AI Readiness Data: what real use cases score before review
Every result here came from someone who ran the free AI Readiness Checklist on a real AI use case and chose to add it. No names, no answers, no email, no IP address: just the score, the stage totals, the data-risk tier, the verdict, and the type of organization. Nothing is shown until twenty results exist, because a smaller sample would be a story, not data.
Readiness by GROUND stage (average of 12)
The lowest bar is the stage most use cases skip. Reviewers stop on it first.
Data-risk tier
Verdict
Organization type
ERP Implementation Readiness Scan (opt-in results)
Not enough results yet. Publishes at twenty.
Resistance read
Project phase
Six ERP Failure Modes Self-Scan (opt-in results)
Not enough results yet. Publishes at twenty.
Band
Do AI readiness and ERP readiness move together?
When the same browser runs both the AI Readiness Checklist and the ERP Implementation Readiness Scan and opts in on both, the two scores are sent once as a pair. The server keeps only running sums (count, totals, cross-products), so a correlation can be computed and no individual pair exists anywhere. This is the public strand of a research question BBI is studying: whether technology readiness is one organizational property rather than a technology-specific one.
Not enough pairs yet. Publishes at twenty.
Measured from sums; a correlation is not a cause, and the people who run both tools are a self-selected group.
How the score is built (methodology)
The AI Readiness Checklist asks 25 questions, four or five per GROUND stage (Gather, Reveal, Order, Unify, Narrow, Deploy and Demonstrate). Each is answered on a 0 to 3 scale, where 3 is the state a reviewer would accept without conditions. The readiness score is the sum of the 24 scored answers divided by the maximum, as a percentage. One question (the most sensitive data the use case touches) does not score; it sets the data-risk tier (Low, Moderate, High, Governed review) together with whether the AI acts or recommends without a named human review. The verdict is Ready for review at 75 or above with no hard blocker, Fix first between 50 and 74 with no hard blocker, and Not yet below 50 or whenever a hard blocker is present (no accountable owner, regulated data with no approved handling path, no human review on an acting AI, no success metric, a high-risk case with no off switch).
The ERP Implementation Readiness Scan asks 20 questions in five areas named for the five themes of the founder's 2022 doctoral study (process and culture, adoption, leadership, change, method), scored 0 to 3 each, with a resistance sub-score built from the eight items that predict pushback (of 24). The Six ERP Failure Modes Self-Scan asks 12 questions, two per mode, scored 0 to 3, and the worst mode drags the band so a dirty table never reads as ready.
What is recorded here, by opt-in only: the score, the tier or band, the verdict, the stage or area totals, the organization type, and for the ERP scan the project phase and resistance sub-score. Nothing else: no answers, no use-case text, no name, no email, no IP address. Aggregates publish at twenty results so that no small sample is presented as a finding. The scoring model is BBI's own (Original); the aggregates, once published, are Measured from the submitted results and nothing more is inferred from them.
How to read this
- Measured: every number on this page is a count or an average of opt-in results. Nothing is modeled or projected.
- Not a market survey: the people who run a readiness checklist are, by definition, the ones thinking about readiness. Treat the figures as a view of that group, not of all organizations.
- Scoring: 25 questions, 0 to 3 each, across the six GROUND stages (Gather, Reveal, Order, Unify, Narrow, Deploy and Demonstrate); the risk tier comes from data sensitivity and whether the AI acts without human review.
- Reuse: cite as "BBI AI Readiness Data, bbiedge.com/ai-readiness-data, accessed [date]." The raw aggregate is at
/ai-readiness-data.json(JSON, CC BY 4.0).
Add your use case to the data
Run the checklist, tick the opt-in box under the brief, and your anonymous result joins the set. Then, if you want a score you can defend to a board, the AI Pre-Flight is BBI's fixed-fee diagnostic.