MIT and McKinsey found a gap the size of the Grand Canyon: 80% of companies claim they’re using generative AI, but fewer than 1 in 10 use cases actually ship. Blame it on scattered data, fuzzy goals, and governance that's still MIA.
A new stack is stepping in: product → agent → data → model. It flips the old ML workflow on its head. Start with the user problem. Build the agent. Then wrangle your data. Only then reach for the model.
