Board packs are getting heavier. Strategy decks, risk reports, ESG disclosures, cyber updates and transaction papers often run to hundreds of pages. Even highly engaged directors struggle to absorb everything before each meeting.
Large language models (LLMs) are starting to change that reality. When used with care, they help professional boards digest complex information faster, without handing decision making to a machine. This article explains how LLMs are being used in practice, what they are good at, and which guardrails keep judgement and accountability in human hands.
Why the modern board pack is so hard to manage
Several forces have combined to make board materials more complex:
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Regulation and disclosure requirements keep expanding.
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Risk landscapes, especially cyber and geopolitics, move quickly.
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ESG, climate and stakeholder topics add new layers of reporting.
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Data from across the organisation is easier to collect and present.
The result is that boards receive more information, more often, in more formats. Time, however, has not expanded. Directors still have the same number of days between meetings to prepare.
This is where LLMs can help. They do not replace reading. They help focus reading on what matters most.
What LLMs actually do for directors
In a professional board environment, LLMs are rarely used as standalone chatbots. They are embedded into secure board portals and collaboration tools that already hold agendas, packs and minutes.
Common uses include:
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Summarising long documents
Turning a 60-page risk report into a one-page brief that highlights key changes since the last meeting. -
Highlighting signals and themes
Picking out recurring issues across multiple committee reports, such as persistent control weaknesses or culture concerns. -
Comparing versions
Showing what has changed between this quarter’s policy or plan and the previous version, so directors can see the real movement. -
Answering navigation questions
Helping directors find where a topic, such as third-party risk or capital allocation, was last discussed in past minutes.
Used this way, LLMs become an assistant that makes the pack more navigable and less overwhelming.
A typical workflow: from upload to insight
In leading organisations, the use of LLMs around board packs often follows a simple workflow.
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Secure ingestion
Board and committee papers are uploaded into a secure portal. Documents are tagged to agenda items, owners and entities. -
Indexing and preparation
The system creates an index of the text content and metadata so the LLM can work with smaller, relevant chunks instead of whole packs. -
Generation of summaries and overviews
For each agenda item, the model produces a draft summary of the underlying documents, including key risks, decisions required and open questions. -
Human review and refinement
The corporate secretary or paper owner reviews each summary, edits for accuracy and nuance, and approves it for inclusion in the pack. -
Use by directors
Directors read the summaries first, then drill into the source documents for areas that need more scrutiny. -
On-demand queries
Before or during the meeting, directors use natural-language search to find past decisions or references without trawling through archives.
This approach treats the LLM as infrastructure that supports preparation and discussion rather than a stand-alone application.
Benefits that professional boards report
When implemented well, boards and governance teams typically see several gains:
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Time saved on preparation for corporate secretaries and report authors.
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More focused reading time for directors, who can spot priorities quickly.
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Better continuity because it is easier to reference past discussions and decisions.
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Clearer articulation of key risks and decisions in the pack itself.
Research on generative AI in knowledge work, including analysis from publications such as Harvard Business Review, suggests that the biggest productivity benefits come from drafting, summarising and information retrieval tasks rather than from fully automated decision making. Articles on how generative AI changes strategy underline that leadership teams gain the most when they use these tools to support, not replace, their own thinking.
Guardrails professional boards put in place
The same boards that experiment with LLMs are often cautious by nature. They sit under regulatory scrutiny and understand fiduciary duty. As a result, they tend to put clear guardrails around AI use.
Typical safeguards include:
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Human review as a hard rule
No AI-generated text goes into official minutes or packs without review and approval by a named person. -
Scope limitations
LLMs are allowed to summarise and help navigate documents but not to generate recommendations on strategy, remuneration or major transactions. -
Secure environments only
Directors and executives are asked not to paste confidential material into public AI tools. Instead, they use features embedded in controlled platforms. -
Clear policy and training
A short AI policy explains acceptable use, and directors receive briefings on how the tools work, what they can and cannot do, and how to challenge outputs.
Guidance from organisations such as MIT Sloan Management Review on how boards can govern AI stresses the need for clear accountability, risk frameworks and continuous learning at the board level. Professional boards use LLMs within that kind of structured governance, not outside it.
Risk and compliance considerations
LLMs raise a set of specific questions that boards and audit committees cannot ignore:
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Data protection and confidentiality
Are board materials processed and stored in a way that meets privacy, banking or listing rules? Is data ever used to train shared models? -
Accuracy and bias
How are errors in summaries detected? Are there checks to ensure that repeated emphasis on certain themes does not reflect hidden bias in the model? -
Auditability
Can the organisation show which documents fed into a summary, who approved it and what changes were made?
Professional guidance from the Institute of Internal Auditors has begun to explore these questions, encouraging audit functions to include AI-supported processes in their risk assessments and assurance plans. Their materials on emerging technologies and internal audit, available on the IIA website, give a useful lens for boards that want independent comfort.
The role of specialised board platforms
In practice, professional boards rarely build their own AI stack from scratch. They work with specialised governance platforms that combine secure document management with carefully designed AI features.
Solutions such as boardroompro aim to bring agendas, packs, minutes and AI-supported summaries into a single environment, with consistent access controls and audit trails. This reduces the temptation for directors to experiment with unsecured tools and helps organisations apply one set of policies across all board work.
Practical starting points for boards
Boards that want to move beyond theory can start small and build confidence over time:
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Pilot on a single committee
Try LLM-based summaries for one risk or audit committee cycle, with close human review and feedback. -
Focus on past packs first
Use the tools on historical materials to test accuracy and usefulness before touching live agendas. -
Update policies and training
Make sure the board’s AI and technology policies explicitly cover use of LLMs in board work. -
Ask management for a roadmap
Request a simple plan that shows where AI will be introduced into board processes, how it will be governed and how success will be measured.
By following these steps, professional boards can use LLMs to digest complex packs faster while keeping their core responsibilities intact. The technology becomes a co-pilot in information processing, not a substitute for human oversight and judgement.