AI can significantly improve the quality and speed of a project review when used deliberately.

Rather than replacing judgment, it can act as a thinking companion that helps surface patterns, test assumptions, and structure insights so they can be used more effectively.

Here are a few practical ways AI can support your lessons learned process:

• Scan and summarise project records: AI can rapidly process meeting notes, status reports, email threads, and change logs to highlight repeated pain points or recurring risks that may not stand out when documents are reviewed individually.
• Spot shifts across time: By comparing early plans to later versions, AI can help identify where scope, tone, or risk appetite changed—useful for desktop review and governance insight.
• Draft prompts for reflection: AI can generate probing follow-up questions in the style of the 5 Whys, helping teams go beyond surface explanations during workshops or interviews.
• Turn rough notes into usable recommendations: Even if you capture quick bullet points during delivery, AI can expand them into clear, SMART-style recommendations ready for review.
• Challenge correlation assumptions: AI can be prompted to ask, “What else might have caused this?” or “What evidence supports this causal link?” helping reviewers avoid drawing incorrect conclusions.
• Cluster insights into themes: By grouping related lessons automatically, AI can help structure recommendations under Start–Stop–Continue or other formats without you having to manually sort through every entry.

AI can also be used after lessons have been captured to interrogate and transform the raw material into usable outputs.

By feeding your collected lessons or notes into an AI tool, you can ask it to cluster them into start–stop–continue categories, highlight patterns across multiple projects, or identify which lessons represent real points of intervention rather than just observation.

From there, AI can take each cluster and help rewrite the insight as a SMART recommendation, complete with clear ownership, timeframe, and measurable intent.

This turns a long list of unstructured reflections into a focused, prioritized action set that an organization can actually implement and track—shifting the review from “interesting insight” to “operational improvement.”

Used thoughtfully, AI allows project managers and reviewers to spend less time gathering data and more time interpreting, prioritizing, and deciding where intervention would make the most difference, which is the real purpose of a lessons learned process.