Executive summary
85%
of enterprise AI projects fail to deliver real business value, per Gartner
L2
the Readiness layer in our framework, which most proposals skip
Key conclusions
- When scattered records contradict each other, a model gives inconsistent answers with confidence, and no error appears.
- Producing complete, current and trustworthy data is integration work. That is why we quote systems integration and AI workflow automation separately and deliver them as one job.
- Map what is actually running before designing anything. Then decide what to decommission, what to move and what to keep on premises. Only then can anyone say whether the data can support the decision.
A model reads whatever data you give it. If its records are spread across systems that were never designed to agree with each other, its answers will be inconsistent but confident. The model will also give no sign that anything is wrong.
The usual response is to fix this at the model layer, with a better model or a longer prompt. The constraint is almost never there. Producing complete, current and trustworthy data is integration work. That is why we quote systems integration and AI workflow automation separately and deliver them as one job. They are two of our four service lines; robotics and assurance are the other two.
Scattered data causes four kinds of error
- The same entity exists three times under three identifiers, so every total is wrong and nothing flags the error.
- It is unclear which copy is authoritative, so two answers can both be defended although only one is right.
- Nobody knows how current a record is: it exists, but nobody can say when it was last accurate.
- Access rules were enforced in the source system and are lost when the data is copied into a shared layer.
Map the systems first, then decide what to keep, move or retire
Map what is actually running before designing anything. On plant and port projects, this usually means tracing signals across equipment that was never built to connect to other systems. We record what we find, even where it differs from the drawings.
Then decide what to decommission, what to move and what must stay on premises. Only then can anyone give a defensible answer on whether the data can support the decision you plan to automate. That is the L2 Readiness layer, and most proposals skip it.
