Every week I see organizations treat AI adoption like enterprise procurement—months of planning, millions invested, very little learning. The real question is: how do we learn what AI can actually do for the business without betting the farm?
My answer is a data-first, problem-first, experiment-driven philosophy backed by a nine-week cycle that discovers, designs, prototypes, validates, and decides before you scale. It matches AI tools to the right problems, builds governance in from day one, and creates a portfolio of experiments that either ship or teach.