🧠 AI Adoption: Problem to Solve or System to Manage?

MIT’s State of AI in Business 2025 report found that only 1 in 20 AI initiatives ever make it from pilot to production. That’s a management pattern and not a problem with the technology itself. (see AI is not a 'silver bullet': Why most initiatives falter)

It reminds me of the Pareto Principle: not exactly 80/20, but close — a vital few projects succeed while the trivial many fade out.

⚙️ Most organizations still treat AI adoption like a problem to be solved — with a deadline, a rollout, and a checklist. But it behaves more like a system to be managed — something that needs culture, feedback loops, and constant alignment between people, data, and purpose.

Here’s where the real opportunity lives: If only 5% of initiatives are truly scaling, then the best ROI might come from focusing on that 5% up front — identifying what makes them work and managing toward those conditions.

That shifts the question from:

“How do we roll out AI?”

“What system conditions let AI actually take root here?”

💡 I’m curious how others see it —Are you trying to solve AI, or manage it? And how do you identify the “vital few” projects that deserve real investment?

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