AI Adoption: Why It Stalls and How to Fix It

AI adoption fails for boring reasons. Not because teams hate AI, but because execution gets messy: unclear ownership, bad data, broken handoffs, and pilots that never turn into habits.

Quick decision checklist

Define the job-to-be-done: what must be faster, cheaper, or safer?
Fix the data path: AI can’t help if the inputs are missing or inconsistent.
Pick one workflow to operationalize before you scale across the org.
Assign ownership: who updates prompts, monitors drift, and handles exceptions?
Measure adoption: usage, time saved, quality, and follow-through—not vibes.

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AI Adoption

This page collects the best posts on what blocks adoption and how to push past it.

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Common blockers

Pilot purgatory: experiments never become a standard operating process.
Data friction: missing fields, inconsistent labels, and disconnected systems.
No owner: nobody is responsible for outcomes after the demo is over.
Tool fatigue: teams avoid “one more dashboard” that adds work back to them.

Next step

Most AI efforts stall after the demo. The win is operationalizing one workflow with clean inputs, ownership, and measurements.

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