Europe is entering the AI production era, when budgets shift from experimentation to systems that run the business.
Most conversations about AI in enterprises still focus on models: which vendor, architecture or copilots to deploy. But the winners and losers in AI won’t be determined by algorithms; companies won’t hit a wall because their AI is underpowered. They’ll fail because of something far more mundane and far more expensive: the data systems feeding the models are not built for the level of operational trust needed by enterprises.
Good data is what makes AI predictable, auditable and safe to embed into real business workflows. And right now, AI is moving into production faster than companies are building the systems to keep it reliable. It’s not an AI problem. It’s an operational trust problem.
The signal is already visible. 95% of AI projects never make it to production and among those that do, reliability and monitoring are consistently cited as top blockers to scaling.
The misconception that will cost leaders the most
As pressure to deploy AI intensifies, many leaders reach for a “responsible” sequence: fix data quality first, then launch AI. In theory, that sounds prudent. In practice, it’s a trap.
You can spend months cleaning datasets and standardising definitions, only to discover in production that the “clean” data wasn’t the right data, or that the system is highly sensitive to a variable no one treated as critical. Until AI is live, most “data readiness” work is guesswork.
That’s because data quality isn’t absolute, it’s contextual. What counts as “good data” depends on the decision being automated. And those decisions are dynamic, evolving and tied to the real world.
A model forecasting demand in retail may look stable in testing until a major grocery supplier changes how it reports substitutions for out-of-stock products. What used to be recorded as “item unavailable” is now logged as “customer chose an alternative”.
On paper, nothing looks broken. Sales data still arrives on time and dashboards stay green. But the model is now learning the wrong lesson: it interprets stock-outs as healthy demand. Forecasts drift. Inventory decisions quietly degrade. The data is technically “clean,” but the meaning has changed, and the model has no way to tell the difference.
And the technology doesn’t fail with a dramatic crash — but slowly, quietly and at scale.
AI fails differently than BI
Most enterprises already live with imperfect data in analytics. Teams tolerate inconsistencies and business users learn to interpret dashboards cautiously. When business intelligence (BI) is wrong, it creates confusion. But when AI is wrong, it makes bad (and costly) decisions.
A model that misclassifies customers changes their experience immediately. A loyal shopper is suddenly treated as price-sensitive and starts receiving aggressive discounts instead of early-access offers. At the same time, a new customer is flagged as “high value” and gets priority service they haven’t earned.
Nothing appears broken, but the experience feels wrong on both sides. Revenue leaks quietly and trust erodes without a single error alert firing.
The most dangerous AI failures are rarely obvious. They don’t look like crashes or outages, but show up as slow, silent degradation. Many organisations simply don’t have the tooling to detect these shifts early — or the ability to trace what happened once the damage is done.
2026, the inflection point
The US market is already forcing AI into production hard and fast and the same wave is now arriving in Europe. Investment is accelerating accordingly: global AI spend is projected to reach $3.34tn by 2027. Nearly two-thirds of organisations say they haven’t even begun scaling AI enterprise-wide.
In 2026, boards will be asking new questions. Leaders will be asked not only whether they are deploying AI, but whether they can prove impact, defend decisions and manage risk.
As AI becomes embedded in business operations, leaders won’t only be asked what the model decided, but also whether the data feeding that decision was sound and how they can prove it.
Can you investigate anomalies? Can you trace a decision back to the data that shaped it? Can you assign accountability when systems break? That isn’t a future concern. It’s an operational reality.
So what should leaders do?
The answer is not to slow AI down until data quality is “fixed”. That approach delays value indefinitely and still won’t prevent production failures. Instead, business leaders must treat AI like infrastructure: launch, then build the operational feedback loops that keep systems reliable as reality changes.
In practical terms, this means defining ownership for each data product, making lineage and traceability non-negotiable, measuring stability over time (and not just point-in-time correctness), and building closed-loop improvement.
These aren’t “nice to have” governance ideas. They’re the operating foundations of AI at scale.
Enterprise AI won’t be won by whoever launches the fastest or trains the biggest model. It will be won by whoever builds AI systems they can trust in production, together with the feedback loops to keep them trustworthy even as the world changes.
