Make your business ready for useful AI.

Pick work the team can describe and recognize when it is complete. Reports, intake, follow-up and onboarding are easier to evaluate than a company-wide mandate to use AI.

Put it into practiceWhat repeats every week and still takes too much effort?

Identify where the current instructions, records and examples live. Assign an owner to resolve conflicting versions before an agent depends on them.

Put it into practiceWhere does the team go for the current answer?

Document what a good result looks like, what must never happen and when a person takes over. The knowledge base should support a real job, not simply collect documents.

Put it into practiceWhich decisions require judgment rather than a repeatable rule?

Walk through normal cases and awkward exceptions with the team. The person closest to the task often knows where a promising automation would break.

Put it into practiceWhat would stop working if you were away for two weeks?

Define its inputs, outputs, permissions and reviewer. Start with one role that can be checked, rather than a general-purpose agent with unclear responsibility.

Put it into practiceWhat is this system responsible for, and where does its responsibility end?

Bring the workflow owner, representative examples and a list of tools. Agree access and success criteria before connecting anything or promising a result.

Put it into practiceWhich evidence would help us decide whether to build?