The argument of my new book, STOP PREDICTING: 13 Lessons from 1,400 Attempts to See What Was Coming is that the forecast was never the deliverable. The muscle you build by making it, sensing change, questioning your own assumptions, staying loose enough to adapt when you’re wrong, is.
AI Is Revolutionizing Strategic Decision-Making
“AI Is Revolutionizing Strategic Decision-Making” makes the same case. For decades, strategy tools were simple because human teams were the bottleneck. A SWOT analysis has four boxes and Porter’s framework has five forces not because markets are that simple, but because that’s what a room full of tired executives could manage on a whiteboard in an afternoon.
AI has removed that ceiling. It can generate and screen thousands of options where a team might manage a handful. It can build live, continuously updated models of markets and competitors instead of static quarterly snapshots. And it can run a structured, adversarial critique of a plan. A creator agent, a critic agent, a simulated competitor, without the groupthink and hierarchy that quietly wreck most real strategy meetings.
The punchline: if every company has access to the same AI, the technology itself stops being the advantage. What you build on top of it, your proprietary data, your embedded workflows, your speed of execution, is where the moat actually forms.
Where it meets the book
The author´s three shifts:
- Widen the option set before narrowing it
- Replace static models with living ones
- Institutionalize structured challenge.
Are a precise description of what #Mindcandy does, at a much smaller scale, for three and a half years. Cast a wide net of scenarios. Don’t converge too fast. Pressure-test what you think you know. AI is now doing at an industrial scale what a weekly scenario prompt used to do, one person, one LinkedIn post, at a time. That’s not a threat to the exercise. It’s a validation of it. The instinct to widen the aperture before you commit to a plan isn’t a quirky habit of futurists. It’s about to become standard strategic practice because the cost of doing it just collapsed.
It leaves the real (human) questions: “What do we believe? What are we willing to risk? What kind of company do we want to become?”
That is not data. No model, however well-fed with proprietary information, answers it for you. And it’s exactly the question STOP PREDICTING spends 13 lessons circling. Not “what will happen”. You’ll be wrong about that more often than not. That is not the point. The real question remains “what do we do given that we don’t know, and are we prepared enough to move when we find out?”
The part AI doesn’t touch
AI can generate the long list. It can keep your market model current instead of stale. It can argue with your plan harder than your VP of Strategy ever will, because it doesn’t need the job next year. All useful. None of it is the work of deciding what you value, what you’re willing to lose, or who you want to be. Once the option-generation and stress-testing get outsourced to a machine that never gets tired, that harder question becomes the only thing left for the humans in the room to do — which means it deserves more attention, not less.
AI can widen the net infinitely. It still can’t tell you what you’re willing to risk.
STOP PREDICTING: 13 Lessons from 1,400 Attempts to See What Was Coming is available now on Amazon UK and Amazon US. Buy a copy, leave a review, and I’ll buy you a glass of wine.
