Why Data Quality Alone Won't Fix AI Hallucinations
Three out of four AI projects fail. And the reason isn’t bad data — it’s missing context.
We all know the statistic: 75% of AI projects don’t deliver value. The common assumption is that better data quality will solve the problem. It won’t. In this talk from the Data Innovation Summit 2026, One Data’s Christian Stadlmann explains why — using two powerful, real-world examples that expose a critical blind spot in how enterprises approach AI.
When good data leads to bad decisions.
A large manufacturer with over 10,000 employees built a predictive maintenance system. They collected vibration data from 1,500 machines, each equipped with 1,000 sensors. The data was accurate, fresh, and well-maintained. Yet the AI ordered millions in unnecessary spare parts — because it couldn’t distinguish machine vibrations caused by failures from those caused by routine cleaning cycles. The data was correct. The context was missing.
In a second example, a retailer built a pricing optimization model based on the correlation between discount days and increased footfall. The model was technically sound — but the real driver behind the customer surge was simultaneous TV advertising, not the discounts themselves. Again: right data, wrong conclusion.
The missing layer: context.
These failures share a root cause. The AI had access to accurate data — but not to the relationships, dependencies, and business logic that give data its meaning. What was missing was a semantic context layer that connects data assets, surfaces correlations, and ensures AI understands not just what the data says, but what it means.
Trust. Alignment. Automation.
Christian outlines three pillars that every enterprise must address to make AI work reliably. First, trusted data: quality is necessary, but not sufficient — you also need lineage, contracts, schemas, and governance. Second, alignment: business users and data teams must collaborate in a shared environment, not exchange specification documents. Third, automation: manual code conversion and pipeline management simply don’t scale in the age of AI agents.
The takeaway is clear:
Before you train your next model or deploy your next AI agent, make sure it has the context it needs to make the right decisions — not just the right data.