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Most AI strategies fail not because the technology doesn't work, but because the strategy was never really a strategy to begin with. Here are the three patterns I see most often.
1. Starting with the technology instead of the problem
Organizations get excited about a specific tool or model and try to find a problem for it. The result is a solution looking for a justification, not a business need driving a technology choice.
2. Underestimating the organizational change
AI adoption requires new workflows, new skills, and often new roles. A strategy that treats implementation as a technical project rather than a change management initiative will stall the moment it hits the org chart.
3. Confusing a pilot with a strategy
Running a proof-of-concept is not the same as having a strategy. Without a clear path from pilot to production — including governance, scaling criteria, and success metrics — most pilots die on the vine.
The fix for all three is the same: start with outcomes, design for people, and build the roadmap before you build the model.