The AI Readiness Scorecard
Ten questions built from the reasons AI projects actually fail — not from how advanced your technology is. You'll get a score out of 100 and the biggest risk in your way. No email needed to see it.
0/100
0 more in your report
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Where to point a first pilot given how you answered, plus every other risk the scorecard flagged. We'll show it here and send you a copy.
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This is a readiness indicator built from published research on why AI projects succeed and fail — not a prediction of your return. It scores how ready the groundwork is, which is the part most businesses skip.
The things that decide it are rarely technical
It weights problem definition and people most heavily, because that's where the research puts the failure. A low score usually means a fortnight of definition work, not a project.
Cause #1
of AI project failure is miscommunication about the problem to solve — the business misstates the problem, so the system optimises the wrong thing. RAND interviewed 65 experienced data scientists and ML engineers, and reports that more than 80% of AI projects fail.
70%
of what determines an AI transformation is people and process. Algorithms account for 10%, technology and data for 20%.
7×
more likely to meet their objectives: initiatives with excellent change management, versus those with poor change management.