Why enterprise AI initiatives stall between
pilot and production
AI capability is advancing every quarter.
Enterprise decision velocity, in most large organisations, has not changed in a decade.
Evaluating a new technology. Clearing governance. Reaching a commitment. The process still runs at the same speed it always did, while the market moves faster each quarter.
The gap between these two speeds is widening. This report names it, measures it, and explains what it costs.
THE PATTERN
A consistent pattern across enterprise AI programmes
Most organisations are running AI experiments. Far fewer are running AI in production.
MIT’s 2025 GenAI Divide report, based on 150 interviews and 300 public deployments, found that 60% of organisations evaluate AI tools. 20% reach pilot. 5% reach production.
The drop-off is not random, and it is not a technology problem. The models work. The pilots succeed. What fails is the decision infrastructure around them.
60%
Evaluate AI
20%
Reach Pilot
5%
Reach Production
WHO IS THIS FOR?
Written for people responsible for AI delivery, not just AI strategy
If you sit between AI ambition and production reality, responsible for making programmes move, not just approving them, this report is written for you.
That includes:
CIOs and CTOs
Owning enterprise technology strategy
Chief Data and AI Officers
Leading adoption programmes
Heads of Innovation
Managing portfolios of vendor evaluations
Enterprise Architects and Procurement
Navigating governance and onboarding
If your organisation is running pilots that aren’t reaching production, this report will likely describe exactly why.
WHAT IT COVERS
What the report explains
The paper introduces Decision Latency as a structural condition with four measurable dimensions: Access, Evaluation, Governance, and Commitment Latency. Each compounds independently.
It explains:
- Why the constraint in enterprise AI adoption is decision velocity, not model capability.
- Why vendor evaluation processes create structural delay that process improvement alone cannot fix.
- What the hidden financial cost of slow evaluation cycles is, and why it rarely appears on any dashboard.
- And it provides a diagnostic framework that allows any technology leader to measure their own Decision Latency in a single working session.
DIAGNOSTIC
Where does your organisation sit?
The report includes a practical diagnostic built around the four dimensions of Decision Latency.
Across most Tier 1 financial institutions, the total time from pilot success to production commitment ranges from 9 to 18 months.
Organisations with dedicated evaluation infrastructure operate at 6 to 10 weeks.
That gap – measured in months, not weeks – is the competitive variable that determines whether AI investments generate value or generate activity.
The organisations that capture value from AI will not be those with the best models. They will be those with the fastest, most governed path from evaluation to production.
THE AUTHOR
Karan Jain is CEO of NayaOne, a company building Vendor Delivery Infrastructure for global financial enterprises. He works with enterprise technology leaders across the UK, US, and Middle East.
