The five most common AI adoption mistakes

Over the past year, the failures we have seen repeat themselves.

1. Starting from a tool, not a process

Ask which process hurts most, not which AI product is trending. Tools change; processes do not.

2. Treating the POC as the finish line

A POC proves feasibility, not readiness. Production needs permissions, monitoring, error handling, and handover docs.

3. No baseline measurement

If you do not know how many minutes an order takes today, you cannot prove it got faster.

4. Ignoring data quality

The ceiling of a RAG system is set by document quality, not by the model.

5. Nobody owns operations

An AI system is not "install and forget". Without a named owner, in three months nobody dares touch it.