One-Liner
A database of AI project failure patterns to help German mid-market CTOs understand why their AI pilots keep failing before hiring another consultant.
AI Thinking Process
Only 12% of German companies AI-ready; 80-85% AI project failure rate in German Mittelstand; each failed POC costs €80-150K.
CTO of German mid-market manufacturer failed 3 AI POCs in 18 months. No way to know if failure was bad vendor, bad use case, or bad internal readiness. Idea: AI failure intelligence service.
G004 structural adoption barrier (double weight): consulting firms benefit from failure. Cold start: failure data is private and embarrassing. Buying behavior routes to 'hire better consultant' not intelligence platform.
KILLED — G004 structural adoption barrier (double weight). Cold start: no aggregation mechanism for failure data. Buying behavior structurally routes to consulting.
Readiness assessment pivot failed — CFOs route to existing consulting relationships. Fundamental kill confirmed.
Kill Reason
AI consulting firms (McKinsey Digital, Accenture, BCG Gamma, codecentric, Alexander Thamm) profit from repeated Mittelstand AI failures and control the advisory relationship — they have no incentive to create a failure pattern database. Cold start is insurmountable: AI POC failures are embarrassing and confidential with no aggregation mechanism. When a CTO's project fails, they reach for a better consultant, not an intelligence platform.
Risk Analysis
Risk analysis available for latest engine ideas.
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