Start with a clear, shared goal for exploring AI. Is the aim to reduce time to insight, improve decision-making, or free up team capacity? Align the team around one powerful, measurable goal that connects to broader business priorities.
Identify two to three practical, valuable use cases. The best starting points are tasks you already understand well and can easily test. Choose use cases that are simple, clear, and doable with current resources.
Every AI solution depends on data. Check whether the relevant data is available, clean, and accessible. Map out what data each use case will rely on, identify gaps or barriers. If the data isn’t ready, the use case isn’t either.
Establish your core working group, plus decision-makers and supporters who will be critical to unblock progress i.e. IT, compliance, brand, agency partners. If you miss a key voice, your pilot risks delay or rejection at the final hurdle.
Define what success looks like. Keep pilots small and fast. Assign owners for testing, and put feedback loops in place to capture learnings quickly. If it’s not testable, it’s not pilot-ready.
Which risks are most relevant to your use case? How will you monitor or mitigate them? Human-in-the-loop processes, approvals, or hard constraints can keep you safe while moving fast.
Assuming the pilot is successful, how will you roll it out? Make the path forward clear so momentum builds, not dissipates, once the pilot ends.
Use proven behaviour change techniques: name change champions, train the team, and reinforce adoption through nudges or incentives. Communicate early and often. The goal is to build confidence across the team.