Otter.ai’s new act is bigger than transcribing meetings. Under CEO Sam Liang, the company is retroactively positioning itself as a conversation intelligence layer for the enterprise, integrating tools that push insights from calls and presentations into the systems where actual work occurs. The idea is straightforward: Turn ephemeral spoken communication into a persistent, searchable form of knowledge that can be used to power decisions and revenue.
From Note Taker to a Centralized Knowledge Hub
Otter made its mark with precise, searchable transcripts. Now Liang wants it to be the system of record for conversations, not simply a passive record of who said what. The new approach rests on transforming the content of meetings into structured, permissioned knowledge that teams can reuse across sales, marketing, product and support. In practice, this is about indexing not just transcripts but slides, action items, decisions and follow-ups — and then making that corpus easy to query and automate against.
That distinction matters. A transcript is a static property; a knowledge hub is something with a pulse that fuels workflows, kicks off tasks and answers questions long after the Last Session Today Everyone’s Leaving Turns Out The Chicken Parmesan Was Wonderful Banquet Room 2 empties. Liang’s wager is that the most valuable context lives in spoken conversation — and that enterprises will pay for a reliable way to capture, store and operationalize it.
Building the Enterprise Stack for Conversation Intelligence
To enable that transition, Otter unveiled an API for developing custom integrations with work platforms like Jira and HubSpot. Rather than have meeting-generated tasks, risks and customer signals go to die in the notes, which no one ever reads again, we want these valuable nuggets sent straight back into ticket queues or recorded as CRM records.
The company also released an MCP server, which allows enterprises to securely link Otter’s meeting data to external AI models. That opens the door to retrieval-augmented generation: Ask an internal assistant a question, and it can respond using citations drawn from the organization’s own conference calls and presentations. A new conversational AI agent, optimized for enterprise search, is layered atop this stack to help surface relevant snippets, decisions and owners — turning a pile of recordings into navigable memory.
If the integrations are strong, a post-demo sales touch could automatically create follow-up tasks in Jira, update opportunity notes in CRM and send a note to customer success alerting them to risks — all without any manual re-entry work. That’s the difference between a piece of software people enjoy and one that platform buying teams are going to rationalize.
The Importance of Centralized Meeting Intelligence
According to Harvard Business Review, knowledge workers spend a massive chunk of their week just looking for information. McKinsey Global Institute has also observed that a significant amount of time is spent finding context and recreating content that already exists. Meetings, after all, are where nuance is captured and decisions are made, but context frequently doesn’t make it out of those silos — the slide in one folder, the transcript in another and the action item lost somewhere in chat.
The calculus changes with this stream centralized. For instance, key takeaways from a discovery call can feed product management as a proof point for a roadmap decision, and marketing can expose verbatim customer language to refine messaging. By making conversational data addressable — who said what, attached to what account, with which next steps — companies can shorten cycles and avoid the costly “didn’t we already decide this?” loop.
Privacy Guardrails and Trust for Enterprise Adoption
The reward is tempting; the risk is real. Enterprise buyers will examine how small talk, throw-away comments and private matters are collected and shared. A lawsuit filed in August on behalf of a class accuses the company of recording private conversations without proper consent and leveraging that data for training purposes — allegations that Otter hasn’t publicly addressed in detail. The larger conversation is industrywide: Vendors in the meeting-assistant category all face the same questions.
Trust will be based on clear consent flows, granular access controls and auditability. Enterprises will likely want features such as:
- Policy-based recording
- Blacking out personal or confidential information
- Role-based permissions
- Admin-level retention rules
This will be mapped out by compliance teams to regulatory regimes such as GDPR, the California Consumer Privacy Act and its modifications, and sector-specific requirements. In jurisdictions that require consent from both parties, making a recording without express agreement is a non-starter.
Competitive Pressures and Differentiation
It is yet another crowded field in which Otter’s entry was unlikely to disrupt the top order of finish. Longtime competitors such as Fireflies.ai and newer players such as Circleback and Granola are moving beyond transcription into action items and analytics. Platform incumbents are also bundling their own assistants: Microsoft has woven Copilot through Teams and Microsoft 365, Zoom is promoting AI Companion, and productivity suites such as Notion and Slack are including AI search over internal content.
Otter’s positioning will probably be based on three things:
- Quality enterprise search across multimodal content
- Depth of integrations that reduce manual work to organize the workforce’s memory better — and, importantly, far beyond just audio/video recording apps — tying into productivity workflows as part of the post-meeting experience
- Strong governance that is already satisfying security reviews
If it can be that neutral, cross-platform place where we are conversational about what’s true, it can route these suite-native assistants rather than get disintermediated by them.
What to Watch Next for Otter.ai’s enterprise push
The leading indicators are straightforward. Is the API something customers use to wire Otter into their core systems? How often do workers quiz the new AI agent vs. skim up helpers? Do teams drive tangible time-to-insight, deal progression or ticket resolution improvements? Procurement will also seek out transparent pricing, data residency options and proof that third-party model connections do not leak sensitive content.
Liang’s approach reflects a simple reality: The value is not in the transcript, but in how rapidly an organization can turn conversation into action. If Otter can turn meetings into a trustworthy, searchable memory for the enterprise without sabotaging trust, it won’t just be a scribe. It will be infrastructure.
