Bringing AI into the boardroom: small steps, clear oversight gains
Boards are increasingly asked to oversee complex, data-driven systems such as AI-enabled distribution models, ESG scoring engines, and operational risk analytics. While many firms have adopted AI across key functions, its use at board level remains limited.
There could be many reasons for this. For example, AI adoption would be focussed on areas that can increase revenue or reduce cost. IT resources are limited, and making things easier for board members may not be their main focus. Still, practical opportunities now exist to apply AI in ways that help directors access information more efficiently, monitor emerging issues more easily, make existing operational information useable for the board, and follow up on key decisions more consistently.
This article proposes one approach to this, in a step by step fashion. These steps are designed so that they don’t require new platforms or large-scale change. They are low-cost, measurable, and directly linked to board priorities. They also provide a foundation for more advanced tools later, guided by experience and shifting needs.
Tools
Before describing the benefits that AI can bring to board oversight, it is useful to describe one critical issue: confidentiality.
One aspect of many AI tools, such as GPT4o, is the fact that they are online, work by API, and that interaction requires the transfer of potentially confidential data to the cloud. Many directors feel uncomfortable about this. However many models such as Mistral, Claude, and Llama can be downloaded and used locally, without uploading corporate data to the cloud. Many firms do in fact run a local LLM. While these models typically do not run as quickly as cloud-based products – because firms typically do not have as many high performance GPUs available as Google for example does – local models can compensate by simply taking more time to solve the tasks assigned to them. This provides most of the benefits of LLMs whilc preserving the confidentiality of corporate documents.
Accessing information in board materials more efficiently
Boards face a regular, often quarterly, avalanche of documentation: strategy updates, risk dashboards, committee minutes, internal policies, regulatory summaries. The challenge is not just the volume – it’s understanding what needs attention, what has changed, and what is inconsistent.
AI tools can now help in many ways.
First, they can be used to summarize longer documents into key points which provide a mental map when reading these documents in detail. This allows directors to keep an overview of the document, and perhaps to gain a better understanding than they otherwise would.
Second, they can help highlight changes from previous versions. Some documents, for example, are presented and evolve over several meetings. Sometimes, small but significant changes to the documents may be missed. In this way, AI can help to focus on potential issues.
Third, they can help flag potential discrepencies across different reports. This can be particularly useful in cases where financial information may seem to contradict the narrative.
A fourth, perhaps more involved, use case could be to generally suggest questions based on shifts in narrative or emphasis across board meetings.
These tools work with current materials and reasonably modest systems. They don’t replace human judgment but support more focused and efficient preparation. They are designed so that directors can quickly identify where to spend their time.
These examples can serve as a first step, subject to the priorities of board members, which improves review efficiency and helps sharpen board discussions.
Identifying Issues That Warrant Board Attention
Directors also need to stay alert to early signs of misalignment or emerging risk. These often remain buried in routine reports, only surfacing after problems escalate.
AI tools can help highlight these issues:
- Unusual changes in fund distribution activity
- Inconsistencies in ESG scoring across portfolios
- Recurring audit items that remain open
- Shifts in key metrics relative to agreed risk appetite
These uses rely on existing data and represent a step forward from simply enhancing clarity in board materials. They do require some modeling, but they help the board focus on what’s moving – and what might merit early discussion.
This step builds anticipatory oversight, enabling directors to ask better questions at the right time.
Making Existing Insights Board-Usable
Many firms already use AI in operations and compliance: to detect conduct issues, monitor third parties, or assess ESG exposures. But the insights often stay within those teams—rarely reaching the board in a usable form.
Bringing this intelligence into the boardroom doesn’t require new tools. It requires translation:
- Short summaries of flagged trends or outliers
- Alerts organized by themes relevant to board oversight
- Notes that explain: “What changed, and why it may matter”
This later-stage step helps directors tap into existing capabilities without added complexity. It builds coordination between board priorities and the firm’s internal monitoring.
Supporting Follow-Up and Continuity
Oversight doesn’t end with a single decision. Boards must track how issues evolve, how management follows up, and whether earlier concerns reappear.
AI can support this by:
- Tracking open items from previous meetings
- Highlighting changes in language or assumptions over time
- Spotting dropped topics that haven’t been formally closed
These tools help maintain continuity – especially across long reporting cycles or changes in leadership. They keep oversight steady and reduce the risk of important issues fading from view.
This reinforces good governance practice: helping boards close the loop and stay aligned over time.
AI adoption in the boardroom doesn’t need to start big. It can start small – with tools that improve clarity, help spot signals, translate existing insights, and support follow-through.
Each step builds familiarity. And over time, boards can decide where deeper adoption brings clear value.