AI Ready
AI Usage card, MethodKit for AI Readiness
Card 11 of 48 · MethodKit for AI Readiness
  • ThemeYour Work
  • CardCard 11 of 48
  • Questions5 to explore
Your Work

AI Usage

How well the team understands and uses AI today

An honest reading of how your team uses AI today is the starting point that prevents you from building on an inaccurate map.

Most teams have more variation in AI usage than they realize. Some people are using AI tools daily without mentioning it; others have tried a few times and stopped; some have not started. That variation matters because it means any shared AI workflow is meeting people at very different starting points.

The question is not just how much AI is used but how well. Using a tool every day and using it superficially produces different results than using it less often but with good prompts, clear context, and critical review of the output. Volume is not the same as readiness.

A team-level usage picture also reveals gaps in shared practice. If some people have figured out what works and others have not, the knowledge transfer has not happened yet. Getting AI-ready as a team means raising the floor, not just rewarding the ceiling.

Make it visibleRun a short team check-in (fifteen minutes is enough): each person names one AI tool they have used in the last month, what they used it for, and whether it worked. Write the results down. That simple inventory is more accurate than any assumption about where the team stands, and it opens the conversation about what to try next.

Why AI needs this

Each part of your work matters to AI in a specific way. Some of it is context a tool needs before it can help, some of it is work a tool can take on, and some of it is judgment that should stay with you.

Who is using what and how

A clear picture of current usage reveals where the skills and habits already exist and where they need to be built. That is a much better starting point than assuming uniform capability.

Quality of use, not just frequency

Knowing that someone uses an AI tool every day says nothing about whether they are using it well. The practices that produce good output (clear prompts, relevant context, critical review) need to be assessed separately from frequency.

What has worked and what has not

Teams that have tried AI on real tasks have learned something from those attempts. Surfacing what worked and what failed is the most efficient way to raise the team's collective level.

The gap between current use and potential

Current usage is not the ceiling. A team that understands where it is today can set a realistic horizon for where AI can take them, rather than projecting from hype or from the most enthusiastic person in the room.

Questions to explore

Use these on your own or in a group. There are no right answers, only better conversations.

  1. Who on the team uses AI tools regularly, and what are they using them for?

  2. Where has the team tried AI and found it genuinely useful, and what made it work?

  3. Where has the team tried AI and been disappointed, and what went wrong?

  4. What shared practices or norms around AI use, if any, does the team already have?

  5. If you rated the team's current AI capability honestly from one to ten, what score would you give and why?

Readiness traps

  • Self-reported AI usage tends to be inflated upward in some teams (no one wants to seem behind) and inflated downward in others (no one wants to seem like they are cutting corners). Getting an honest read requires psychological safety.
  • The most capable AI user on a team can give a misleadingly optimistic picture of team-level readiness. What matters is the median, not the ceiling.
  • Teams often know what AI can do in principle but have not connected that to their specific work. The gap between general awareness and specific applied use is often wider than people expect.