AI Readiness Calculator

A single-page web tool that measures how ready employees are to adopt AI in their daily work, and turns the results into personal and team-level action plans.

Live site: https://sghatge.github.io/ai-readiness-calculator/
Overview: see ABOUT.md for what the tool is, who it's for and how to get started.

What it does

Section	Purpose
Assessment	Profile (anonymous by default) + 31 questions across 10 dimensions (~6 min). Produces a 0–100 score, readiness level, dimension scores, a radar profile, strengths, top 3 barriers, critical-gap flags, a 30-day plan, role-based use cases, suggested training, CSV export and Save & add to team roster.
AI profile	One of ten score patterns (e.g. Eager Explorer, Blocked Enthusiast, Fast Mover, Loose Guardrails) with a focus area. A conversation starter, not a diagnosis.
One-page report & share image	Print-ready report (save as PDF) and a 1200×630 PNG card with score, level, profile and radar. Never includes a name.
What-if simulator	Sliders per dimension with locks; projected score and level update live, the governance cap is respected, up to 3 saved scenarios.
History	Every completed run is saved in the browser: trend chart, labels, two-run radar overlay with change table, JSON export/import.
Workshop	Facilitator view with session name, join QR, paste-in participant codes, team average radar, Low/Mid/High spread per dimension, auto-generated discussion cards (copy, print, CSV) and projector mode.
Team report	Manager view combining responses saved in this browser with 20 optional sample employees: average score, level distribution, department comparison, dimension averages, highest-risk gaps, recommended interventions, a 90-day roadmap and a CSV-exportable table.
Method	Research sources, weights and rationale, full question bank with option points, Google Sheets formulas, level definitions, rollout plan and limitations.

Scoring model

1. Each answer → 1–5 (reverse-scored items: 6 − answer).
2. Item score = (value − 1) / 4 × 100.
3. Dimension score = average of its items.
4. Overall = Σ (dimension score × weight) / 100.
5. Governance cap: if the data-handling risk flag is raised, the overall score is capped at 59 (top of AI Aware) until the gap is fixed.

Dimension	Framework	Weight
AI awareness	ADKAR Awareness	8%
Attitude and willingness	ADKAR Desire	10%
Practical AI skills	ADKAR Knowledge	12%
Prompting and verification	ADKAR Ability	12%
Role and use-case fit	Role fit	10%
Data privacy and responsible AI	OECD · NIST AI RMF	14%
Access and organizational support	ADKAR Reinforcement	10%
Confidence without over-reliance	OECD critical thinking	8%
Applying AI to real work	ADKAR Ability	10%
Continuous learning	ADKAR Reinforcement	6%

Levels: 0–39 AI Beginner · 40–59 AI Aware · 60–74 AI Practitioner · 75–89 AI Confident · 90–100 AI Champion.

Critical flags (override the level): data-handling risk, no approved tool access, verification gap, over-reliance risk.

Privacy

• Workshop codes (AIR1-…) carry only an anonymous ID, department, the 10 dimension scores and flag keys. Participants paste them into the meeting chat; the facilitator pastes them into the Workshop tab. No server is involved.

• History, scenarios and workshop participants are kept in browser local storage only. Clearing browser data deletes them; export a backup from the History tab.

• Everything runs in the browser. No answers are ever sent to a server. Users can download their result as CSV.

• Save & add to team roster stores results in that browser's local storage only (useful on a shared workshop laptop). Clear it from the Team tab.

• The team report uses fictional sample data (IDs E01–E20).

• To collect real responses, use Google Forms → Sheets → Looker Studio (formulas are in the Method tab), and only report groups of 5 or more.

Deploy on GitHub Pages

1. Create a public repository named ai-readiness-calculator.
2. Upload index.html and README.md to the repository root and commit.
3. Settings → Pages → Branch: main, folder / (root) → Save.
4. After about a minute the site is live at the URL above.

Third-party code

QR codes use qrcode-generator 1.4.4 by Kazuhiko Arase (MIT), inlined in index.html.

Customize

All content lives in plain data arrays at the top of the <script> in index.html:

• DIMS: questions, weights, barriers, training and 30-day actions
• LEVELS: score bands and descriptions
• USECASES: role-based use cases by department
• SAMPLE: team dashboard data (replace with your exported results)
• TEAM_FLAGS: thresholds for team-level risk flags
• personaOf(): profile rules
• TALK: discussion-card questions per dimension

Sources

• Prosci: How businesses assess readiness for AI adoption
• Prosci: ADKAR for AI adoption
• OECD: Building an AI-ready public workforce
• MIT Sloan Management Review: Getting your employees ready for work in the age of AI
• Microsoft & LinkedIn: 2024 Work Trend Index
• NIST AI Risk Management Framework

Limitations

Self-reported answers, expert-judgement weights (re-check them once you have 50+ responses), only 3–4 items per dimension, and scenario answers people can learn over time. See the Method tab for mitigations.

Feedback

Report a bug or suggest an improvement via GitHub Issues. Issues are public, so please don't include personal data.

License

MIT © 2026 Sangram (@sghatge). Bundled qrcode-generator is also MIT-licensed.