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Key Takeaways u003cstrongu003eAI has to live inside real decision points.u003c/strongu003e If it only feeds a slide deck or a dashboard nobody owns, the business keeps operating exactly as before.u003cbru003e
AI models work in the lab, then stall in the wild when workflows are undefined, data is messy and responsibility sits in one function instead of across the business
AI projects inside large companies usually start as one-off experiments. Teams spot a promising use case, spin up a model and prove it out in a lab or sandbox. On slides, it looks like momentum. Inside the business, almost nothing changes.
I’ve watched this play out over and over: a team builds something genuinely useful, shares early results, and then hits a wall when it’s time to plug the system into everyday decisions. The model performs, but the business continues to operate exactly as before.
The real problem: AI sits on the side
The core issue is where these systems live. Too many AI efforts sit alongside the business rather than inside it, generating insights that rarely shape decisions in a reliable, repeatable way. They inform a presentation, not a process.
Companies that actually scale AI start by looking at how decisions happen today: who makes them, what information they use, what gets prioritized and how risk is weighed. They also examine what slows execution down when people try to act on those decisions.
Once you look closely, the gap becomes obvious. Decisions are fragmented across teams, rarely defined consistently, and often driven by habit or intuition rather than structured inputs. That fragmentation creates friction, which in turn limits the impact any single AI system can have.
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