Assess Pilot Outcomes Against Business Goals
Start by reviewing the pilot’s performance against the measurable purpose set at the outset. Did it reduce enquiry response times, improve data completeness, or streamline handovers as intended? Collect data on completion rates, handovers to staff, response quality, and the time spent correcting errors. This evidence will help determine if the pilot achieved meaningful improvements or if adjustments are needed before scaling.
Evaluate Workflow Integration and User Experience
Consider how well the AI solution connected with existing systems and workflows. Were there issues with data access, duplicate records, or integration failures? Also, gather feedback from users about ease of use and whether the assistant’s scope and handover points matched their needs. Identifying friction points or gaps in the workflow can guide refinements to improve reliability and acceptance.
Decide on Expansion, Refinement, or Alternative Approaches
Based on performance and user feedback, decide whether to expand the AI assistant’s scope, refine its capabilities, or pursue simpler automation options. Sometimes, rule-based automation or improved forms can solve parts of the problem more efficiently. Consider the controls needed for sensitive decisions and ensure clear boundaries and escalation paths are in place.
Plan for Ongoing Support and Ownership
Successful AI use depends on clear ownership of the tool, monitoring performance, and support processes. Define who will manage updates, handle issues, and provide staff training. Establish escalation paths and guidance so AI use does not rely on a single person’s enthusiasm. Preparing for everyday use reduces risks and sustains value over time.