The Head of AI problem
AI adoption usually looks healthy before it looks governed. Seats go up, messages go up, experiments multiply, and leaders see demos everywhere. But the best workflows still live in private chats. That means the company has activity without a reusable system of record for how AI-assisted work should happen.
The Head of AI needs a way to preserve bottom-up discovery while creating a path from private usage to approved company capability.
What knacks gives you
See which repeated AI workflows are emerging across teams before they become invisible standards.
Route skill candidates to the right owner for usefulness, source quality, access, and business fit.
Publish approved skills with owners, examples, permissions, version history, and usage guidance.
Track reuse, stale context, duplicate patterns, sensitive access, and skills that need review.
First 30 days
Revenue, support, operations, or customer success usually produces the fastest signal.
Look for prompts and context employees rebuild weekly: prep, triage, reporting, research, follow-up.
Choose workflows with clear owner, repeat frequency, low ambiguity, and visible quality benefit.
Track adoption, owner coverage, context access, quality review, and stale assumptions.
Best first skills
Metrics that matter
| Metric | Question it answers |
|---|---|
| Skill conversion | How much repeated private AI work became reviewed company skills? |
| Owner coverage | Does every shared workflow have an accountable owner? |
| Access clarity | Which skills touch sensitive data, sources, or systems? |
| Reuse | Which approved skills are used by which teams? |
| Drift | Which skills depend on stale product, policy, pricing, or market context? |
Build the AI workflow layer.
Book a walkthrough and we will map one team from private AI work to reviewed company skills.
Book a walkthrough