Promising pilots that generate interest and learning but never make it into everyday operations
AI chosen by technical curiosity rather than where it creates disproportionate business value
Unclear ownership, decision rights and success criteria spread across teams
Data quality, integration and platform readiness quietly slowing every initiative down
Rising expectations with no shared path from experiment to scaled, measurable impact
AI initiatives exist, but value creation doesn’t scale
This is where many AI initiatives quietly stall.
Pilots and proof-of-concepts move forward, teams learn and momentum builds, yet few initiatives make it into everyday operations. Instead of becoming a shared capability, AI remains a growing collection of disconnected experiments with unclear ownership and limited reach.
The most future-proof skill today is the ability to learn continuously and to unlearn what no longer creates value. The pace at which AI evolves challenges us to learn and unlearn fast.
Lasse Girs Head of AI Transformation, Solita
How this typically looks like
Why this becomes a problem
Without a clear operating model, governance and production-ready foundations, AI can’t scale safely or cost-effectively. Pilots multiply, but the value stays trapped in isolated experiments while complexity and cost keep rising. And when business users can’t trust the outputs, they stop using them, so the ROI never lands.
Over time the gap widens between the AI a company talks about and the AI that actually changes how decisions get made and work gets done. What was meant to become a capability quietly turns into the organisation’s most expensive experiment.
What we recommend
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A shared, business-led view of where AI creates real and measurable value
Focused on the few moments where AI impact is disproportionate, not spread thin across every idea.
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Clear ownership, governance and decision-making for AI initiatives
Initiatives have a path from experiment to production instead of stalling between teams.
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Production-ready AI and data platforms, including MLOps and lifecycle management
Including MLOps, lifecycle management and the data foundations that let AI run reliably, not just demo well.
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Responsible, secure and cost-aware ways to embed AI into daily operations and decision-making
With trust and traceability built in from day one.
How can we help
What this looks like in practice
Fimea
New AI application to support reporting of adverse reactions
“ “We product data on adverse reactions for all of the EU. Now, that data quality is higher and more accurate. This is important, as nearly everything about pharmaceutical safety is based on high-quality data.
Kari Salmela Researcher and Product Owner of the Register, Fimea
Let’s talk