Board software is changing quickly. What began as a secure place to upload PDF packs is turning into an intelligent workspace where large language models (LLMs) act as meeting co-pilots. For UK boards, this creates both opportunity and risk. The right tools can cut preparation time, improve oversight, and surface hidden issues. The wrong tools can introduce security gaps, opaque algorithms, and confusion about accountability.
Platforms such as board-rooms already show how digital portals can streamline access to papers, agendas, and minutes. The next step for many organisations is deciding which systems are truly “AI ready” and which simply bolt on a chatbot.
This article offers a practical lens for evaluating board software in the age of AI and LLM co-pilots.
From document portals to AI co-pilots
Traditional board portals solved the problem of distribution. They made it easier to share documents securely, manage versions, and keep an audit trail. They did not, however, help directors understand complex material faster.
AI enhanced tools change that. They can:
-
Summarise long reports into clear briefings
-
Highlight trends across multiple meetings and committees
-
Provide natural language search across past papers and minutes
-
Suggest questions or angles of challenge for directors to consider
KPMG’s work on “AI in the boardroom” notes that many boards are only beginning to understand how far AI could reshape oversight, and that directors risk misjudging its implications if they treat it as a narrow efficiency tool. That is exactly why evaluation criteria need to expand beyond basic features.
What AI enhanced board software should actually deliver
When you look past marketing language, AI enabled board software should support three core outcomes.
1. Better preparation
LLM co-pilots should help directors move from volume to insight:
-
Concise summaries of long papers
-
Comparison of this quarter’s numbers with previous periods
-
Clear lists of key risks and decisions for each agenda item
Directors still read the underlying documents, especially on high risk topics, but they do so with sharper focus.
2. Stronger oversight
AI should help boards see patterns, not just pages:
-
Links between risk reports, audit findings, and performance trends
-
Recurring issues that appear across multiple committees
-
Indicators that something is changing faster than expected
Governance analysts increasingly argue that boards which use AI to strengthen their view of risk and opportunity can gain a strategic advantage, provided controls are robust.
3. Less administrative friction
For company secretaries and governance teams, AI co-pilots should:
-
Speed up agenda and pack assembly
-
Flag missing documents or overdue reports
-
Generate draft minutes and action logs for human refinement
If the tool does not meaningfully reduce workload, it is probably not worth the added complexity.
Non-negotiables: security and governance of AI
In a board context, no feature matters more than security and governance. Before looking at any clever co-pilot functionality, directors should confirm that the software meets strict standards.
Key questions include:
-
Where is board data stored and processed, and under which jurisdiction?
-
Is all AI processing done inside a private, enterprise environment, or do prompts and documents go to public models?
-
How is access controlled, logged, and reviewed?
-
Can the organisation disable or limit AI features if policy or regulation changes?
Recent guidance from the UK Financial Reporting Council on the use of AI in audit emphasises proportionate documentation, clear accountability, and careful explanation of how AI tools are used. Those principles translate directly into board software choices.
Boards should also understand how the vendor approaches AI governance more broadly. The Institute of Directors, for example, has set out principles for AI governance that treat AI as a strategic, ethical, and legal issue rather than a purely technical one. Any serious provider should be able to explain how their product aligns with similar expectations.
Evaluating AI and LLM capabilities: features that matter
Once security and governance are in a good place, boards can compare AI features more confidently. Focus on what genuinely helps rather than novelty.
Important capabilities include:
-
High quality summarisation
Can the system summarise complex financial, risk, and regulatory documents in a way that is accurate, balanced, and easy to review? -
Transparent behaviour
Does the tool show which sources it used for a summary or answer, and can users easily check the original documents? -
Configurable scope
Can administrators control which data sets and which parts of the platform the LLM can access? -
Robust natural language search
Can directors ask clear questions across past packs and minutes, such as “when did we last discuss this supplier” or “how has our cyber risk profile changed”? -
Strong guardrails against error
Does the vendor provide guidance and in-product warnings about the possibility of AI “hallucinations”, and are there workflows that encourage human verification?
Beware tools that promise “automatic insights” without explaining how they reach those conclusions.
Questions every board should ask vendors
When evaluating board software in the AI era, boards can use a simple set of questions.
-
Use cases
Which board use cases are your AI features designed for, and which use cases do you explicitly advise against? -
Data handling
How do you ensure that our board data cannot be used to train models for other clients, or by third parties? -
Explainability
If an AI feature flags a risk or suggests a summary, how can we understand why it reached that result? -
Controls and oversight
What configuration options do we have to limit or phase in AI capabilities, and how do you support policy enforcement? -
Roadmap
How will your AI features evolve in the next one to three years, and how do you factor in regulatory developments?
The answers will often tell you more than a feature checklist.
Common pitfalls when choosing AI era board software
Boards should be cautious about a few recurring traps:
-
Choosing a product because it has “AI” in the marketing, without linking features to real board tasks
-
Underestimating the change management required for directors and governance staff
-
Ignoring jurisdictional and sector specific rules on data residency and confidentiality
-
Allowing experimentation with unapproved consumer tools alongside an official portal
The aim is to centralise board work in one secure, well governed environment, not scatter it across different apps.
A balanced approach to AI and co-pilots in the boardroom
AI and LLM co-pilots can lift the quality and speed of board work if they are implemented with care. The evaluation of board software now has to look beyond user interface and storage capacity. It must assess how AI features support the board’s core responsibilities, and whether they do so under strong governance.
Boards that ask disciplined questions, insist on private and explainable AI, and focus on genuine productivity and oversight gains will be better placed to choose tools that match the age of AI co-pilots without sacrificing trust or control.