Systems integration
Most AI projects fail on the data, not the model
Gartner puts the share of enterprise AI projects failing to deliver real business value at 85 percent. Buying a capable model before the underlying data is connected does not shorten that path, it lengthens it.
A model reads what you give it. If the records it reads are scattered across systems that were never designed to agree with each other, it will produce answers that are confidently inconsistent — and it will do so without raising anything that looks like an error.
The instinct is to fix this at the model layer, with a better model or a longer prompt. The constraint is almost never there. Getting to complete, current, trustworthy data is integration work, which is why we quote the two pillars separately and deliver them as one job.
Where scattered data actually hurts
- The same entity exists three times under three identifiers, so any aggregate is wrong and nothing flags it.
- The authoritative copy is ambiguous, so two answers are both defensible and only one is right.
- Currency is unknown: the record exists but nobody can say when it was last true.
- Access rules lived in the source system and do not survive the copy into a shared layer.
The order that works
Map what is actually running before designing anything. On plant and port work that usually means tracing signals across equipment that was never built to talk to anything else, and recording it as found rather than as drawn.
Then decide what to decommission, what to move, and what stays on premise because it has to. Only then is there a defensible answer to whether the data can carry the decision you were planning to automate — which is the L2 Readiness layer, and the one most proposals skip.