Abstract
We surveyed 412 enterprise AI leaders across eight industries and three regions to answer a single question: what separates the AI projects that reach production from the ones that stall? The answer is not model quality, talent density, or compute budget. It is the operating substrate — eval harnesses, governance frames, deployment topology, and engagement cadence — that the 22% who ship invest in before they pick a model.
This report is the most rigorous independent benchmark of enterprise AI delivery published in 2026. We built it because the public discourse on enterprise AI continues to over-index on model selection and under-index on the engineering reality. The data tells a different story.
Methodology
Online survey conducted Q4 2025 – Q1 2026 with 412 enterprise AI leaders (VP+ titles) across U.S., U.K., and Middle East. Stratified across 8 industries. Confidence interval ±4.8% at 95%.
- Sample
- 412 enterprise AI leaders
Key findings
- 01
78% of enterprise AI projects never reach production
- 02
Median time-from-pilot-to-production is 14 months — 2x what teams predict
- 03
Eval harness presence at kickoff is the single strongest predictor of shipping
- 04
Self-reported AI cost per use case dropped 64% YoY for teams with model tiering
- 05
Healthcare leads industry maturity on governance; retail leads on velocity
Table of contents
- 01
Executive summary
- 02
Methodology and respondent demographics
- 03
The pilot-to-production gap
- 04
Governance and evaluation maturity
- 05
Cost benchmarks and optimization patterns
- 06
Industry deep-dives: financial services, healthcare, retail, manufacturing
- 07
What separates the 22% that ship
- 08
Forward outlook: 2026 – 2027
- 09
Appendix: full survey instrument