services, keeping a system three versions behind current running at a reliability target that newer infrastructure would have struggled to hit without strain.
That requirement didn’t soften just because the underlying technology was ageing and the replacement programme was stuck in a queue somewhere above someone’s financial delegation. The risk of that gap, between what the system was being asked to do and what it was realistically capable of doing reliably, didn’t sit with whoever was holding up the approval. It sat with the people keeping the lights on every day, the ones whose job it was to make 99.9% happen on infrastructure that leadership already knew was past its best.
This is what I mean when I say AI exposes the constraint rather than removes it. If AI is used to speed up the analysis, the business cases, the options papers, the reporting, exactly the things that feed into a board cycle or a steering committee, none of that touches the actual bottleneck, which is a financial threshold and a meeting cadence set by governance arrangements that have nothing to do with how fast anyone can write a paper. What changes is the size and polish of the queue sitting in front of that bottleneck, and the distance between what’s technically possible and what’s actually approved gets more visible, faster, because the technical side is now moving at AI speed and the governance side is moving at the same speed it always has.
The gap was always there. Sweating the IRD assets, the Te Pūkenga standstill, these existed long before anyone in those organisations had touched an AI tool. What AI does is make the two speeds, the speed of producing the case for change and the speed of actually deciding on it, diverge so sharply that the gap stops being something you can quietly manage and starts being something everyone downstream can see.
I think that’s worth sitting with, if you’re the one several layers down, keeping something running at 99.9% while the decision about its future sits in someone else’s inbox. AI won’t move that decision any faster. It’ll just make it more obvious, to more people, sooner, exactly how long that decision has been sitting there.
Leadership, in my experience, genuinely doesn’t see this whole chain. They see their piece of it. The approval is pending, that’s a known item on a register somewhere, fine. The systems are running, availability targets are being met, also fine. Nobody in the room is looking at the line connecting those two facts, that the second fact is only true because of effort and risk being absorbed by people several layers down, for as long as the first fact remains unresolved. As long as it isn’t front-page news, the risk reads as managed.
I’ve been on both sides of that gap. I know which side feels the cost first.