How to Get Your Team to Actually Use AI Tools

The number of advisory firms that have purchased AI tools nobody actually uses is higher than anyone wants to admit. The tools get bought, logins go out, a training session happens, and three months later the usage data is flat.

This isn't a technology problem. It's a change management problem. Those require different solutions.

Why tools don't stick

Tools fail for predictable reasons. They require more steps than the old way. The output needs so much editing it's faster to start from scratch. They don't connect to the workflow they're supposed to improve. Or the team had no say in picking them and feels no ownership over making them work.

Any one of those is enough to kill adoption. All four together and you're not getting anywhere.

The design fix

The best way to ensure adoption is to make the tool genuinely easier than the alternative. Not easier in theory. Actually easier for the specific person using it in their specific workflow on a normal Tuesday.

That means testing with real users before rollout, not a demo environment. Incorporate their feedback. Fix the friction points before you ask the whole team to switch. Don't design for an idealized user. Design for the people you have.

The involvement fix

People adopt tools they helped choose or helped build. Bring one or two team members into the evaluation process. Let them test the candidates, give feedback, and shape how the workflow gets structured. They become advocates at rollout instead of skeptics.

The follow-up fix

The launch isn't the end of adoption. It's the start of it. Plan for two to four weeks of active follow-up: check in with users, ask what's working and what isn't, make adjustments quickly. Teams that see fast responses to their feedback adopt tools. Teams that feel ignored don't.

Measuring real adoption

Adoption isn't login frequency. It's whether the workflow the tool was built to improve is actually running through the tool. Define what that looks like before launch and measure it directly.

If people are logging in but not using it for its intended purpose, something about the design isn't working. That's fixable. But only if you're looking at the right data.