Is your business ready for an AI integration?

Define a task small enough to evaluate

Replace a broad goal such as add AI to support with a specific workflow: draft a response from approved help material for a support agent to review. The narrower task gives you clearer inputs, output expectations, and accountability.

Identify which parts require judgment and which are deterministic. A rule, search function, or conventional automation may be the better option for some steps. AI should be chosen because it helps the task, not because every step needs a model.

• Who uses the output?
• What counts as a useful result?
• What is the consequence of an incorrect result?

Check the information and permissions first

List the sources the system needs and who owns them. For a knowledge assistant, outdated or contradictory documents can create problems even when the model behaves as expected. Data preparation and ownership are part of the project.

An assistant should not make restricted information available to someone who could not access the original source. Document retrieval, account identity, tool access, and data retention need to be designed together.

• Representative, permissioned source material
• An owner for keeping information current
• Defined access and retention requirements

Build a test set before polishing the demo

Collect ordinary requests, difficult edge cases, and examples the system should decline or escalate. Agree how the results will be reviewed. An impressive demonstration on a few prompts does not establish how the system will behave in routine use.

Compare quality with the current process or a simpler baseline. Track measures relevant to the task, such as correction effort, successful completion, latency, or cost per completed workflow. Record limitations alongside the scores.

Plan the human decision points

Decide which outputs can be used directly and which require review. If the integration can change business records, send communications, or trigger other actions, define approval boundaries and recovery paths before connecting those actions.

The interface should show users when they are receiving generated content and how to correct or escalate it. Logs and feedback can support investigation without unnecessarily collecting sensitive material.

Treat release as the beginning of evaluation

Account for usage-based provider charges, monitoring, source updates, and model changes. Define who will review quality and what triggers a fallback or pause. An integration that works on launch day can still change as inputs and dependencies change.

A practical first engagement can be a limited prototype with an evaluation report and a go-or-no-go decision. That makes the next investment depend on evidence rather than the promise of a general-purpose assistant.