Board expertise is one lens through which investors can scrutinize companies’ efforts to manage the risks and opportunities of AI. But the sudden emergence and rapid evolvement of AI as a business-ready technology suggests that boardrooms may lack expertise that could help directors and companies navigate these challenges. Despite some experiments giving generative AI a direct voice in the boardroom, human directors remain corporations’ ultimate decision makers.[1] Given this fact, it’s important for investors to have a sense of how well those human directors understand this new technology, as this could have an effect on how well companies are managed.
Using GPT-4 to evaluate AI expertise
To understand how well-prepared boards are to address AI today, we used OpenAI’s GPT-4 model to read the biographies of over 24,000 directors and categorize these directors under three definitions of AI expertise: direct, ancillary and transferable.
Definitions of AI expertise
| Type | Description | Examples |
|---|---|---|
| Direct | Expertise specifically related to the development and application of AI. | - Professional experience in machine learning. - An advanced degree in computational linguistics. |
| Transferable | Expertise that can provide a foundational background for understanding the development and application of AI. | - Professional experience in data science. - An advanced degree in mathematics. |
| Ancillary | Expertise related to potential risks and consequences of AI. | - Professional experience in data privacy. - An advanced degree in intellectual property law. |
Source: MSCI ESG Research.
We applied these definitions to 24,035 individuals who served on the board of a constituent of the MSCI ACWI Index as of March 25, 2024.[2]
GPT-4 estimated that direct AI expertise was scarce, with just 2.4% of individuals meeting our definition. This is a lower frequency than even risk-management expertise — the rarest skill we evaluate for directors in our current corporate-governance methodology, with just 3.8% of individuals in this sample qualifying as risk experts.
Estimated percentage of over 24,000 individuals with AI expertise, MSCI ACWI Index
Though few in number, GPT-4 found that directors with AI-related experience were widely distributed across sectors. One in five companies had a direct AI expert on hand, comparable to risk-management expertise, which was found at 23% of boards. Half of companies had access to a director with transferable or ancillary AI expertise. While higher than the other categories, this was still well below rates for financial expertise (found on 97% of boards).
Companies with at least one estimated AI expert per sector, MSCI ACWI Index
The information-technology sector led the market in terms of estimated direct and transferable AI expertise — hardly surprising, given the importance of new technologies to the sector. But the sector was tied with the materials sector for the lowest estimated proportion of boards with ancillary expertise. These estimates suggest that the boards of some technology companies may struggle to oversee the legal, regulatory and reputational impact of AI on their business without efforts to enhance board expertise.
Load management
The relatively small number of AI experts suggests that these individuals may be especially sought after by board-nomination committees and could thus become vulnerable to “overboarding” — that is, serving on an excessive number of boards. Overboarded individuals risk seeing their overall effectiveness as a director on any one of their boards reduced due to the total volume of work to which they are expected to contribute.
Overboarded individuals by estimated AI-expertise category, MSCI ACWI Index
To date, direct AI expertise has been something of an exotic commodity, with estimated experts having lower overboarding rates than any other expertise category — including directors with no AI expertise — despite the small number of estimated experts. In contrast, ancillary experts had higher overboarding rates than any other type of director we evaluated. These existing board commitments suggest that ancillary expertise may be hard to acquire through director recruitment alone.
For all types of AI expertise, director recruitment is only one approach available to boards. Boards can also rely on director education — whether with external consultants or internal experts — to leverage existing expertise and upskill boards organically. This approach could help to minimize risks from overboarding, as well as other potential risks from a reliance on “specialist directors.”[3]
Preparing for the future of business
Reviewing the expertise of boards is just one way investors can analyze companies’ efforts to manage the risks and opportunities of AI. Our recent paper, AI Engagement: A Stakeholder Approach, sets out to help investors engage with companies on a broad set of AI risks. It provides sample engagement questions and evaluation approaches addressing risks related to four key stakeholder groups: the board and management, customers and data subjects, companies' workforce and the environment and climate.[4]