Verify Key Decisions and Action Items
The primary function of meeting notes is to create a clear record of what was decided and who is responsible for what comes next. AI summaries are designed to pull these out, but they can misinterpret or oversimplify. An AI might capture that a decision
was made but miss the crucial conditions or deadlines attached to it. Cross-reference the summary with the full transcript, paying close attention to the moments where decisions were finalized. Ensure every action item is clearly defined, assigned to a specific person, and includes a due date. A vague task assigned to no one in particular is a task that won't get done. This step prevents the classic problem of ending a meeting with good intentions but no clear follow-through.
Listen for Tone, Nuance, and Sarcasm
AI models are notoriously bad at detecting sarcasm, humour, and subtle shifts in tone. A sarcastic comment like, “Great, another project with an impossible deadline,” might be transcribed literally as an expression of excitement. This can lead to serious misinterpretations of team morale or stakeholder sentiment. When you review the notes, listen to the audio or read the transcript for passages that feel emotionally charged or ambiguous. The AI summary might present a clean, logical flow, but the raw conversation often contains the hesitation, enthusiasm, or concern that provides essential context. Ignoring this emotional layer means you're only getting half the story.
Check for Correct Speaker Attribution
Misattributing a statement is one of the most common and potentially damaging errors in AI transcription. This is especially true in meetings with multiple participants speaking in quick succession or in hybrid meetings where audio quality varies. An idea suggested by a junior team member could be incorrectly credited to a senior leader, or vice versa. This not only creates an inaccurate record but can also cause workplace friction. As you review, scan the speaker labels and confirm they match your memory of the conversation. If you’re unsure, check the full transcript. Correcting these errors ensures that credit is given where it’s due and that accountability for statements is accurately recorded.
Confirm the Context Behind Decisions
A good decision record doesn't just state what was decided, but why. AI summaries tend to focus on the outcome, often stripping away the preceding discussion that led to it. For example, the notes might say, “We will proceed with Option B.” But they may omit the critical information that Option B was chosen because of budget constraints, despite Option A being technically superior. Without this context, team members who weren't in the meeting might question the decision later, forcing the team to relitigate the issue. When you review, ensure the reasoning behind major decisions is included in the final notes. This creates a more robust record and prevents future confusion.
Fill in the Gaps from Off-Record Cues
Not everything important in a meeting is spoken. A stakeholder’s visible hesitation on video, a shared look between two colleagues, or a quick note passed in a private chat are all forms of context that AI cannot capture. While these non-verbal cues won't appear in the transcript, they are often crucial for understanding the real dynamics at play. After reviewing the AI notes, take a moment to add your own human annotations. You might add a note like, “Team seemed hesitant about the Q4 timeline,” or “Client was most engaged during the discussion of X.” This layer of human intelligence is something AI cannot replicate and can be invaluable for future reference.
