Confirm the pilot’s clear purpose and scope
Begin by revisiting the specific business problem your AI pilot aims to address. Whether it’s reducing enquiry response times, improving data completeness, or automating repetitive tasks, your pilot should have measurable goals. Define what success looks like with clear acceptance criteria to evaluate outcomes.
Agree on the pilot’s scope, including the tasks the AI will handle and the limits of its authority. Consider which decisions require human review and how handovers from AI to staff will occur. This clarity helps manage expectations and ensures the pilot stays focused on achievable improvements.
Validate data quality and system integration
Assess the data sources the AI will use. Confirm that data is accurate, up-to-date, and accessible within your existing software environment. Identify any gaps or permissions needed to ensure smooth data flow.
Review how the AI will integrate with your current systems, including APIs and user access controls. Plan for how the workflow handles duplicate records, failures in integration, and escalation paths. Reliable connections and fallback procedures are vital to maintain business continuity during the pilot.
Prepare your team and workflows for adoption
Engage the staff who will interact with the AI pilot early in the process. Provide training focused on the real tasks they perform, including how to instruct the AI, verify outputs, and manage exceptions.
Establish clear guidelines on when to trust the AI and when to escalate to a human. Create simple, accessible guidance and support channels so the pilot does not rely on a single enthusiastic user. This helps embed the AI into everyday workflows and builds confidence across the team.
Set up monitoring and feedback mechanisms
Define the key performance indicators to track during the pilot, such as task completion rates, response accuracy, handover frequency, and time saved. Monitoring these metrics provides evidence of value and areas needing adjustment.
Implement a process for collecting staff feedback and logging errors or unexpected behaviours. Regular reviews allow you to refine the AI’s role, improve training, or decide if a simpler automation might be more effective before expanding the pilot.