AI Ready
Skills card, MethodKit for AI Readiness
Card 6 of 48 · MethodKit for AI Readiness
  • ThemeYour Work
  • CardCard 6 of 48
  • Questions5 to explore
Your Work

Skills

What you're good at and what you do

The skills you bring to work are context AI needs to calibrate how much to explain, what to assume, and where to defer to you.

Skills shape the relationship between you and a tool. An expert in a domain who tells AI what they already know well gets output calibrated to that level. Without that signal, a tool defaults to explaining everything at a middling level of detail that often fits nobody well.

Skills also define where your contribution is irreplaceable. The things you do that others on the team cannot do easily are the things AI should support, not replace. Knowing your skill set helps you see where the leverage is: AI handles more of the general, so you can spend more time on the specialized.

This is also a useful audit. The skills you use daily are different from the ones on your profile or in your head. What you actually practice and keep sharp is the more honest picture, and it is the one that matters for deciding where AI can genuinely assist.

Make it visibleList your strongest five skills with a sentence on each: what you can do, how you use it at work, and where the limits of your expertise are. Save the list somewhere you can reference it when briefing a tool, so you can set the right level and tell the tool what to defer to you on.

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.

Calibrating the tool's output

When you tell AI what you know well, it stops over-explaining the basics and can engage at a level that is actually useful. That calibration saves time on every interaction.

Where human skill is the point

Some tasks require the full depth of your expertise and a tool acting as a substitute would degrade the result. Knowing which skills those are is how you protect the work's quality.

Skill gaps as AI candidates

The areas where your skills are thin but the task still needs to be done are strong candidates for AI support. A tool can compensate for a skill gap better than it can add nuance to deep expertise.

What to feed a tool versus what to do yourself

Skills determine the boundary between what you should do and what you should delegate. A clear picture of your skills makes that boundary easier to draw and easier to explain to others.

Questions to explore

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

  1. What are the three things you do better than most people you work with?

  2. Where do you find yourself having to explain things to a tool that you wish it already understood?

  3. What skills do you rely on every week that you have never written down or documented anywhere?

  4. In which areas of your work would a tool's output need the most correction from you?

  5. Are there areas where your skills are thinner than the work requires, and where does that create friction?

Readiness traps

  • Self-assessed skill levels are often optimistic. The skills that most need documenting are the ones you use without thinking, which are easy to understate.
  • Skills erode without practice. A skill listed on a profile from five years ago may not reflect what you can actually do today.
  • The most valuable skills in knowledge work are often the combinatorial ones (the ability to bridge two domains) and those are the hardest for AI to replicate. Make sure those are named explicitly.