AI Meeting Assistant: Automate Note-Taking, Summaries, and Follow-Up

Reclaim 10+ Hours Weekly from Meeting Administration

“We spend more time documenting meetings than actually executing on what we discussed.”

This frustration defines knowledge work in 2025. The average Chicago professional attends 8-12 meetings weekly, each requiring:

• Real-time note-taking (dividing attention between listening and documenting)
• Post-meeting summary writing
• Action item extraction and assignment
• Follow-up task creation
• Information sharing with stakeholders who couldn’t attend
• Searching past notes for context on current decisions

Result: 2-4 hours weekly on meeting administration instead of actual work. Multiply across your team and the productivity drain becomes staggering.

Worse, rushed note-taking captures incomplete information. Critical context gets lost. Action items fall through cracks. Decisions require re-discussion because no one documented the rationale.

AI meeting assistants eliminate this entire burden. They join meetings automatically, transcribe conversations in real-time, generate intelligent summaries highlighting key decisions and action items, integrate tasks into project management systems, answer questions about past discussions, and create searchable knowledge bases—all without human effort.

Chicago consulting firm Northside Strategy implemented AI meeting assistants across their 23-person team. They reclaimed 47 hours weekly (previously spent on meeting notes and summaries), reduced project delays from “unclear next steps” by 68%, and created a searchable archive of 1,200+ meetings that serves as institutional knowledge for new hires and project context.

This guide shows you how to deploy your own AI meeting assistant, transforming meetings from productivity drains into efficiently documented decision-making sessions.

What Is an AI Meeting Assistant?

An AI meeting assistant is an intelligent system that automates everything meeting-related: recording audio/video, transcribing conversations with speaker identification, generating summaries and key takeaways, extracting and assigning action items, answering questions about meeting content, and integrating with productivity tools to ensure follow-through.

Core Capabilities:

Automatic Recording and Transcription: AI assistants join scheduled meetings (Zoom, Google Meet, Microsoft Teams), record audio/video, and generate real-time transcriptions with speaker identification—no human intervention required.

Intelligent Summarization: Rather than providing raw transcripts, AI assistants generate executive summaries highlighting key decisions, important discussions, questions raised, and conclusions reached—distilling 60-minute meetings into 3-5 minute reads.

Action Item Extraction: AI identifies commitments, deadlines, and assignments from conversation, extracting “John will send proposal by Friday” or “Sarah to research vendor options” automatically, ensuring nothing gets lost.

Task Integration: Action items flow automatically into project management systems (Asana, Monday, ClickUp, Jira), assigned to appropriate team members with context and deadlines—no manual task creation required.

Searchable Archive: All meeting transcripts, summaries, and recordings become searchable knowledge base: “What did we decide about pricing strategy in March?” returns relevant meeting excerpts immediately.

Stakeholder Distribution: AI automatically shares summaries with meeting participants and relevant stakeholders who couldn’t attend, ensuring everyone has context without manual email writing.

Integration with Workflows: AI assistants connect to CRM (updating deal notes after sales calls), wikis (documenting decisions), chat platforms (posting summaries to Slack), and calendars (scheduling follow-up meetings).

Question Answering: Ask “What feedback did the client give on the proposal?” and AI searches all relevant meetings, extracting specific information—your institutional memory becomes instantly accessible.

AI Meeting Assistants vs. Manual Note-Taking:

Manual note-taking:

• Divides attention (listening vs. documenting)
• Captures incomplete information
• Lacks speaker attribution
• Requires post-meeting cleanup
• Lives in scattered documents
• Difficult to search across meetings

AI meeting assistants:

• Record everything automatically
• Capture complete verbatim conversation
• Identify who said what
• Generate summaries automatically
• Centralize all meeting knowledge
• Enable instant search across all meetings

The difference: incomplete, effort-intensive manual process vs. complete, automated documentation with no human effort.

Business Impact: ROI Data from Chicago Implementations

Chicago businesses deploying AI meeting assistants report measurable improvements in productivity, follow-through, and knowledge retention:

Time Savings: 2-4 Hours Per Person Weekly

Manual meeting documentation:

• Taking notes during meeting: 30-45 minutes (while trying to participate)
• Cleaning up and formatting notes: 15-20 minutes
• Distributing summary to stakeholders: 10 minutes
• Creating and assigning tasks from action items: 15 minutes
• Per-meeting total: 70-90 minutes

AI meeting assistant:

• Automated transcription: 0 minutes human effort
• Automated summary generation: 0 minutes
• Automated distribution: 0 minutes
• Review and approve summary: 5-10 minutes
• Per-meeting total: 5-10 minutes

For professionals attending 8 meetings weekly: 9.3-12 hours saved weekly = 37-48 hours monthly.

West Loop marketing agency (15 person team, averaging 10 meetings/person weekly) reclaimed 127 combined hours weekly after implementing AI meeting assistants—equivalent to hiring 3.2 full-time employees purely for meeting administration.

Meeting Effectiveness: 30-50% Improvement

When participants don’t need to divide attention between listening and note-taking:

• Better engagement and participation
• Deeper discussion quality
• More creative problem-solving
• Faster decision-making

Lincoln Park product development team measured decision quality before/after AI assistant implementation. Decisions made with AI-documented meetings showed 43% fewer “need to revisit this” occurrences (decisions stuck the first time, with proper context captured).

Action Item Completion: 40-60% Improvement

Manual action items often fall through cracks—not captured, not assigned, not tracked. AI-extracted action items flow directly into task systems with assignments and deadlines.

River North consulting firm tracked action item completion rates:

• Before AI assistant: 64% of action items completed on time
• After AI assistant: 91% completion rate

The improvement came from better capture (AI catches all commitments), clear ownership (automatic assignment), and system integration (items appear in task management immediately).

Knowledge Retention: 85-95% Improvement

Ask someone about a decision made three months ago—they remember fragments at best. AI assistants create perfect institutional memory.

Gold Coast SaaS company measured “decision context recall” (ability to explain why past decisions were made):

• Human memory alone: 23% accuracy at 3-month mark
• AI meeting archive: 94% accuracy (instant access to exact discussion)

Onboarding Efficiency: 50-70% Faster

New employees typically spend weeks absorbing context: “Why do we do it this way? What was the thinking behind this decision?”

With searchable meeting archives, new hires access complete context:

• Search for project name, see all related meetings
• Understand decision rationale from actual discussions
• Hear customer feedback in sales calls
• Learn company processes from team meetings

Chicago creative agency reduced new designer onboarding from 8 weeks to 3 weeks by providing access to complete meeting archive showing design decision processes, client feedback patterns, and team workflows.

Client Satisfaction: 25-35% Improvement

For client-facing businesses, AI assistants ensure nothing discussed with clients gets lost:

• All client requests documented
• All commitments tracked
• All feedback captured
• All decisions recorded with context

West Loop consulting firm saw Net Promoter Score increase from 67 to 84 after implementing AI meeting assistants—clients felt “truly heard” because consultants referenced specific past conversation details accurately.

Chicago Business Use Cases

AI meeting assistants adapt to virtually any role involving meetings:

Sales Teams:

Use Case: Sales call documentation, prospect need tracking, objection handling, deal progression, client relationship history.

Chicago Example: Loop-based B2B software company deployed AI assistant for all prospect and client calls. AI generates call summaries highlighting customer pain points, objections raised, decision criteria discussed, and next steps committed. Sales reps spend zero time on call documentation. CRM automatically updates with call notes, action items, and deal stage progression. Sales cycle shortened 31% (reps spend more time selling, less time documenting). Win rate increased 27% (complete context on every prospect interaction).

Consulting and Professional Services:

Use Case: Client meeting documentation, project discussions, decision tracking, billable hour capture, stakeholder alignment.

Chicago Example: Gold Coast management consulting firm uses AI assistant for all client engagements. AI generates client-ready meeting summaries (professionally formatted, action items highlighted), tracks project decisions with rationale, creates searchable project history, and identifies scope creep (requests not in original SOW). Consultant productivity increased 38% (less admin work). Client satisfaction scores increased 41% (clients receive excellent documentation, nothing gets lost).

Product Development:

Use Case: Design review documentation, user research synthesis, sprint planning notes, technical discussion capture, decision documentation.

Chicago Example: River North product development team records all design critiques, user testing sessions, and sprint planning meetings. AI summarizes user feedback themes across multiple sessions, tracks feature decisions and rationale, documents technical constraints discussed, and creates searchable product evolution history. Product development cycle accelerated 22% (less time re-discussing decided issues). Feature adoption improved 34% (better decisions from synthesized user research).

Leadership and Executive Teams:

Use Case: Strategic planning documentation, board meeting minutes, executive decision tracking, cross-functional alignment.

Chicago Example: Chicago mid-market manufacturing company CEO uses AI assistant for executive team meetings and board sessions. AI generates formal minutes, tracks strategic decisions with context, identifies action item owners, and creates quarterly strategy review summaries. Board members receive professional documentation within hours (vs. 2-3 weeks previously). Executive team alignment improved dramatically (clear record of strategic decisions).

Project Management:

Use Case: Stakeholder meetings, status updates, risk discussions, resource planning, timeline negotiations.

Chicago Example: West Loop construction project management firm deployed AI assistants for client meetings, subcontractor coordination, and internal project reviews. AI tracks all commitments (who’s responsible for what by when), documents scope changes and approvals, captures risk discussions with mitigation plans, and creates timeline of project decisions. Project delivery improved (fewer missed commitments, better accountability). Client disputes decreased 73% (complete documentation of all discussions and decisions).

Remote and Hybrid Teams:

Use Case: Ensuring remote participants have equal access to meeting content, creating async-friendly documentation, maintaining context across time zones.

Chicago Example: Chicago marketing agency with distributed team (12 Chicago, 8 remote workers across time zones) uses AI assistants for all meetings. Remote team members unable to attend live receive comprehensive summaries with action items relevant to them. Searchable archive enables async work (team members search meeting history rather than scheduling calls for context). Remote employee satisfaction increased 52% (no longer feeling “out of the loop”).

Step-by-Step Implementation Guide

Deploying AI meeting assistants follows a straightforward process. Most Chicago teams move from setup to full adoption in 1-2 weeks.

Phase 1: Platform Selection (Days 1-2)

Choose Your AI Meeting Assistant Platform:

Several excellent options exist with different strengths:

Otter.ai:

• Excellent transcription accuracy
• Real-time collaboration (team members can highlight, comment during meeting)
• Good summarization
• Integration with Zoom, Google Meet, Teams
• Pricing: Free tier (limited), $10-20/user/month

Fireflies.ai:

• Strong automation capabilities
• Excellent CRM integration (Salesforce, HubSpot)
• Good search functionality
• Custom vocabulary training
• Pricing: Free tier, $10-19/user/month

Fathom:

• Focused on sales calls
• Automatic CRM updating
• Call highlights and coaching
• Good for smaller teams
• Pricing: Free for individuals, $29/user/month for teams

Grain:

• Video-focused (clips key moments)
• Strong collaboration features
• Good for product/design teams
• Pricing: $15-29/user/month

Notion AI or Google Meet transcripts + Custom AI:

• Native meeting transcripts (Google Meet, Zoom)
• Custom AI processing (GPT-4, Claude) for advanced summarization
• Maximum customization
• Requires technical setup
• Cost-effective at scale

Selection Criteria:

Integration Requirements:

• Video platform compatibility (Zoom, Google Meet, Teams)
• CRM integration needs (Salesforce, HubSpot, Pipedrive)
• Project management integration (Asana, Monday, Jira)
• Calendar sync (Google, Outlook)

Team Size and Budget:

• Small team (< 10): Otter.ai or Fathom (free/low-cost tiers)
• Medium team (10-50): Fireflies.ai or Grain (good collaboration features)
• Large team (50+): Enterprise plans with volume pricing

Primary Use Case:

• Sales calls: Fathom or Fireflies.ai (CRM integration)
• General meetings: Otter.ai (versatile, affordable)
• Product/design: Grain (video clips useful)
• Custom needs: Build with native transcripts + AI APIs

For most Chicago businesses, Fireflies.ai or Otter.ai offer best balance of features, ease of use, and cost.

Phase 2: Initial Setup (Days 3-4)

Connect Calendar and Meeting Platforms:

1. Grant AI assistant access to your calendar (Google Calendar or Outlook)
2. Connect to video meeting platforms (Zoom, Google Meet, Teams)
3. Configure which meetings AI should join automatically:
   • All meetings (comprehensive documentation)
   • Only specific meeting types (sales calls, team meetings)
   • Opt-in per meeting (manual activation)
   • Exclude 1-on-1s or sensitive discussions

Integrate with Productivity Tools:

CRM Integration (for sales/client-facing teams):

• Connect Salesforce, HubSpot, or Pipedrive
• Configure automatic call logging
• Map meeting fields to CRM fields
• Set up action item sync to CRM tasks

Project Management:

• Connect Asana, Monday.com, ClickUp, or Jira
• Configure action item creation rules
• Set up default projects/workspaces
• Define assignment logic

Communication Platforms:

• Slack integration (post summaries to channels)
• Teams or Discord integration
• Email distribution rules

Note-Taking/Wiki Systems:

• Notion, Confluence, or Google Docs integration
• Automatic archiving of meeting notes
• Searchable knowledge base creation

Configure Team Settings:

User Permissions:

• Who can view all meeting recordings?
• Who can access summaries?
• What security/privacy controls needed?
• How long to retain recordings?

Summary Preferences:

• Desired summary length (brief vs. detailed)
• Include/exclude specific elements (decisions, questions, next steps)
• Format preferences (bullet points, paragraphs, structured sections)

Notification Settings:

• When to send summaries (immediately, end of day, scheduled)
• Who receives summaries (participants only, broader team, stakeholders)
• Notification channels (email, Slack, in-app)

Phase 3: AI Enhancement and Customization (Days 5-7)

Custom AI Processing (Optional but Recommended):

Native meeting assistant summaries are good, but custom AI processing creates exceptional value.

Enhanced Summary Generation:

Feed meeting transcript to GPT-4 or Claude with custom prompts:

Meeting Transcript:
[Full transcript from Fireflies/Otter]

Generate comprehensive meeting summary:

## Executive Summary (2-3 sentences)
High-level overview of meeting purpose and outcomes

## Key Decisions Made
List all decisions with context and rationale

## Action Items
| Owner | Task | Deadline | Priority |
[Format as table with specific assignments]

## Important Discussions
Summarize significant topics discussed

## Open Questions
Questions raised but not resolved

## Risks or Concerns
Any issues, blockers, or concerns mentioned

## Next Steps
Clear roadmap for what happens next

## Attendees & Participation
[List participants with contribution notes]

Format: Professional, concise, actionable
Tone: Neutral, objective documentation

This custom processing creates superior summaries tailored to your needs.

Workflow Automation:

Use Make.com or Zapier to create automated workflows:

Sales Call Workflow:

1. AI assistant transcribes sales call
2. Trigger automation on call completion
3. Custom AI generates summary highlighting:
   • Customer pain points mentioned
   • Objections raised
   • Buying signals detected
   • Competitor mentions
   • Decision timeline and process
   • Next step commitments
4. Update CRM with summary and extracted data
5. Create follow-up tasks
6. Send thank-you email to prospect (draft generated by AI)
7. Notify sales manager if deal stage advanced

Team Meeting Workflow:

1. AI transcribes weekly team meeting
2. Extract all action items
3. Create tasks in project management system
4. Assign to team members
5. Post summary to Slack #team channel
6. Update project status documents
7. Archive notes in Notion/wiki

Client Meeting Workflow:

1. AI transcribes client call
2. Generate client-ready summary (professional formatting)
3. Extract scope changes or new requests
4. Create internal summary (includes sensitive observations)
5. Email client-ready version to client within 30 minutes
6. Update project documentation
7. Create billing records for client requests

Custom Vocabulary and Speaker Training:

Improve accuracy:

• Add industry-specific terminology
• Train on company/product names
• Add client/customer names
• Include technical jargon
• Specify speaker identification improvements

Phase 4: Team Rollout (Week 2)

Pilot with Small Group:

Before company-wide rollout:

1. Select 3-5 early adopters
2. Run AI assistant for 1-2 weeks
3. Gather feedback on accuracy, usefulness, issues
4. Refine settings and workflows
5. Create training materials based on learnings

Team Training:

Prepare team for adoption:

Basic Training (15-minute session):

• How AI assistant joins meetings
• What it captures and documents
• How to access summaries and transcripts
• Privacy and security considerations
• How to exclude AI from specific meetings
• How to highlight important moments during meetings

Power User Training (30-minute session):

• Advanced search functionality
• Editing and annotating transcripts
• Custom action item creation
• Integration with personal workflows
• Best practices for maximizing value

Set Usage Guidelines:

Establish clear policies:

When to use AI assistant:

• All team meetings (default on)
• Client/prospect calls (sales, consulting)
• Project discussions
• Planning and strategy sessions

When NOT to use:

• Sensitive HR discussions
• Confidential strategic planning (unless securely configured)
• Personal 1-on-1s (optional, participant discretion)
• Legal/compliance discussions (check with legal first)

Privacy and consent:

• Inform external participants AI is recording
• “This meeting is being recorded and transcribed” notification
• How to opt out
• Data retention and security policies

Create Feedback Loop:

Collect team input:

• Weekly survey: How useful are meeting summaries?
• What’s missing from summaries?
• Technical issues encountered?
• Feature requests?
• Use cases we haven’t explored?

Phase 5: Optimization and Advanced Use Cases (Ongoing)

Continuous Improvement:

Based on usage data and feedback:

Summary Quality:

• Refine AI prompts for better summaries
• Adjust summary length and format
• Improve action item extraction accuracy
• Better decision documentation

Integration Optimization:

• Streamline CRM updates
• Improve task creation logic
• Better Slack/email notifications
• Enhanced search functionality

Workflow Automation:

• Add new automated workflows
• Improve existing automation reliability
• Reduce manual review requirements

Advanced Use Cases:

Meeting Analytics:

• Track meeting time by type/project
• Identify time spent on different topics
• Measure talk time distribution (who dominates conversations?)
• Sentiment analysis (team morale trends)

Coaching and Development:

• Sales call coaching (what separates successful calls?)
• Meeting facilitation improvement (track questions-to-statements ratio)
• Communication pattern analysis

Knowledge Base Development:

• Create searchable company wiki from meeting archive
• Automatically generate FAQs from recurring questions
• Build onboarding resources from captured expertise

Competitive Intelligence:

• Track competitor mentions across all meetings
• Analyze customer feedback themes
• Identify market trend signals

Customer Success:

• Analyze customer calls for satisfaction signals
• Identify churn risk indicators in conversation patterns
• Track feature requests and customer needs

Tools and Technology Required

Core AI Meeting Assistant Platform:

• Otter.ai: $10-20/user/month
• Fireflies.ai: $10-19/user/month
• Fathom: $29/user/month (teams)
• Grain: $15-29/user/month

Custom AI Processing (Optional):

• OpenAI GPT-4 API: $30-100/month (for enhanced summaries)
• Anthropic Claude API: $30-100/month

Automation Platform (Optional):

• Make.com or Zapier: $20-100/month
• n8n: $20/month or self-hosted

Integrations (typically included in meeting assistant platform):

• CRM (Salesforce, HubSpot, etc.)
• Project management (Asana, Monday, Jira)
• Communication (Slack, Teams)
• Notes/wiki (Notion, Confluence)

Total Technology Investment:

Basic Setup (small team): $10-20/user/month Enhanced Setup (custom AI processing): $15-30/user/month Advanced Setup (full automation): $25-40/user/month

For 10-person team:

• Basic: $100-200/month
• Enhanced: $150-300/month
• Advanced: $250-400/month

ROI typically positive within first month (2-4 hours saved per person weekly = 80-160 hours monthly for 10-person team).

Common Challenges and Solutions

Challenge: Transcription Accuracy Issues

AI occasionally misunderstands words, particularly technical terms, names, or accents.

Solution:

• Add custom vocabulary (company names, products, industry terms)
• Speaker training (improve voice recognition)
• High-quality microphones (better audio = better transcription)
• Speak clearly and minimize cross-talk
• Edit transcripts when accuracy matters (legal, client-facing summaries)
• Use human review for critical meetings initially

Challenge: Privacy and Security Concerns

Team members uncomfortable with AI recording, clients concerned about data security.

Solution:

• Clear privacy policy (how data is used, stored, protected)
• Always announce AI presence (“This meeting is being recorded”)
• Allow easy opt-out (simple way to exclude AI from specific meetings)
• Choose platforms with strong security (SOC 2, GDPR compliant)
• Configure retention policies (auto-delete after X days if appropriate)
• For sensitive industries, use self-hosted or private-cloud solutions

Challenge: Information Overload

Too many summaries, transcripts, notifications overwhelming rather than helping.

Solution:

• Smart notification rules (only notify for action items assigned to you)
• Summary consolidation (daily digest instead of per-meeting emails)
• Prioritization (star important meetings, archive routine ones)
• Search instead of read (use search functionality, don’t read all summaries)
• Customization (different summary depths for different meeting types)

Challenge: Team Adoption Resistance

Some team members skeptical or resistant to AI assistant.

Solution:

• Start with volunteers (early adopters demonstrate value)
• Showcase quick wins (time saved, action items not lost)
• Address concerns directly (privacy, job security, complexity)
• Make it optional initially (build confidence before mandating)
• Provide excellent training and support
• Share success stories from team members

Challenge: Action Items Not Actually Completed

AI extracts action items, but completion rates don’t improve.

Solution:

• Ensure action items flow into task management systems (not just mentioned in summaries)
• Assign clear owners (AI should identify who committed to what)
• Set specific deadlines (vague “follow up on this” doesn’t work)
• Follow-up automation (reminder notifications for overdue items)
• Manager visibility (dashboards showing team action item completion)
• Review in subsequent meetings (accountability for prior commitments)

Challenge: External Participants Uncomfortable with AI

Clients or partners hesitant about being recorded.

Solution:

• Always ask permission (“Is it okay if we record for notes?”)
• Explain benefits to them (“Ensures we capture your feedback accurately”)
• Offer to share summary (“You’ll get meeting notes afterward”)
• Respect “no” gracefully (some meetings are human-only)
• Professional approach (position as documentation tool, not surveillance)
• Share excellent documentation (clients appreciate thorough notes)

FAQ: AI Meeting Assistants

Is AI recording legal?

Legality varies by location. In “one-party consent” states, you can record if one participant (you) consents. In “two-party consent” states, all participants must consent. For interstate/international calls, safest approach: inform all participants and get verbal consent. Always check local laws or consult legal counsel.

What about confidential or sensitive meetings?

Use discretion. For highly sensitive discussions (legal, HR, confidential strategy), you can exclude AI assistant. Most platforms allow per-meeting opt-out. For regulated industries (healthcare, finance), choose HIPAA or SOC 2 compliant platforms.

Can AI distinguish between different speakers?

Yes, most platforms do speaker identification (Speaker 1, Speaker 2). Accuracy improves with voice training and consistent participants. Some platforms allow speaker labeling (“Speaker 1 is John Smith”). Video meetings provide better speaker identification than audio-only.

How accurate is transcription?

Current platforms achieve 90-95% accuracy with clear audio and standard English. Accuracy decreases with poor audio quality, heavy accents, technical jargon, or cross-talk. Sufficient for most business purposes; critical/legal documentation may need human review.

What happens to meeting recordings and data?

Depends on platform. Most store encrypted data on their servers, with retention policies you configure (30 days, 1 year, forever). For maximum control, use platforms with self-hosting options or export/delete capabilities. Review privacy policies carefully.

Can we customize what AI includes in summaries?

Yes. Most platforms allow template customization. For advanced needs, custom AI processing (GPT-4/Claude with specific prompts) provides complete control over summary format, length, and content.

How long before team sees value?

Immediate for time savings (no more manual note-taking). 2-4 weeks for full value realization as team adjusts workflows to leverage summaries, action items, and searchable archive. ROI is clear within first month for most teams.

Getting Started with Your AI Meeting Assistant

AI meeting assistants eliminate the productivity drain of meeting administration while improving information retention, follow-through, and institutional knowledge. The technology is mature, affordable, and delivers ROI within weeks.

The question isn’t whether to implement AI meeting assistants—it’s how quickly you can deploy them before your team wastes another hundred hours on manual note-taking.

Immediate Next Steps:

1. Calculate current meeting administration time: How many hours weekly does your team spend on meeting notes and summaries?

2. Select platform: Choose AI meeting assistant matching your tech stack, budget, and primary use case.

3. Start pilot: Deploy with 3-5 early adopters for 2 weeks, gather feedback, refine approach.

4. Roll out to team: Train everyone, set usage guidelines, monitor adoption.

5. Optimize workflows: Build automations connecting meeting insights to CRM, project management, and communication tools.

Ready to Deploy Your AI Meeting Assistant?

At AI Workshop Chicago, we teach Chicago professionals to implement and optimize AI meeting assistants in our intensive weekend workshops.

You’ll leave with:

• AI meeting assistant configured for your organization
• Custom workflows automating meeting follow-up
• Integration with your CRM and project management tools
• Team training materials and adoption strategies
• Advanced use cases customized for your industry

Our next Chicago workshop is designed for operations leaders, sales managers, and productivity-focused professionals ready to reclaim hours weekly from meeting administration.

[Register for our next AI Agent Workshop →]

Questions about whether AI meeting assistants are right for your team?

Schedule a free 15-minute consultation. We’ll discuss your meeting patterns, documentation needs, and current challenges, then recommend the optimal approach.

[Book your free consultation →]

The future of meetings is AI-documented, fully searchable, and perfectly followed-up—no human effort required. The teams adopting these tools now gain compounding productivity advantages while competitors waste hours on manual note-taking.

Start deploying your AI meeting assistant today.