I keep seeing the same pattern: companies invest heavily in AI strategy, then struggle to move from planning to actual results.
The gap isn't usually about the technology itself. It's about execution—how you organize teams, change processes, measure success, and sustain momentum when the initial enthusiasm fades.
A solid strategy document means nothing if your organization can't operationalize it. That requires aligning incentives, building the right skills across departments (not just data science), and being honest about what needs to change in how work actually gets done.
The companies that move AI initiatives from pilot to production tend to share something in common: they treat it as an organizational challenge first, a technical one second.
What's your experience? Are you seeing execution gaps in your organization, and if so, where does the friction typically show up?
Source and context
This commentary was originally published on LinkedIn in response to The AI Strategy Execution Gap: Why Most Companies Fail to Deliver Results - solutionsreview.com.