Prepare exception reporting before review meetings

What this solves

Recurring meetings get used to assemble status because exceptions, blockers, and changed assumptions are not summarized before people arrive.

• Meetings start with data gathering instead of decisions.
• People bring different versions of status from spreadsheets, boards, inboxes, dashboards, or memory.
• Issues are discussed without enough source context to assign a corrective action.

What AI is good at here

• Highlighting what changed since the last review and which items breached agreed thresholds.
• Summarizing exceptions, blockers, open decisions, owner, age, value at risk, and likely next action.
• Drafting a meeting-ready issue list from approved data and notes with source links.

What AI should not own

• Humans should verify sensitive metrics, decide action, and own tradeoffs involving money, care, staffing, scope, or customer trust.
• AI should not hide uncertainty, infer root cause without evidence, or turn weak data into confident recommendations.

Best first implementation

• Pick one recurring review where time is currently lost to status assembly.
• Define the exception rules: red, amber, green thresholds; required source; owner; age; value at risk; and decision needed.
• Send the exception summary before the meeting and use the meeting to assign actions, not recreate the report.

What good looks like

• Meetings focus on decisions and recovery actions rather than status assembly.
• Exceptions are visible before people walk in, with enough source context to act.
• Follow-up actions are clearer, faster, and tied to the next review.

Common mistakes

• Summarizing everything instead of only exceptions, blockers, changes, and decisions.
• Treating AI commentary as accurate without source links, threshold rules, or human metric review.