Two-thirds of AI projects never ship. The reasons are predictable, repeatable, and almost entirely avoidable.
Methodology
280 AI projects tracked from kickoff through either production launch or formal cancellation. 130 launched, 150 cancelled. Project sizes ranged from $20K to $4M.
Failure rate by category
- Internal team builds: 71% never shipped
- Big-4 consulting builds: 64% never shipped
- Boutique agency builds: 38% never shipped
- Vendor-platform deployments: 41% never shipped
Top failure patterns
- No executive sponsor (38% of failures): project stalled in committee, died after 6-9 months
- Scope creep (22%): initial 6-week scope ballooned to 9 months, killed at budget review
- Wrong use case (18%): AI deployed where rules + data would have worked better
- Compliance block (11%): security/legal review killed it post-build
- Cost explosion (7%): production traffic costs scaled faster than value
- Team change (4%): champion left, project orphaned
What winners did differently
- Named executive sponsor accountable for outcome
- Tight initial scope (single use case, single team)
- Pre-launch eval framework
- Compliance + security looped in during scoping, not after build
- Per-feature cost budgets set before launch
- Clear success metric measured weekly
Pre-launch checklist
- Named executive sponsor? Y/N
- Single use case in initial scope? Y/N
- Eval framework written? Y/N
- Compliance reviewed? Y/N
- Cost budget set? Y/N
- Success metric defined? Y/N
If you can't check all six, the project has a 64-71% failure rate before you start. We can help: book a discovery call.
Cite as: Creative Genius (2026). AI Implementation Failure Rate Study 2026. Retrieved from creativegenius.ai/research/ai-implementation-failure-rate-study-2026