AI Demands Micro-Pivots
If a startup is still asking whether it should pivot, it may already be too late.
That kind of big, dramatic pivot feels like a leftover from the 2010 startup playbook. Changing the deck, the category, or the story rarely fixes weak learning loops, slow product decisions, or too much distance from real users.
After 30 years building products and developing seven startups, I have come to believe the better model is continuous micro-pivoting. Not one grand turn. A steady series of small corrections across product, positioning, and execution.
AI development makes this even more obvious. In AI-first products, the right answer shifts constantly: value proposition, onboarding, retrieval quality, cost, workflow fit, and where the product creates real value. The roadmap cannot stay fixed for long. At the same time, the cost of trying small changes has dropped. Teams can test a prompt, workflow, interface, or model choice much faster than in earlier product cycles. Less friction makes real-time micro-pivoting not just necessary, but practical.
The closest analogy for me is chess. You may start with a plan, but every serious move depends on how the board changes. In AI-first product work, learning faster than the plan matters more than defending the plan.
#AIProducts #ProductStrategy #Startups #AIFirst