SUMMARY
Internal AI Readiness surveys measure intent. SignalScore measures operational throughput. It scrapes verifiable external signals such as job postings, GitHub contributions, engineering blogs and compresses them into a 0–100 score benchmarked against the signals of confirmed AI-forward firms.
AI expectations in non-engineering job postings correlate most strongly with genuine transformation. Legacy self-reporting frameworks remain necessary for auditing safety (Infra, security, governance).
Five Core Signals are scored 0-100
| Signal | Weight | Description |
|---|---|---|
| 1. AI/ML in IT | 25% | Building AI, not just mentioning it. |
| 2. Agentic Signals | 20% | The most forward-looking metric detecting movement towards agentic flows and optimized infrastructure in customer or supplier experience. |
| 3. Tool Stack | 20% | Identifies the "Tools of the Trade" using source-weighted detection. Tools found in GitHub repos or verified job postings carry higher weight than homepage mentions. |
| 4. Non-Eng Roles | 20% | A critical differentiator for leading versus operational, scanning for AI adoption in departments like Finance, HR, Marketing, Legal, Sales, Product, and Operations. High scores here indicate Systemic adoption (Gartner Level 4). |
| 5. AI Keywords | 15% | A three-tier sub-score: Results (e.g. AI friendly product documentation) > plans (e.g. hiring AI lead) > mentions. |
About
the project and
the methodology arrow_forward
Preview
1. Entry Form
2. Processing and Scoring
3. Scoreboard
4. Readiness Tiers
