You bought a standard system so the business could run on standard processes. Then every department asked for an exception — and got one. Three years on, you own a bespoke system nobody can upgrade, and the workarounds are back.

Three departments report on 'active customers' and get three different numbers, all technically correct, because each one is quietly using its own definition. That's not a data quality problem. It's a definitions problem wearing a data quality costume.

The CRM launch always looks like a success. Six months later, half the fields are empty and everyone's back on spreadsheets. The system didn't fail — the adoption did, and it failed after the training ended, not during it.

Most mid-market businesses have more data than they've ever had — and it changes fewer decisions than ever. The gap usually isn't the data.

Most mid-market AI projects disappoint for the same reason: the business automated a broken process instead of fixing it first.