If AI can generate infinite answers, strategy becomes about asking the questions that matter.
That single thought has shaped every conversation I’ve had with clients this year. While the world obsesses over prompts and the latest LLM platforms, the real story isn’t about what AI can do – it’s about how fast we can evolve the way we think.
We’re moving from an experimental era (think pilots, chatbots and inevitable mistakes) into an advantage age. One where the competitive edge comes not just from using AI, because everyone is. But by knowing how to use it with intent.
Abundance versus sacrifice
AI is creating an abundance. More content; more data; more slop. But abundance comes with a hidden cost. It tempts us to do everything.
That’s where strategy earns its keep. Because strategy, at its core, is the act of sacrifice. To decide what something is, you have to be willing to decide what it isn’t.
If the machines give us volume, then strategy can give us focus. If the machine gives us a thousand routes, then strategy can choose the one that matters most.
In a world obsessed with more, our value now lies in less and what we’re brave enough to leave out. Judgement, not data, becomes our most finite resource. And that’s what separates strategy from automation: the willingness to take a stand and fight for a point of view
The strategist’s paradox
LLM models don’t do ‘right’.
As tech analyst Benedict Evans reminds us, they’re probabilistic systems that tell you what a good answer would probably look like. Which means the strategist’s role is shifting from being the person with the answers to being the person who knows which questions are worth asking.
Think of it as a little like trying to find the sweet spot between humans teaching machines to think like us while we also learn to think like them. Somewhere in the middle is where modern strategy can find its edge.
The Advantage Equation
To move from experimentation into advantage, I think of three forces working together:
Risk × Control × Creativity
Each is essential — and together they define the new shape of strategic value
Risk has to be reframed as opportunity: In an AI context, risk means being open to imperfect experiments, purposefully failing and learning faster than the competition. The strategist’s job is to turn that uncertainty into structured curiosity, so exploration doesn’t feel reckless, it feels necessary.
Control is what transforms curiosity into confidence: In a world of open-source models, deepfakes and hallucinations, control isn’t about restriction, it’s about rigour. It’s the scaffolding that lets our thinking flourish without collapsing under ethical or reputational weight. Control means defining boundaries: what we train on, how we label, how we stay transparent. Clients aren’t just buying ideas; they are buying reassurance that the ideas are responsibly built.
Creativity is the multiplier — the part that turns all of it into something memorable: Because creativity is where meaning returns to the equation. It’s what transforms a technically capable use of AI into an emotionally resonant one. When we inject imagination into data, we stop building automations and start building stories. Risk is unavoidable. Control makes it reliable. Creativity makes it memorable. When all three align, AI stops being a productivity hack and becomes a strategic advantage.
Restraint as the new superpower
AI won’t diminish the need for strategists. But it does demand a shift in posture.
We’re no longer the gatekeepers of information; we’re the interpreters of probability. Translators between what the machine can calculate and what humans can feel.
The more AI we use, the more human our instincts have to become. Restraint, not speed, could be the new superpower.
The strategist of the future won’t be defined by who can prompt faster, but by who can think braver and who can see the connections no algorithm has been taught to notice
From blandness to boldness
The risk of this new era isn’t that AI will take all the jobs. It’s that it will take our opinions.
If everyone uses the same models trained on the same data to chase the same best practice, we end up optimising ourselves into blandness.
That’s why strategy matters more than ever. Lobbying for human intelligence in a world of code. Insisting on tension, narrative and nuance.
The advantage age isn’t about faster answers. It’s about better questions and the courage to ask them when the machine can’t.
