Describe an AI workflow, tool, or transformation idea. The engine analyzes it against a weighted investment framework and current marketplace research, then returns whether it is worth the investment, the transformation upside it opens, and where it is most likely to fail.
Estimated gain in speed, capacity, or accuracy for the affected workflow once adopted. Directional, not a guarantee.
Why this score
Pros
Cons
Recommendation
Stress test
Market reality, by the data
80%
of AI projects fail to deliver intended value (RAND, 2,400+ initiatives)
95%
of GenAI pilots show no measurable return (MIT Project NANDA, 2025)
42%
of companies abandoned most AI initiatives in 2025, up from 17% (S&P Global)
13%
of U.S. workers received any employer AI training (SurveyMonkey, 2026)
The research is consistent: failure is an enablement problem, not a technology problem. Prosci attributes roughly 38% of AI implementation difficulty to user proficiency versus about 16% to technical issues. This engine weights readiness and adoption risk accordingly.
Data sources: RAND Corporation (2,400+ enterprise AI initiatives); MIT Project NANDA, The GenAI Divide (2025); S&P Global Market Intelligence (2025); Prosci change-management research; SurveyMonkey Workplace AI (2026); McKinsey Global Institute, The Economic Potential of Generative AI; and category benchmarks from Helperfy, Freshworks, ABBYY, UiPath, Forrester, and Rossum (2025 to 2026). Figures reflect published research; the engine's scores and improvement estimates are directional decision inputs, not guarantees.