Why the organisations that thrive with AI won’t be the most technical — they’ll be the most agile. And why leading AI-induced change is a change-leadership job, not an IT one.
Every week there’s a new AI tool.
Every month there’s a new prediction about which jobs will disappear.
Every quarter there’s a new executive declaring that AI will transform their organisation.
And in the middle of all that noise, we think a lot of organisations are busy solving the wrong problem.
The question they’re asking is “How do we become AI capable?”
The question they should be asking is “Are we agile enough to survive AI?”
Because AI capability isn’t the starting point. Agility is.
AI doesn’t create uncertainty. It amplifies it.
AI isn’t necessarily coming for your job. Not yet. But it is changing how work gets done, how decisions get made, what expertise even looks like, what customers expect, and the pace at which organisations have to learn.
The result is that a lot of organisations feel like they’re sailing through stormy seas — and trying to navigate the storm with the same instruments they used in calmer waters.
Detailed plans. Lengthy approval processes. Big-bang implementations. Perfection before release.
Those approaches were already creaking in a world of continuous change. AI just exposes their limits faster.
The winners aren’t the most technical. They’re the best learners.
One of the more interesting things emerging from AI adoption is that the organisations pulling ahead aren’t always the ones with the best technology.
They’re the ones with the strongest learning cultures — the ones that already know how to experiment safely, learn quickly, adapt continuously, involve people early, and make decisions with incomplete information.
In other words, they’re already agile.
When a new tool emerges, they don’t disappear for six months to write the perfect implementation plan. They test. They learn. They adjust. Then they test again.
This isn’t recklessness. It’s responsiveness.
You’re choosing a path — whether you name it or not
Here’s where agility becomes more than speed. Agile leaders make deliberate choices, and AI hands every executive team a set of them.
Strip away the hype and most organisations are choosing between four change designs:
- AI-native transformation — “We’re all in.” AI everywhere, all at once. Crystal-clear intent, real opportunity for the ambitious, and serious ROI risk if the enthusiasm masks bad design.
- AI augmentation — “Slowly and cautiously.” Keep the structures and roles, layer AI in selectively. Easier to adopt, less threatening — but if your processes were weak to begin with, you now have AI-augmented slop.
- Deliberate redesign — “Never waste a crisis.” Use AI as the occasion to rethink how work is structured without abandoning who you are. Often the smartest choice, and the one that asks the most of your change leadership.
- Unsanctioned shifts — the default you back into when you make no choice at all. Employees quietly adopting their own tools, others pushing back, customers reacting to AI-mediated service. The change still happens. You’re just not leading it.
The point isn’t that one path is right. The point is that each one asks something different of you — different pacing, different investment, different communication, different leadership behaviour.
And if you’re not making a strategic change choice, you’re defaulting to one. That can be a very expensive default.
AI demands exactly what agile change has always promoted
This is why agile change leadership isn’t a “nice to have” alongside your AI strategy. It is the capability your AI strategy depends on. The same things agile change practitioners have advocated for years are the things AI now makes non-negotiable:
Progress over perfection.
By the time you’ve built the perfect implementation strategy, the technology has moved. The organisations making headway are learning in public and adjusting as they go.
Continuous engagement.
AI adoption is rarely a technology problem. It’s a people problem. What does this mean for my role? What skills do I need? What happens next? The leaders answering those questions early and often see better adoption.
Data-informed decisions.
AI generates extraordinary volumes of data, but data alone doesn’t make good decisions. The real skill is sensing what’s happening, reading the signals, and responding well.
Empathy over efficiency.
The biggest mistake leaders are making is treating AI purely as a productivity play. People don’t experience AI as a productivity initiative. They experience it as uncertainty, as possibility, as risk, and sometimes as fear.
When Canva announced workforce reductions linked to AI, some read it as proof of job displacement and others as proof of productivity gains. Both reactions are fair. The real lesson sits underneath the debate: whether AI creates jobs or eliminates them, your organisation has to be capable of navigating the transition. That capability is not primarily technical. It’s organisational, cultural, and human.
This was underscored by global furniture giant Ikea – IKEA famously redesigned 8,500 customer service jobs rather than eliminating them. When their AI chatbot “Billy” took over routine tasks like order tracking and returns, IKEA retrained these call center agents into remote interior design consultants, resulting in zero layoffs and generating over $1 billion in new revenue.
The human bit hasn’t gone anywhere
Whichever path you choose, there are humans at the centre of whether it works.
Fear is still the primary experience — and if anything it’s intensified into a kind of compound fatigue. People who survived the pandemic and the workplace upheaval that came with it are now facing a technology that moves exponentially fast and appears to think on their behalf.
So in any given team you’ve got people fretting about their relevance, people over-indexing on AI because they think they should, people who are curious and moving fast, people who’ve spent twelve months of nights and weekends building agent workflows, and a decent chunk who are simply tired.
All of that shows up in how they engage with the change. Leading through it takes judgement, not just tooling.
Agility is the anchor
Storms create uncertainty. Anchors create stability — not by stopping movement, but by stopping drift.
That’s what agile capability does. It doesn’t eliminate uncertainty. It gives leaders and teams a way to move through uncertainty without being overwhelmed by it.
Which is why we increasingly believe agility is the precursor to AI capability, not the other way around. If your organisation struggles with experimentation, feedback, adaptation, transparency, and continuous learning, AI won’t solve those problems for you. It will magnify them.
The biggest risk right now isn’t AI. It’s leaders skipping the thinking and going straight to the doing — getting dazzled by the technology and forgetting that the returns depend entirely on good design and the savvy management of people.
Because when the seas get stormy, it’s not the fanciest boat that matters.
It’s whether you have an anchor.
This is the thinking behind Leading AI Change — the Agile Way — our package for leaders who’d rather lead AI-induced change deliberately than be dragged along by it. If you want to build the anchor before the storm, start here.
