Board portals are evolving fast. What used to be a secure document delivery tool is turning into an intelligent workspace where AI and large language models (LLMs) summarise packs, surface risks, and help directors navigate years of material in seconds. For governance teams, this shift creates a new challenge. It is no longer enough to ask whether a solution is safe and convenient. The real question is whether an AI-first platform strengthens board decision-making without undermining accountability.
Research from IMD describes how AI is transforming boards by enhancing risk management, decision quality, and efficiency when used thoughtfully. Other commentators note that AI-driven analysis of board packs and decisions can help boards focus on what is genuinely material instead of being buried in detail. Modern teams need criteria that reflect this new reality, not just a checklist of traditional features.
This guide sets out practical evaluation points for anyone assessing AI-first board software.
1. Start with governance outcomes, not features
The first step is to be clear about why you want AI inside the board portal.
Governance teams should define desired outcomes such as:
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Better use of board and committee time
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Clearer visibility of emerging risks and themes
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Stronger continuity of institutional memory
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Reduced manual work for secretariat and administrators
An AI-first platform should be judged on how well it supports these outcomes, not on how many algorithms it contains. If a feature does not help the board prepare, discuss, or follow up more effectively, it is noise.
2. Evaluate AI capabilities through real board use cases
Instead of accepting generic “co-pilot” promises, ask vendors to demonstrate specific board workflows. For example:
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Summarising a 200-page risk or strategy pack into a concise briefing
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Highlighting changes between this quarter’s report and last quarter’s
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Surfacing all past decisions related to a particular project or risk
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Enabling natural language search across historic minutes and papers
Useful questions include:
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How accurate are summaries when documents include complex financial or legal content?
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Can the system show exactly which passages were used to generate a summary?
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Does search work across committees and years, or only within a single meeting?
Thoughtful providers will be able to explain how their AI supports better board performance rather than only shorter reading time.
3. Check security and data protection at AI level, not just platform level
AI-first board software must meet the same security expectations as any critical governance system, and then go further.
Key points to test:
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Data flows and residency
Where is data stored and processed, and under which legal jurisdiction? Are AI models hosted in the same controlled environment, or does content leave the platform for processing elsewhere? -
Use of public versus private models
Confirm that confidential board material is never sent to public consumer AI services. All AI processing should take place in private, enterprise-grade environments. -
Training and reuse
Ask whether your documents contribute to general model training for other clients. For a board-level system, the answer should be no.
Recent reports on board-level AI governance stress that directors face new liabilities if AI is adopted without clear controls over legal, ethical, and reputational risk. Your evaluation should reflect that reality.
4. Assess explainability and auditability
For governance teams, AI is only helpful if its behaviour can be explained and reviewed.
When you see a summary, insight, or suggestion, you should be able to:
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Trace it back to specific documents and passages
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See which date range and data sets were included
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Understand any filters or parameters that were applied
This matters for three reasons:
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Directors must be able to verify context quickly.
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Internal audit needs to test how the tool behaves.
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Regulators or investigators may one day ask how a particular analysis was produced.
Guidance from organisations such as NACD highlights the need for boards to align AI oversight with their core fiduciary responsibilities and to avoid gaps in governance coverage. That includes the tools they use themselves.
5. Look at usability through a director’s eyes
AI-first board software is only valuable if directors actually use the intelligent features. Usability is not a soft concern. It is a core evaluation criterion.
Consider whether:
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AI features appear where directors already work, for example next to agenda items
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Natural language search is simple and forgiving, not fussy about wording
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Summaries are short, accurate, and written in clear language
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There are obvious links back to source documents for anything important
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The interface remains calm and focused, without constant prompts or pop-ups
Governance teams may also want to test how the platform performs on tablets, given how many directors still prepare on iPads.
6. Ensure AI supports, not replaces, human judgement
A good AI-first platform respects the boundary between assistance and decision-making. It presents drafts, patterns, and prompts. It does not claim to recommend or decide.
In product demonstrations and documentation, look for:
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Clear labels showing which content is AI generated
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Prompts or warnings that remind users to verify important information
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Workflows that require human review before anything becomes part of the official record, such as minutes or decisions
If the messaging suggests that AI will “do the thinking” for the board, that is a red flag.
7. Review the vendor’s AI governance and roadmap
Finally, modern governance teams need confidence that a vendor can keep pace with regulation and practice.
Questions to ask:
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Do you have an internal AI governance framework and ethics policy?
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How do you monitor models for bias, drift, and security vulnerabilities over time?
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How do you plan to adapt as AI regulation evolves in our key markets?
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What independent assurance, certifications, or external reviews support your claims?
You are not just buying features. You are entering a long-term partnership in a fast-moving area.
Bringing it together: a structured evaluation checklist
When comparing AI-first board software options, governance teams can create a matrix that scores each product against:
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Strategic fit and governance outcomes
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Breadth and depth of AI use cases
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Security, data protection, and AI control
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Explainability and auditability
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Usability for directors and administrators
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Vendor governance, assurance, and roadmap
This turns abstract AI claims into concrete comparisons.
AI-first board software can genuinely improve governance if it is evaluated with discipline. The best platforms will help boards see patterns earlier, prepare more efficiently, and preserve institutional memory without diluting accountability. The right evaluation criteria make it easier to tell the difference between a true governance tool and a clever technical demo.