AI pilots never reach production
AI pilots are working. Scaling them in mission‑critical systems is harder than expected.
Data readiness
Is our data reliable, structured and governed enough to support AI-driven decisions?
Security & governance
Backlogs, manual controls and compliance requirements are slowing down AI initiatives.
Legacy applications
Core systems remain critical, but they were never built for AI-driven speed.
Skills & adoption
Teams must learn new tools and new ways of working, without disrupting operations.
See what 8 global organisations
did differently without compromising control or security
Research from Public sector, Banking, Healthcare, Manufacturing, Insurance
Most AI Pilots Fail to Reach Production
Download 8 Success Stories: real world examples based on interviews with enterprise IT leaders across Europe, who are building apps and agents with AI !
Introduced AI through specific, measurable use cases in business units.
Modernised key applications step by step instead of via big‑bang rewrites.
Reduced the number of overlapping tools to improve governance.
Invested in enablement so teams could work confidently with new solutions.
What was the hardest part of scaling AI in their organisations?
Real-world examples
Backlog reduction, legacy modernisation, AI agents in workflows
Architecture & approach
How they structured platforms, governance, and integration.
Lessons learned
Decisions they would repeat, and what they would change.
For whom is this report relevant
For CIO or IT leaders
You are a CIO or IT leader responsible for applications and platforms.
Leaders Exploring AI
You are exploring or already running AI in production.
Innovating teams
You want to combine innovation with clear governance and risk control.
Governance Leaders
A governance system will prevent the proliferation of poorly designed or unwanted applications, ensuring adherence to best practices and quality standards.
