Otter.ai, which began life as a straightforward transcription tool and now integrates with Microsoft Teams, Zoom and Google Meet, Tuesday introduced a cross-platform enterprise suite of new features that it said optimize how organizations capture, organize and leverage meeting conversations.

The company said in a release, “[without a] central repository for conversations, valuable insights go untapped. Otter enables organizations to realize the full potential of meetings by creating proprietary intellectual property from all conversations and building a comprehensive corporate knowledge base that scales with business growth.”

Sam Liang, co-founder and CEO of Otter.ai. said in an email to Computerworld,  “over the years, what started as a consumer app has become an enterprise tool as individuals have wanted to use Otter for work because the tool saves them time and increases productivity.”

He added, “with more and more enterprise customers coming to us, we wanted to ensure we were building not just a great individual AI tool, but a serious enterprise player.”

The company, added Liang, “now has over 25 million global users spanning individuals, teams, startups, and Fortune 500 companies ,and Otter has moved well beyond transcription — today’s platform seamlessly integrates with Salesforce, HubSpot, Zapier, Notion, Atlassian, and Asana.”

Asked if an answer provided by AI contains references that allow a human to check the source material, he replied, “Otter AI Chat provides users with the source for its responses, whether that’s from within Otter (such as specific meetings or notes) or from external sources like [a] CRM or integrated documents. This allows users to verify the information and access the full context behind any AI-generated answer.”

Promises can outpace safeguards

Thomas Randall, a research director at Info-Tech Research Group, said the new capabilities “capture a familiar tension in the agentic AI surge: bold productivity promises often outpace the safeguards needed for governance, accuracy, and accountability. Hallucinations are a structural feature of how these systems work and cannot be 100% mitigated.”

Crucially, he added, “such tools lack the tacit knowledge and unwritten norms that shape how departments truly operate. As a result, they reflect an organization as it is, not as it claims to be. This is a potentially uncomfortable mirror for firms that fall short of their stated values, especially in frontline roles fully exposed to customers.”

Johannes Ullrich, dean of research for SANS Technology Institute, viewed the announcement as a move by Otter to follow the lead of other companies attempting to integrate business data into AI agents to streamline customer interactions, sales or support.

The issue, he said, “has been that once data is imported into a solution like this, it is very difficult to implement any form of access control. In short, the AI agent does not know which customer is supposed to have access to what data. Even if constraints like this are implemented as part of the system prompt, they are often easily bypassed with creative prompt injections.”

For example, a sales tool with the ability to query a CRM system may be tricked into revealing special terms other customers were able to negotiate, he said.

The other risk, said Ullrich, is hallucination, “causing customers to be provided with bad information. This has already happened with automated sales agents offering discounts that were not actually available. Air Canada’s chatbot recently offered such discounts, and the airline had to honor them. Companies can be required to honor deals like that, even if they are very costly to the company.”

Randall added, “when end users fail to critically evaluate AI output (particularly new hires unfamiliar with organizational nuance), the risk compounds. Mitigating these issues will require transparent data practices, active human oversight, and distributed accountability frameworks. Traditional, linear governance models are ill-suited for managing systems that learn, adapt, and infer in real-time.”

Otter launch deemed ‘logical evolution’

Adam Preset, VP analyst for Digital Workplace at Gartner, said, “Otter.ai’s push to make meetings the center of enterprise collaboration is a logical evolution for a vendor in this space. Integrating meeting data with broader enterprise systems can unlock value, but it also introduces new considerations. Meetings are just one part of work. Leaders must recognize the limits of what meeting platforms should become within the enterprise stack.”

Expanding Otter’s access to corporate data, he said, “brings familiar but serious risks. Any third-party access must comply with existing data governance, access controls, and security policies.”

The more deeply Otter is integrated into the enterprise, the “more critical it becomes to validate its authentication, authorization, and data handling, both contractually and technically,” he said. “Trust, but verify. Organizations must rigorously test and confirm that Otter’s security claims (including HIPAA compliance) hold true in their own environments before broad deployment. Failing to do so can expose organizations to significant security and reputational risk.”

Preset also pointed out, “we are still in the early days of generative AI. Enterprises should not treat AI-generated meeting summaries or sales insights as infallible. There is real liability if an AI provides inaccurate or misleading information to client.”

If a client claims they were misled by AI-generated information, he said, ”the organization is responsible. Practical risk mitigation means never handing over unchecked AI outputs to clients, and ensuring that AI augments, rather than replaces, human expertise in customer interactions.”

No meeting platform, he said, “should be considered the ‘single source of truth’ for sales or enterprise data. At best, Otter can provide a valuable lens onto meeting conversations and related knowledge, but core systems, like CRM or contract management, remain authoritative for their respective domains. Enterprises should be wary of vendors positioning their platform as the central hub for all work.”

For organizations, he recommended that best practice should “start with clear use cases, involve IT, legal, and security stakeholders, and rigorously evaluate the platform before scaling. Ensure vendor data handling terms meet your organizational and regulatory requirements. Balance risk and value: the promise of AI is real, but so are the risks, and most organizations are right to proceed with caution and focus on proven, incremental value.”

AI meeting platforms like Otter.ai, said Preset, “offer exciting new capabilities, but enterprises must approach integration with discipline, clear governance, and a commitment to human oversight. Those who use AI to augment, not replace, human judgment will ultimately win on trust and outcomes.”