Why mid-market companies are getting AI wrong
The board wants AI. The infrastructure can't support it. A senior view on closing the gap.
The mid-market has an ambition problem in reverse: the ambition is fine, the foundation is not. Boards approve AI initiatives against an infrastructure that cannot serve clean data at the moment a decision is made.
The common failure pattern is a proof of concept built by a small team on exported data, praised in a steering committee, and then quietly abandoned when it meets identity, entitlement and integration reality.
Closing the gap is mostly sequencing, not spend. Choose one workflow that already has a cost per transaction. Instrument it. Fix only the data that workflow depends on. Then automate, with a human escalation path and a published error budget.
Companies that do this get a second and third use case almost for free, because the plumbing is now real. Companies that skip it buy the same proof of concept three times.