AI Meeting Summaries for PMs: Otter, Fireflies, Read & More

In my experience, project managers attend more meetings than almost any role in modern organisations. The classic estimate of 25 hours a week in meetings still holds for the senior PMs I work with in 2026, even after the post-pandemic rationalisation efforts. I’d argue AI meeting summary tools are the single highest-leverage technology a PM can adopt right now, because they restructure not just how meetings are captured but what I count as a useful meeting in the first place.

In this guide I review the leading AI meeting tools, the use cases where I’ve seen each excel, the integrations that make a real difference, and the rituals I use to turn meeting capture from a checkbox into a durable knowledge advantage.

The classic problem with meetings is that the value disappears the moment the meeting ends. People remember 30-50% of what was said within an hour, less than 20% within a day. Action items get lost. Decisions get re-litigated. Cross-functional context gets fragmented across attendees.

AI meeting summaries change four specific dynamics:

• Search: every conversation becomes searchable.
• Distribution: people who could not attend get a useful artefact.
• Accountability: action items become extractable, traceable, and reviewable.
• Continuity: a project’s history of decisions builds up into a queryable corpus.

The tools differ in how well each does these four things. The choice matters less than the discipline of using whichever tool consistently.

Most PMs end up with two tools: a primary meeting bot for cross-platform coverage and a note-taking app for personal use.

Otter has been the default for PMs for years. In 2026 it remains a strong choice for general meeting capture.

Strengths:

• High transcription accuracy across accents.
• Solid AI summary with clear action item extraction.
• Otter Assistant joins Zoom and Teams meetings automatically.
• Searchable archive of all meetings.
• Reasonable pricing ($16-30/month for business tiers).

Weaknesses:

• Sales focus has diluted PM-specific features.
• Custom integrations require API work.
• Action item attribution sometimes inaccurate.
• Privacy and recording-consent UX has been criticised.

Best for: PMs who want a single reliable tool across Zoom, Teams, and Meet.

Fireflies positions itself as a workflow tool, not just a transcript producer. It has the strongest integration ecosystem of the cross-platform tools.

Strengths:

• Native integrations with Slack, Notion, Asana, Jira, HubSpot, Salesforce, and 50+ others.
• Strong action item extraction with auto-routing to ticketing systems.
• AI search across the meeting archive (“when did we discuss the Acme contract?”).
• Smart-search filters for sentiment, topics, and speakers.
• Custom prompts that run against any meeting (“summarise this meeting for an executive audience”).

Weaknesses:

• Transcription accuracy slightly behind Otter in some accents.
• Slack notification volume can be noisy without configuration.
• Pricing tiers are confusing.

Best for: PMs whose work involves tightly integrating meeting outcomes with PM and CRM tools.

Read.ai differentiates on sentiment, engagement, and meeting-effectiveness analytics.

Strengths:

• Real-time sentiment tracking during meetings.
• “Meeting effectiveness” scores that surface low-engagement meetings.
• Speaker-level engagement metrics.
• Browser extension shows information about meeting attendees.
• Clean summary format suitable for stakeholder distribution.

Weaknesses:

• Sentiment accuracy is debated; team comfort with being scored varies.
• Less integration breadth than Fireflies.
• Some features feel intrusive without clear consent flows.

Best for: PMs leading distributed teams who need to monitor team health and meeting hygiene at scale.

Granola took off in 2024-2025 as a personal meeting note-taker that augments your own notes rather than replacing them.

Strengths:

• Designed for personal use; the human stays in the loop.
• Excellent at producing clean structured notes from your shorthand.
• Works with any platform (audio capture only, no bot joins meetings).
• Privacy-friendly model.
• Strong UX for editing and refining post-meeting.

Weaknesses:

• Single-user focus means no team archive.
• Lacks the workflow integrations of Fireflies.
• Best in 1:1s and small meetings; loses precision in 8+ person meetings.

Best for: PMs who want personal augmentation without putting a bot in every meeting.

By 2026 the major meeting platforms have credible AI summary features built in.

Zoom AI Companion strengths:

• No third-party tool needed if your org is Zoom-first.
• Tight integration with Zoom workflow.
• Reasonable summary quality.
• Available in business and enterprise tiers.

Teams Copilot strengths:

• Deep integration with Microsoft 365.
• Strong if your meetings are Teams-only.
• Good summary discipline.
• Higher cost but bundled with Microsoft licensing for many enterprises.

Google Gemini Notes strengths:

• Native to Google Meet and Workspace.
• Solid summaries delivered to the relevant Google Doc or Drive folder.
• Less mature than the dedicated tools but improving rapidly.

Best for: PMs in single-platform organisations who want zero new vendor relationships.

Some sales call recording tools have features PMs find useful even though they were not built for PMs.

Gong:

• Deep call analytics.
• Topic and competitor mention tracking.
• Strong for PMs in B2B SaaS who need sales-call insights for VoC.

Chorus:

• Similar to Gong, slightly different UX.
• Salesforce-native.

Clari Copilot:

• Increasingly broad use beyond sales.
• Good for revenue-focused product teams.

PMs use these when sales call insights matter for their work and when the org is willing to extend access beyond the sales team.

Pricing varies and changes. Verify current pricing before committing.

The single most-valuable AI meeting summary feature is action item extraction. Strong PMs build a discipline around it:

• Every meeting AI runs action item extraction.
• Action items are reviewed within 30 minutes of the meeting ending.
• Each is confirmed: real action, real owner, real due date.
• AI-extracted action items are pushed to the relevant ticketing system (Jira, Linear, Asana).
• Open action items are reviewed weekly.

A useful prompt for any AI tool’s transcript:

“From this meeting transcript, extract action items. For each: action, owner, due date if mentioned, confidence in attribution. Flag any items where the owner is ambiguous or the action is not clearly committed.”

The PM then resolves ambiguity in 10 minutes and pushes confirmed items to the system of record.

The biggest leverage from AI meeting tools comes from integration into other workflows:

Configure 2-3 integrations early. Compounding value comes from end-to-end flow, not just transcript capture.

AI meeting tools touch real privacy concerns. Strong practice:

• Disclose AI recording at the start of every meeting.
• Get explicit consent for external attendees.
• Use enterprise tier with data-use clauses.
• Confirm data residency for regulated industries.
• Establish a data retention policy (most teams settle on 90-180 days).
• Anonymise customer data in shared summaries when possible.
• Never use AI summary tools for performance management.
• Have an opt-out path for employees uncomfortable with recording.

These practices protect both legal compliance and team trust. Without them, AI meeting tools can backfire dramatically.

Tools without rituals waste budget. Effective rituals:

• Pre-meeting: AI generates a one-paragraph brief from the calendar invite and previous related meetings.
• In-meeting: AI captures transcript, no human note-taker needed.
• Post-meeting (within 30 min): scrum master / PM reviews summary, confirms action items, pushes to ticketing system.
• Weekly: action item closure review.
• Monthly: archive audit - delete or archive old recordings per retention policy.

These rituals separate teams who get value from teams who paid for tools that sit unused.

These are the failure modes I run into most when teams roll out AI meeting tools. I’ve watched each of them quietly erode the value of an otherwise solid investment.

• Tool sprawl. I’ve seen three different AI meeting tools across one team produce inconsistent archives. Standardise.
• No human review. AI summaries with no human pass produce confidently wrong action items.
• Privacy backlash. Recording without consent destroys team trust. I always disclose.
• Archive that nobody searches. Capture without retrieval is wasted spend. Make search part of the routine.
• Hallucinated quotes. AI sometimes paraphrases inaccurately. I always quote from the verified transcript.
• Action item drift. Extracted action items that no one closes accumulate. Weekly review prevents this.
• Over-recording. Some meetings should not be recorded (HR conversations, performance reviews, sensitive customer escalations). Set norms.

Days 1-7: pick a primary tool. Pilot in 5-10 meetings. Disclose to all attendees.

Days 8-14: add Slack and PM-tool integrations. Configure summary destinations.

Days 15-21: institutionalise the post-meeting review ritual. Push action items to the ticketing system.

Days 22-30: train the team. Establish privacy and consent norms. Measure: action items closed per week, time saved on note-taking, stakeholder satisfaction with summaries.

By day 30, most PMs report 3-5 hours per week saved on meeting administration plus a measurable improvement in action item closure rate.