What AI capability really looks like in high-performing teams

Executive overview

Most organisations track AI adoption by counting licenses and training completions. These are vanity metrics — a team can use AI constantly and still get worse results. The real divide is between AI literacy (knowing how the tools work) and AI leverage (re-engineering workflows to unlock genuine gains).

Literacy is the foundation; leverage is the multiplier. Teams that stop at literacy plateau. Teams that reach leverage redesign how work is done — not just which step gets handed to AI.

The gap between high-performing and average AI teams is not tool access — it's workflow architecture.

Why adoption metrics mislead

• Tracking license usage or training completion doesn't measure output quality
• Teams overusing AI poorly produce "AI slop" — high volume, low value output
• Burying a team in mediocre AI output actively reduces productivity
• Minimal training ("here's the tool, go use it") is the worst intervention possible
• Most teams learn one use case and repeat it — like using a microwave only to defrost soup

Literacy vs. leverage

• Literacy: understanding what the tools are, how they differ, when to use them, how to avoid AI slop
• Different literacy needs exist for leaders (policy-setting) vs. knowledge workers (daily use)
• Leverage: embedding AI into the workday to solve problems faster or get better outcomes
• Leverage is not inserting AI into an existing process step — that's an interim stage
• True leverage means redesigning workflows entirely now that a new capability exists

What workflow re-engineering looks like in practice

• Old model: manager needs spreadsheet analysis → asks Susie → waits
• Interim model: give Susie AI so she works faster
• Leverage model: manager self-serves via an AI workflow built to deliver exactly the right output every time
• Hotel concierge example: an agent handles 80% of routine queries automatically; humans handle the remaining 20%
• The shift is from plugging AI into old steps to rethinking who needs what and going closer to the source

Building leverage at scale

• AI champions embedded across functional groups drive deeper capability than org-wide generic training
• Champions need two distinct skills: workflow mapping (what steps exist) and workflow re-imagination (what steps should exist)
• Most people lack both — they are not naturally workflow architects
• Minimum viable dose for any workplace benefit: 5 hours of training; comprehensive re-engineering programmes run up to 81 hours
• Stopping at basic training leaves most of the value on the table