The management system for AI adoption

Turn how your team works with AI into a capability you own.

AI Hub captures how your people actually use AI — every tool, every step — then turns the best of it into reusable skills, living wikis, and measurable adoption. The knowledge stops dying in closed tabs.

Evidence-based not surveys Local-first capture Every major AI tool

Manifesto

AI adoption isn't a project you finish. It's a culture you compound.

Your competitors are running pilots. The advantage goes to the company where everyone gets a little better with AI every day — and where that improvement is captured, shared, and measured instead of lost.

Most companies run a few AI projects. The real value is in the thousand small ways your people already touch AI every day — today invisible, uncaptured, and evaporating. AI Hub turns that scattered individual use into a compounding, org-wide capability — steered from the top, fed from the ground, and proven with evidence.

The problem

You're already paying for AI. You just can't see what you're getting.

Every dollar of AI productivity today is rented from the model vendor — and most of it leaks away before it ever becomes something your organization owns.

Spend you can't trace

AI budgets climb while ROI stays a black box. Leadership can't steer what it can't see — and surveys give them nothing to steer with.

$ in · ? out

Knowledge dies in closed tabs

The best prompt, the workflow that saved a day, the agent that just works — used once, then gone. None of it accrues into something the org keeps.

used once · lost forever

Power users can't teach at scale

A handful of people drive most of the leverage. Their know-how is tacit, undocumented, and impossible to spread through town halls and posters.

~10% of the value captured
The big idea

One flywheel. Run it org-wide, every day — and adoption compounds.

Each stage answers a need, not a feature. Run continuously, the flywheel is the adoption engine — with a steering wheel on top and a dashboard underneath.

01

Track ROI + who's good

Make the invisible visible. Auto-capture how AI is actually used across every tool — then surface who's getting real leverage.

02

Build Owned assets

Turn tacit know-how into owned assets. Package the workflows that work into reusable skills your org keeps — not rents.

03

Share Close the gap

Diffuse power-user skill to everyone. A catalog with one-click install and contributor attribution moves the whole team up.

04

Learn Compound it

Context that gets smarter. Living wikis, certifications, and evals so the organization's AI fluency keeps building on itself.

Evidence, not surveys

Find your power users. Bottle what they do. Prove the rest of the org caught up.

Most "AI adoption" programs ask people how they feel. That's a vanity metric — gameable, lagging, unprovable. AI Hub measures the work itself.

Everyone elseSurvey-based
"Do you feel more productive?"
Self-reported, gameable, lagging
"73% feel AI helps" — so what?
Can't prove diffusion ever worked
AI HubEvidence-based
Measured from real sessions: tools, skills, work-type, output
Behavioral, objective, continuous
"A handful of people drive most of the leverage — here's exactly what they do"
Shows the metric lift after a skill spreads

The evidence loop — the engine

01
Detect
Telemetry surfaces who's actually getting leverage — not who says so.
02
Decode
What they do differently: which skills, tools, workflows, context.
03
Codify
Package it into a shareable skill or workflow the org owns.
04
Diffuse
Push it out to everyone else — one-click install, attributed.
05
Measure
Did the rest of the org's numbers move? Evidence at both ends.

A survey can't close that loop — it can't prove the lift. AI Hub can. That's the demo that sells.

The control loop

A steering wheel for AI adoption — not just a dashboard.

The axis isn't top-down vs bottom-up. A real practice needs both, joined by evidence. Leadership sets direction; adoption flows up from how people actually work; the two meet in a continuous loop: measure → evaluate → adjust.

PDCA
Leadership action
AI Hub surface
Plan
Set AI direction & targets — which teams, which work
Goals, tournament rules, org structure
Do
Teams work with AI, every day
Auto-capture — CLI + browser extension
Check
See real adoption metrics by team / chapter
Dashboards, adoption pyramid, org reports
Act
Evaluate, adjust, reward, retrain
Tournament incentives, certs, skill diffusion
Top-down · leadership steers
Set direction → targets → evaluate → incentivize
Evidence — the shared truth
Both sides act on the same objective data
Bottom-up · ground truth
Real AI use captured → power users surface → skills emerge
Capabilities

Everything hangs off a stage of the flywheel.

Grounded in what ships today — across capture, build, share, learn, and the steering layer that sits on top.

Trackmake it visible
  • Auto-capture from IDEs & CLIsClaude Code · Cursor · Codex · Gemini
  • One-click capture from web AIsChatGPT · Claude · Gemini · Perplexity · NotebookLM
  • Enrichment pipelinetokens · tools · MCP · skills · work-type · project
Buildown the asset
  • Publish reusable agent skills from any repoGitHub indexer + skill builder
  • Contribute know-how to the Knowledge Hubwikis · playbooks · prompt libraries
Shareclose the gap
  • Skills Catalog with install commandsgit-blame contributor attribution
  • Share org knowledge via agent-based wikisearchable · always current · ask-in-context
Learncompound it
  • Codebase Wiki — auto-generatedinterlinked Obsidian vault · Mermaid diagrams
Steerleadership layer
  • Dashboards, adoption pyramid, org reportsadmin · compliance
  • AI Tournament — gamified incentives5 weighted metrics
Proof · live deployment

Adoption you can watch move — by tier, by work-type, over time.

Not a survey. These are real, anonymized aggregates pulled read-only from a live AI Hub deployment — percentages are exact, every figure is group-level only.

0
AI sessions
0
people
0
reusable skills
0
work-types
0
repos indexed

01 Adoption tiers

There's a gap — and room
Power500+ sessions13 · 22%
Regular150–499 sessions18 · 30%
Light30–149 sessions22 · 37%
Minimal1–29 sessions7 · 12%

02 Power-user concentration

Find your power users
Top 10%of users55.5%
Top 20%of users72.1%
Top 50%of users91.9%
Bottom 50%of users8.1%

03 Where AI actually shows up — 19 work-types

Every step, not a few projects
Debug20.2%
Refactor4.1%
Develop15.0%
Code review3.8%
Configuration9.3%
Testing3.1%
Onboarding8.8%
Documentation2.9%
Fix bug7.6%
Design2.7%
Research7.4%
Deploy1.4%
Requirements5.5%
Learning1.3%
Planning4.6%
DevOps1.3%
Data analysis0.7%
Pre-sale0.1%
Interview0.04%

04 Adoption momentum — weekly active users

Capability compounds
AI Tournament
Five weighted metrics encode the philosophy: Use → Build → Share → Learn
35%
Engagement
25%
Adoption
20%
Creation
10%
Sharing
10%
Learning
Competition progress · 8-week season Composite tournament score · demo, team-level only
Integrations & trust

Meets your stack. Earns your team's trust.

Connects to the tools your people already use, and captures in a way both executives and the workforce can feel good about — steering, not policing.

Captures every major AI tool

Claude
ChatGPT
Gemini
Perplexity
NotebookLM
Cursor
Claude Code
GitHub

Plugs into your org

MS365 SSO
Org chapters
MCP servers
Project attribution

Local-first, user-controlled capture

Capture runs on the user's machine, under their control. The workforce wins too — it spotlights what works, never catches laggards.

PII & secret scanning

Conversations are scanned for personal data and secrets before anything is stored — no credentials, no surprises.

Dedup & versioning built in

Every captured session is de-duplicated and versioned, so the record stays clean as skills evolve over time.

Stop renting AI productivity. Start compounding it.

See your real AI adoption — and turn it into capability you own. Book a 30-minute walkthrough and we'll show you the loop on live, anonymized data.