Start with one workflow: a focused AI quoting evaluation

Choose a bounded job, a credible baseline and measures that include the human work around the AI.

A useful evaluation begins with a recurring job your team understands. “Improve sales with AI” is too broad to compare. Preparing one defined enclosure package from a permitted set of RFQs gives the team something concrete to inspect.

Choose a business owner and the reviewers who know the current process. Agree what the workflow may use, which actions it may take and what a useful improvement would look like before assessing the results.

Choose cases that represent the work

Include straightforward requests, incomplete information and examples that need an exception. Record how the cases were selected. A set containing only clean, familiar requests can tell you less about the effort required in ordinary use.

Keep the strongest existing process as the baseline. Review the resulting offers for product details, required accessories, service scope, prices and open conditions. Give the reviewers a consistent way to record issues and their significance.

Measure the whole workflow

Count preparation, human review, clarification and correction effort together. Include the time needed to assemble information and resolve exceptions. Faster initial drafting can be useful, but the result matters most when the full review-and-send process improves.

Past-case replay can test output quality and effort. Additional orders and changed conversion need a live evaluation with an agreed comparison. Keep those evidence types distinct when presenting the business case.

Make expansion a decision

Agree the conditions for adding a connection or another action. Read-only access, inbox participation and ERP writeback each create different implementation work and review needs. A successful file-based exercise can inform the next stage without settling every later question.

• One recurring job and a named owner
• Permitted information and a representative case set
• Quality, effort and exception measures
• A comparison with the current workflow
• A specific decision to continue, change scope or stop

Finish with a short record of what worked, what remains open and what the next step requires. That gives the team a decision it can act on instead of a broad impression that the demonstration looked promising.