Most enterprises have an AI strategy document. Few have AI in meaningful production. What's the practical gap — and how do you close it without another round of pilots that never leave the innovation lab?
A pattern we see across the Balkans and wider EU mid-market: enterprises spend 6–12 months running AI pilots, then another 6 months deciding which to productionise, then fail to productionise most of them. The pilots weren't wrong — they were well-run and often demonstrated genuine value. What was wrong was the gap between pilot and production.
That gap is made up of data governance that wasn't in place, MLOps capability that wasn't built, AI risk frameworks that hadn't been written, and operational readiness that wasn't there. The pilot lived in a sandbox; production required an entirely different set of capabilities.
The single most important input to an AI programme is the presence of a named operational owner — the person whose day job materially changes if the AI use-case succeeds. Without that owner, no amount of technical excellence will move the AI into the operational flow. With that owner, even technically modest AI can deliver material value because the operating model bends around it.
Practical rule: if you cannot name the person whose P&L or operational metric moves when this AI succeeds, you do not have a use case yet. You have an idea.
Every serious AI engagement we have delivered has required data-foundation work — often as the critical path. Data that sits in operational systems, formatted for operational purposes, is rarely AI-ready out of the box. The semantic layer, lineage, quality measurement, and governance frameworks required for production AI are substantial foundations.
Organisations that have invested here — often in the context of data-warehouse or lakehouse programmes — reach production AI much faster than those that have not. It is unglamorous work, but it is where the ROI compounds.
Research shows most deployed models drift below acceptable performance within 12 months. MLOps — the engineering and operational discipline of keeping models performing in production — is the thing that prevents this. Feature stores, model registries, drift monitoring, controlled rollouts, and model-risk documentation are not optional extras; they are what separates AI that works for a year from AI that works for five.
The EU AI Act is coming into force in phases through 2026–2027. High-risk AI systems face substantial compliance obligations. Even if your current AI use cases are not high-risk, the odds that some future use case will be are high.
The practical move is to establish an AI governance framework now — model risk classification, human oversight, documentation standards, bias and fairness testing — applied consistently to every model, whether legally required or not. Having the framework in place is a strategic asset; building it under pressure when the first regulated use case arrives is not.
A practical sequencing we would recommend — and that maps to how we run AI-readiness engagements:
By the end of six months, two or three models are in production, the data and MLOps foundations support further development, and the governance posture is credible. What started as "we need an AI strategy" has become "we have production AI that we can extend."
AI readiness is less about technology than about operational commitment. The organisations winning with AI are not the ones with the best models — they're the ones with named business owners, governed data, production-ready MLOps, and an AI governance framework that makes decisions defensible. Each of those is unglamorous work. Each pays compounding dividends.