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Loom AI turns a 10-minute recording into a 4-line summary, 6 chapter markers, and a list of action items in 30 seconds. The features are powerful but only useful when configured for your team's workflow. Here's the full setup.
Who this is forTeams of 5+ recording 20+ Looms/week who want AI to handle the cleanup work. Especially valuable for sales (deal recap auto-summary), support (ticket-ready summaries), and internal updates (skim-able team updates).
What you'll need
Step 1
Workspace → Plan → confirm Loom AI is included or add it. Toggle on: Auto-transcripts, Summaries, Chapters, Action Items, Title generation.
Loom AI is included in Business AI ($16-20/user/mo as of 2026) and Enterprise plans. Starter and Business (non-AI) plans do NOT include AI features beyond basic transcript.
Settings → Workspace → Plan & Billing. Verify "Loom AI" is in your subscription. If not, upgrade or add the AI module.
Once enabled, Settings → Workspace → AI features. Toggle ON: Auto-transcripts (default on for all plans), AI summaries, Auto-chapters, Action items extraction, AI title suggestions.
Toggle ON workspace-wide so every new recording gets the AI treatment automatically. Otherwise reps have to remember to trigger it per video.
Heads-up: Loom AI runs on recordings in English, Spanish, French, German, Portuguese, Italian, and Japanese with strong quality. Other languages get transcript only.
Step 2
Workspace → AI → Summary preferences. Pick length (Brief / Standard / Detailed), tone (Casual / Professional), and bullet format vs paragraph.
Settings → Workspace → AI features → Summary preferences.
Length: Brief = 1-2 sentences (good for internal updates). Standard = 3-5 bullets (most workflows). Detailed = paragraph + 5-8 bullets (sales/customer-success).
Tone: Professional for client-facing video (sales recaps shared with prospects). Casual for internal team updates.
Format: Bullets are easier to scan and copy-paste into CRM/Slack. Paragraph is better for narrative recaps.
These are defaults — individual videos can override. Recommended default for most teams: Standard length, Professional tone, Bullets.
Set a custom vocabulary list (Workspace → AI → Glossary) with your product name, common acronyms, customer names you reference often. AI uses this to avoid transcription errors on jargon.
Step 3
Workspace → AI → Chapters: enable for videos >2 min. Auto-titles: name the video based on content. Both editable per-video after generation.
Settings → Workspace → AI → Chapters → Enable auto-chaptering for videos longer than 2 minutes.
Loom AI detects topic shifts and creates chapter markers (e.g., "Intro," "Live Demo," "Pricing Discussion," "Next Steps"). Viewers see clickable chapters in the player.
For shorter videos (<2 min), chapters add overhead without benefit — leave them off.
Auto-title: Workspace → AI → Auto-title. When enabled, Loom names videos based on content instead of "New Recording - 26 May 2026 14:32." Saves cleanup time.
Per-video override: open any video → Edit → Title / Chapters → manually adjust. AI suggestions appear as a side panel you can accept or reject.
Sales-specific tip: rename auto-titles to "[Prospect Name] — Demo Recap" pattern for CRM logging consistency. Build it as a manual step until Loom AI gets workspace-specific title templates.
Step 4
Workspace → AI → Action items → enable. AI extracts "who needs to do what by when" from recordings. Best for async standups and internal updates.
Settings → Workspace → AI → Action items → Enable.
After every recording, Loom AI extracts: (a) explicit action items ("Sarah will send the contract by Friday"), (b) decisions ("We decided to launch on June 3"), (c) open questions.
Action items appear in the video sidebar with timestamps. Click to jump to the moment they were mentioned.
Integration: connect Loom to Asana, ClickUp, Jira, Linear, or Slack so action items auto-create tasks. Configure in Settings → Integrations.
Use case 1: async standups. Team records a 3-min update. AI extracts each person's commitments. Slack-bot posts them as task assignments.
Use case 2: client meetings. Sales records the call (with consent). AI extracts client commitments + your follow-up tasks. Logged to CRM as next-steps.
Limit: AI accuracy on action items is 75-85% — review before sending to clients or auto-creating tasks. Build a "review then publish" step.
Step 5
Loom AI → "Generate follow-up email" button → drafts a recap email from the recording. Edit, then send via Gmail/Outlook integration.
Open any recorded Loom. In the AI panel, click "Generate follow-up email" (or similar — UI varies).
AI drafts a recap email summarizing what was covered + suggested next steps. Tone matches your workspace defaults.
Edit the draft for accuracy and voice (AI gets the structure right but voice wrong about 30% of the time).
Send directly via Gmail / Outlook integration, or copy into your CRM (HubSpot, Salesforce, Salesloft) for sequence follow-ups.
For sales: this turns a 10-min discovery call into a 2-min recap + email task. Reps can send 3-5x more thoughtful follow-ups per day.
For customer success: post-call summaries auto-populate the account record. CS managers walk into renewal conversations with full context.
Caveat: never auto-send AI-drafted emails without human review. The 15-20% inaccuracy rate is enough to embarrass you with prospects.
Step 6
Document the workflow: when to use AI features, when to override, when to disable. Run a team session showing real Loom + AI output examples.
Build a one-page playbook: which Loom features run on which video types.
Example playbook: (1) Internal updates → auto-transcript + brief summary, no chapters, no action items. (2) Sales recaps → standard summary + auto-titles + action items extracted to CRM. (3) Support replies → transcript only, no summary needed.
Run a 30-min team session: open 5 recent recordings, show the AI panel, walk through what to accept/edit/reject. People learn AI by seeing real examples, not reading docs.
Owner: assign one person to be the "Loom AI quality reviewer" for the first 30 days. They audit a sample of summaries weekly and tune the glossary + preferences based on what they see.
Iterate the glossary: every time AI mis-transcribes a product term or customer name, add it to Workspace → AI → Glossary. After 4-6 weeks of additions, transcript quality on jargon hits 95%+.
Step 7
Before/after: track minutes per video on cleanup (titling, summarizing, emailing). AI should save 15-30 min per video for sales/CS, 5-10 min for support.
Before turning AI on, log 5-10 recordings: how long did the rep spend post-recording on cleanup (renaming, writing the recap, copying to CRM, sending follow-up email)?
Average baseline for sales recap: 18-25 min of post-call cleanup. For support: 5-8 min. For internal updates: 3-5 min.
After AI is on for 30 days, log the same flow. Healthy savings: 15-25 min per sales recap, 3-6 min per support reply, 1-2 min per internal update.
If savings are below those numbers, the AI defaults aren't tuned. Revisit summary preferences, glossary, and the playbook.
Total monthly impact for a 5-rep sales team recording 4 demos/week: ~30 hours/month saved. At $30/hr loaded cost, that's $900/month in time recovered for $80-100/mo in Loom AI add-on cost.
Common mistakes
Not adding company-specific vocabulary to the glossary
What goes wrong: AI transcribes 'Salesloft' as 'sales loft', your product name as something random, key customer names misspelled. Summaries and action items inherit the errors and look unprofessional.
How to avoid: Add product names, common acronyms, customer names, integration names to Workspace → AI → Glossary. Add to it weekly for the first month.
Generic summary preferences for every use case
What goes wrong: Sales recaps come out too casual; internal updates come out too formal. Reps stop trusting summaries and rewrite them anyway. Time savings disappear.
How to avoid: Use per-folder summary preferences (Business+ plans). Sales folder = professional + bullets. Internal folder = casual + brief.
Auto-sending AI-drafted emails
What goes wrong: 15-20% of AI emails contain factual errors: wrong client names, fabricated quotes, misattributed action items. Sent unreviewed, they damage trust and waste sales cycles.
How to avoid: Always human-review AI emails before send. Build it as a step in the workflow: AI drafts → rep reviews → sends. 90-second review per email is non-negotiable.
Chapters on short videos
What goes wrong: Loom AI tries to split a 45-second support reply into 'Intro / Demo / Closing.' Chapters fragment the experience without adding value. Viewers find it confusing.
How to avoid: Disable auto-chapters for videos <2 min in the workspace defaults. Re-enable only for long recordings (demos, training, all-hands).
No human review of action items before task creation
What goes wrong: AI extracts 'Sarah will send proposal by Friday' from a hypothetical example in the recording. Asana auto-creates the task. Sarah is confused. Trust in the system erodes.
How to avoid: Set integrations to "Suggest, don't create" — AI proposes tasks, human approves before they push to Asana/Linear/Jira.
Treating AI summary as the final deliverable
What goes wrong: Reps copy AI summaries verbatim into CRM and emails. Summaries are competent but generic, missing the relationship-building voice that wins deals.
How to avoid: Treat AI summary as a draft. Edit for voice and add the 2-3 specifics the AI missed. Time investment is 90 sec — quality lift is dramatic.
Recap
Done — what's next
How to set up a Loom account for async video work
Read the next tutorial
Hand it off
Setting up Loom AI is a 60-min config. Tuning it into a team workflow — glossary, per-folder preferences, integration with task systems, training the team — is ongoing. A vetted video editor on EverestX can run this end-to-end starting at $14-16/hr — typically $400-900/mo for a team of 5-15.
See video editor rates
Free / lower tiers get basic auto-transcript only. Loom AI (Business AI add-on or Enterprise) adds: AI summaries, auto-chapters, action item extraction, auto-titles, follow-up email drafts, and a customizable glossary. The AI features layer on top of the transcript that everyone gets.
For software/tech recordings with clear audio: 85-92% accurate without glossary tuning. After 4-6 weeks of glossary additions for product names and jargon: 94-97%. Heavy industry jargon (medical, legal, finance) needs more tuning and benefits from the longer 'Detailed' summary mode.
Yes — full AI support (transcript + summary + chapters + action items) in English, Spanish, French, German, Portuguese, Italian, Japanese. Other languages get transcript only. Multi-language teams can translate via the Translate button after transcript is generated.
Loom AI processes recordings on Loom-controlled infrastructure (no third-party model training on your data per Loom's privacy policy). For HIPAA/regulated workloads, Loom Enterprise offers a HIPAA-compliant add-on. Never record actual customer PII expecting AI to redact it — record clean, redact in-product before sharing.
Replaces 70-80% of the cleanup work (titling, summarizing, transcript) but not the editorial work (storytelling, branding, multi-clip editing, polishing). For a 20-Loom/week team, AI handles the volume; an editor polishes the top 10% of high-value videos. The combo is what unlocks scale.
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