Quick answer
Generic AI notetakers can be useful for routine calls, interviews and internal catch-ups. Formal meeting minutes are a different deliverable. They may need to reflect specialist terminology, complex attendance, extensive meeting context, an established template and a controlled review process.
The practical question is not simply, “Can this tool transcribe a meeting?” It is, “Can this workflow help our team produce a consistent, review-ready record while giving us clear control over the underlying data?”
A summary is not the official record
For UK companies, the Companies Act 2006 requires minutes of directors’ meetings to be recorded and retained. That makes formal minutes a different category of output from a convenient meeting recap. Requirements differ by organisation and jurisdiction, but the distinction between a working aid and the approved record matters.
A transcript or AI summary can assist the person preparing minutes, but it should not quietly become the final record. Clifford Chance’s guidance on generative AI in the boardroom recommends clearly distinguishing temporary AI-generated material from approved formal minutes, defining who oversees the process and reviewing AI output before the minutes are approved.
The ICO’s human-review framework similarly describes meaningful human review as a structured control: reviewers need the knowledge and authority to challenge AI output, supported by clear procedures and records of overrides where appropriate.
For meeting-minute workflows, that means AI should accelerate preparation without removing accountability. The final record remains something the responsible team reviews and approves.
Seven requirements to evaluate
1. Accuracy in specialist language
Fund, insurance and corporate-services meetings contain entity names, investment names, legal terminology, acronyms and company-specific language. A plausible-looking transcript can still create significant editing work if it repeatedly misidentifies the terms that matter.
Evaluate whether the system can use approved organisational context and whether corrections improve the working output rather than leaving every reviewer to repair the same vocabulary manually.
2. Speaker attribution in complex meetings
Formal meetings are rarely one person per microphone. Ten participants may join remotely while another ten share a boardroom, with external advisers joining for individual agenda items or several rooms connected at once.
Ask how the workflow handles shared devices, hybrid attendance and speaker corrections from the live meeting through to the final draft. Attribution should remain reviewable; it should not be treated as infallible.
3. Context beyond the conversation
The discussion may rely on a 100-page board pack, agenda, prior actions, compliance registers or entity-specific documents. A transcript captures what was said, but not necessarily the source material behind shorthand references and decisions.
A stronger workflow can use controlled meeting inputs to help draft the record in context. The reviewer should still be able to check the result against the source material.
4. Formal, template-matched outputs
Mainstream meeting assistants increasingly offer configurable recaps, speaker summaries and action lists. Microsoft, for example, supports custom recap templates and tells users to verify AI-generated results. A board or investment-committee meeting may still require a five-, ten- or twenty-page document that follows an established structure, house style and level of detail as part of a wider review workflow.
Assess whether the system produces the document your team actually needs or merely gives them another intermediate output to reformat.
5. Decisions, actions and human approval
Formal meeting work is not finished when a summary appears. Teams may need draft minutes, actions, decisions, follow-ups and governance registers, followed by review, correction, versioning and approval.
Look for a defined workflow that supports human challenge and sign-off. “Human in the loop” should describe an operational process, not a disclaimer at the bottom of an AI-generated document.
6. Clear data handling and retention
Recording and transcription can create additional data to govern. The DWP’s recording and transcription policy, while specific to that organisation, illustrates the questions any buyer should ask: why is the data being created, where is it stored, who can access it, how is accuracy checked and when is it deleted?
For each product and deployment option, establish:
- whether audio recordings are created or stored;
- where transcription and AI processing take place;
- what content is retained and for how long;
- whether customer data is used to train models;
- how access, deletion and audit history are managed.
The answers should be explicit rather than inferred from a generic security statement.
7. Consistency across teams, clients and entities
A client-facing fund administrator or corporate-services provider may produce minutes across many teams, clients and legal entities. Inconsistent structure and tone create review work even when individual drafts appear reasonable.
Evaluate whether templates, contextual inputs and review controls can be applied consistently while still accommodating meeting- and client-specific requirements.
When a general notetaker may be enough
A general meeting assistant may be entirely appropriate for lower-risk internal calls where the output is a convenient recap, the meeting structure is simple and no formal record is required.
A purpose-built workflow becomes more valuable as the meeting grows more complex, the output becomes more formal, the source context becomes larger and the organisation needs stronger control over review and data handling.
A practical buyer checklist
Before selecting a tool for formal meeting minutes, ask:
- What exact document will the system produce?
- Can it follow our templates and use approved meeting context?
- How does it handle shared rooms and complex speaker attribution?
- Who reviews and approves the draft?
- Are recordings created, and what data is retained?
- Where are processing and storage located for our deployment?
- Is our data used to train models?
- Can we see an audit history and manage corrections?
- Will the workflow remain consistent across teams, clients and entities?
How Starlight approaches formal meeting outputs
Starlight is built for funds, insurance and corporate services. It connects to Zoom and Teams, produces real-time speaker-identified transcription without creating or storing meeting audio, and uses approved context such as board packs, agendas and compliance registers to prepare structured drafts.
Outputs can be matched to the customer’s templates and include minutes, actions and decisions. Drafts remain subject to human review and approval. Starlight does not train AI models on customer data and supports EU-resident Azure SaaS or deployment in the customer’s Azure environment and region of choice.
Sources
- Companies Act 2006, section 248 — UK requirement to record and retain directors’ meeting minutes.
- Microsoft Support — Recap a Teams meeting — mainstream recap use case and verification warning.
- Microsoft Support — Customize recap summaries — configurable recap templates and output examples.
- Clifford Chance — Across the Board: Generative AI tools in the boardroom — official-record distinction, review and AI-output governance.
- ICO — Human review — meaningful review, procedures and reviewer authority.
- GOV.UK — DWP Generic Recording and Transcription Policy — practical recording, storage, access, accuracy and retention questions; cited as an organisation-specific example.
- ICO — Governance and accountability in AI — information-flow and AI-governance expectations.
- Starlight AI — AI Meeting Minutes — verified product capabilities.