A practical framework for
AI adoption that works.
Most AI rollouts fail because they train everyone the same way. This framework measures individual readiness first, then delivers discipline-specific skill building that sticks.
The three-phase adoption framework
Effective AI adoption isn't a one-time announcement or a company-wide email. It's a cycle of assessment, targeted learning, and measurable growth — repeated until AI collaboration becomes second nature across your organization.
Assess
Use AIRS to measure AI readiness across eight research-validated dimensions. Identify who's ready to experiment, who needs structured guidance, and who's resistant — so training dollars go where they matter.
Learn about AIRS →Train
Assign discipline-specific AI playbooks — not generic AI overviews. A lawyer learns legal research prompting. A nurse learns clinical reasoning prompting. Each guide has seven use cases and a 5-day practice plan.
Browse 80 guides →Measure
Re-assess with AIRS after training to quantify shifts in readiness, confidence, and adoption patterns. Track which dimensions improved and which need further intervention.
Take AIRS →Why most AI training doesn't stick
Typical approach
- One-size-fits-all "intro to AI" webinar
- Generic prompt tips ("be specific!")
- No measurement of starting readiness
- No follow-up or practice structure
- Success = attendance count
This framework
- Individual readiness assessment (AIRS)
- Discipline-specific prompt patterns
- Baseline → training → re-assessment cycle
- 5-day practice plans + month-long skill building
- Success = measurable readiness improvement
Coverage across 80 professional disciplines
From software engineering to nursing, from executive strategy to skilled trades — the framework provides tailored content for virtually every knowledge worker role. Each guide was tested across real professional workflows and validated for cross-platform compatibility (ChatGPT, Claude, Copilot, Gemini).
Technology
12 guides — Developers, Data, Security, SRE, Architecture, Game Dev, Open Source
Business
12 guides — Executives, Finance, Sales, Marketing, PM, HR, Real Estate, Consulting
Creative
7 guides — Writers, Designers, Podcasters, Comedians, Content Creators, Visual Storytellers
Education
5 guides — Students, Teachers, Researchers, Grant Writers, Early Childhood
Healthcare & Allied Health
13 guides — Nursing, Dental, Respiratory, Radiology, Pharmacy, EMT, Veterinary
Trades & Technical
11 guides — Automotive, Aviation, CAD, Cybersecurity, Construction, Mechatronics
Built on research, not marketing
This framework was developed by Fabio Correa, Director of Advanced Analytics & Data Science at Microsoft and DBA candidate studying AI capability development in knowledge work.
The AIRS assessment draws on established technology adoption theory (TAM, UTAUT, DOI) adapted specifically for generative AI. The AI playbooks grew from longitudinal case study research on how professionals actually develop AI collaboration skills over time.
Frequently asked questions
How much does the framework cost?
The AIRS individual assessment and all 80 playbooks are free to use at your own pace. There are no paid tiers or licensing offers.
Can I use this for a small team?
Absolutely. Have each person take AIRS, review their results together, then assign the playbook that matches each person's role. The 5-day practice plans require 25–35 minutes per day.
What makes this different from vendor-specific AI training?
This framework is platform-agnostic. The prompts work on ChatGPT, Claude, Copilot, and Gemini. The assessment measures psychological readiness, not product familiarity. When a new AI tool launches, the skills still apply.
How do I measure ROI on AI training?
Use the Assess → Train → Measure cycle. AIRS provides numerical scores across eight dimensions. Compare pre- and post-training scores to quantify shifts in readiness, confidence, and adoption intent.