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
- Each answer → 1–5 (reverse-scored items:
6 − answer).
- Item score =
(value − 1) / 4 × 100.
- Dimension score = average of its items.
- Overall =
Σ (dimension score × weight) / 100.
- 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
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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.
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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.
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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
- Create a public repository named
ai-readiness-calculator.
- Upload
index.html and README.md to the repository root and commit.
- Settings → Pages → Branch:
main, folder / (root) → Save.
- 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 actionsLEVELS: score bands and descriptionsUSECASES: role-based use cases by departmentSAMPLE: team dashboard data (replace with your exported results)TEAM_FLAGS: thresholds for team-level risk flagspersonaOf(): profile rulesTALK: discussion-card questions per dimension
Sources
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.
