AI does not need to know
more than your company.It needs to know how to work with what your company knows.

To implement AI is to incorporate it into real processes with objectives, responsible people, authorized knowledge, appropriate tools and control criteria. It includes diagnosis, prioritization, training, pilot design, testing, adoption and maintenance; it does not come down to activating a corporate chat.

It makes sense when there are repeated processes, information available, a shared need, and someone who can validate what a good outcome is. Also when the organization wants to reduce dispersal and turn individual tests into a common capability.

It is necessary to inventory use cases, volume, impact, data, risks, existing tools and people involved. Then choose a driver small enough to learn, but relevant enough for the team to assess whether it brings a real improvement.

The AI can search, summarize, compare, classify, draft, propose and, in bounded circuits, activate tools. Its function must be risk-adjusted: attending is different from executing an action that affects customers, money or rights.

With a baseline and indicators such as cycle time, corrections, quality, volume solved, satisfaction, incidences and adoption. Saving is only real if the time released becomes a job of greater value or better service.