AI note‑takers in the boardroom: Convenience today, legal evidence tomorrow

Artificial intelligence has slipped quietly into the modern workplace, often under the benign guise of productivity. Among its most popular incarnations are AI-powered meeting assistants. These are tools that promise to capture discussions, generate summaries, and free employees from the drudgery of notetaking. Yet beneath this convenience lies a growing legal and compliance risk that many organisations have not fully grasped: the transformation of routine conversations into permanent, potentially discoverable records.

Recent litigation in the U.S. has brought this issue into sharp focus. In February 2026, a class action lawsuit was filed against Microsoft alleging that the live transcription feature in Microsoft Teams captured voice biometric data without proper consent under Illinois law. Similar concerns surround lawsuits involving transcription platforms such as Otter.ai, where plaintiffs argue that not all participants were adequately informed that their conversations were being recorded and processed.

These cases point to a fundamental shift. What was once an ephemeral exchange can now become a structured digital artefact, complete with timestamps, speaker attribution and searchable content. This raises an important question: could a simple meeting transcript become evidence in a privacy lawsuit?

The appeal of AI transcription is obvious. In hybrid and remote workplaces, where meetings proliferate across time zones, automated notetaking offers efficiency and inclusivity. However, the assumption that these tools are harmless can be misleading.

As Ben Walker, CEO of DittoTranscripts, has observed, transcripts are not merely notes. They are data-rich records that can capture sensitive information—employment concerns, financial discussions, client data, and strategic decision-making. More importantly, they persist. Once created, they can be stored, shared, analysed and, critically, retrieved.

From a legal standpoint, this persistence is significant. Meeting transcripts may become discoverable in litigation, regulatory investigations, internal disciplinary processes or contractual disputes. A conversation that participants assumed was informal may later be scrutinised in a courtroom or by regulators.

Moreover, AI-generated transcripts are not always faithful reproductions. Many systems summarise rather than transcribe verbatim, may misattribute speakers, or omit context. In low-risk settings, such inaccuracies are inconvenient; in high-stakes environments, such as HR investigations or legal discussions, they can be problematic.

The central legal issue emerging from current lawsuits is consent. In jurisdictions such as Illinois, biometric privacy laws require explicit, informed consent before collecting data that could identify individuals, including voice characteristics. In Canada, while the legal framework differs, the underlying principles are similar.

Under Canada’s federal privacy law, the Personal Information Protection and Electronic Documents Act (PIPEDA), organisations must obtain meaningful consent for the collection, use and disclosure of personal information. Voice recordings and transcripts that can identify individuals fall squarely within this definition. Importantly, consent must be informed—meaning individuals should understand what data is being collected, why, and how it will be used.

Canada’s legal landscape is further complicated by provincial requirements. For example, some provinces operate under “one-party consent” rules for recording conversations, while others impose additional obligations in employment or healthcare contexts. In practice, organisations operating across provinces—or internationally—must navigate a patchwork of requirements.

This becomes particularly challenging in hybrid meetings. It is not uncommon for an employee to activate an AI note-taker without verifying whether all participants have agreed. In cross-border calls, this could inadvertently expose organisations to multiple, overlapping legal regimes.

One of the less appreciated aspects of AI transcription is discoverability. Once a transcript exists, it may be subject to legal disclosure obligations. This applies not only to formal records but also to drafts, summaries and stored data.

Organisations may find themselves needing to explain why the transcript was created and whether the participants consented. There are also questions over how the data was stored and protected. Failure to answer these questions convincingly can expose weaknesses in governance and compliance.

There is also the issue of interpretation. A transcript may lack nuance. Tone, context and intent can be flattened into literal text, potentially altering meaning. In disputes, opposing parties may rely on these records to support claims, even if the transcript does not fully reflect the conversation.

Canadian compliance considerations

For Canadian organisations, the use of AI transcription tools requires careful alignment with privacy and employment law. Key considerations include:

Meaningful consent: Participants must be clearly informed of recording and transcription, including any downstream uses such as analytics or training.
Purpose limitation: Data collected should only be used for the stated purpose. Repurposing transcripts—for example, for performance monitoring—could breach privacy expectations.
Data minimisation: Only necessary information should be captured. Automatic transcription of all meetings may not meet this standard.
Retention controls: Organisations must define how long transcripts are kept and ensure timely deletion where appropriate.
Safeguards: Adequate security measures must protect stored transcripts, particularly where sensitive information is involved.

With forthcoming updates to Canadian privacy legislation (including proposed reforms to PIPEDA), scrutiny of data practices is expected to intensify.

The solution is not to abandon AI meeting assistants. Rather, organisations should adopt a structured, risk-based approach. First, clear policies are essential. Employees should understand when AI note-takers can be used, and when they are prohibited, particularly for sensitive discussions such as HR matters, legal advice, or confidential negotiations.

Second, consent mechanisms must be robust. This includes visible notifications, verbal confirmation where appropriate, and documented agreement.

Third, organisations should consider tiered controls. Routine meetings may permit transcription with standard safeguards, while high-risk meetings require additional approvals or human oversight.

Finally, accuracy matters. For transcripts that may be relied upon in legal or regulatory contexts, human review should be mandatory. AI-generated summaries, while efficient, are not a substitute for validated records.