Finding Where AI Earns Its Keep

Adoption fails most often not because the tool is weak, but because it was pointed at the wrong work. Your job here is to find the few workflows where AI clearly earns its keep. Prove it there, and refuse the sprawl of pilots that go nowhere.

When to use this

Use this at the start of a program, and again whenever the list of AI ideas has grown faster than the results. It is the antidote to launching more pilots than you can support.

How to discover use cases

Go to the work, not the tool.

• Ask each team where their time actually goes, and which tasks are repetitive, text-heavy, or a known bottleneck.
• Watch for shadow AI. Where people already reach for personal AI tools is a map of real demand.
• Look for tasks with a fast feedback loop, where a person can check the output in seconds.

Collect these as candidate workflows, described as jobs to be done, not as features.

How to prioritize

Score candidates on two axes: value if it works, and feasibility to deliver.

• Value — time saved, quality lifted, or a bottleneck cleared, at a scale that matters.
• Feasibility — data available, output easy to verify, and low stakes if it is wrong at first.

Start where value and feasibility are both high. These are your beachheads. A visible win in a high-value workflow buys the credibility to tackle harder ones.

The workflow-redesign lever

The biggest mistake is bolting AI onto a process and expecting change. The value comes from redesigning the work around what AI now makes cheap. Do not ask where you can insert the tool. Ask how you would run the work differently if a task took minutes instead of hours. That is where the real gains sit. It is also why adoption and workflow redesign are the same project, not two. For the program-level treatment of redesigning work around AI, see Workflow Optimization & Automation.

Avoiding pilot sprawl

Pilots are cheap to start and expensive to sustain. A dozen half-supported pilots produce noise, not adoption.

• Cap the number of live pilots you actively support.
• Give each one a named owner, a success measure, and a decision date.
• Kill it or scale it on that date. A pilot with no path to production is a hobby.

Watch out for

• Tool-first thinking. Starting from "we have licenses, find uses" instead of from the work.
• Boiling the ocean. Too many pilots, none resourced enough to succeed.
• Unverifiable wins. A use case whose output nobody can check quickly will not build trust.
• The demo trap. An impressive demo is not an adopted workflow. Count the second, not the first.