2. Define directors’ technology skills with real specificity
Some investors said that a board skills matrix noting “technology” expertise may not be specific enough anymore. They encourage companies to be more precise in the skills matrix and/or in director bios as to the details of what directors’ technology skills and experiences include. That does not mean they expect to see an AI expert on every board. Most emphasized their interest in understanding how the full board is building AI knowledge; a few said AI expertise on the board should be proportionate to the size of the bet the company is taking on AI. More generally, some investors are also looking at how the skills categories within matrices evolve to keep up with changing oversight needs.
What boards should do
What boards should do: Explore ways to sharpen the board skills matrix and bios with specific and relevant technology skills and experiences and provide examples tied to company strategy and needs.
3. Oversee how management protects the talent pipeline as AI reshapes work
Over half (53%) of the investors we spoke with raised concerns about AI’s impact on the workforce, particularly the risk that the loss of junior talent (as AI replaces entry-level work) could lead to “brain drain” and reduce the talent pipeline for leadership. They encourage companies to be transparent about how AI will affect human capital needs, and to include details in disclosures about how the company is maintaining human expertise, and upskilling employees in order to take full advantage of advanced technologies. Some are also asking whether management provides a channel for employees to raise AI concerns.
What boards should do
Critically examine how management is balancing cost savings with managing the risks of long-term talent erosion and potential customer and investor backlash, and oversee how management is communicating AI-related talent decisions to investors. See more in How boards can lead in a world remade by AI and Can AI advance toward value if workforce tensions linger?.
4. Monitor communications on AI investment progress and ROI
The long-term investors we spoke with generally said they do not expect immediate ROI but would like updates on AI investments, including on equity and debt financing for AI projects, the pace at which returns are expected, the KPIs being used to measure progress, and how these investments fit into the company’s long-term strategy and capital allocation plan. Some wanted greater insight into how companies assess the adequacy and timing of AI investments. A few wondered how activists may assert themselves — for example, through proxy challenges — in this area where ROI is not apparent and companies lack a strong narrative for how those investments are progressing.
What boards should do
What boards should do: Consider the effectiveness of investor communications on how the company’s AI investments drive strategic goals and long-term competitiveness. Encourage management to strengthen communications on how the company is learning, course-correcting and making progress. Monitor the company’s performance on AI investments related to peers.
5. Prepare for AI-powered investor stewardship
Investors are using AI to integrate data sets and distill research at scale, and exploring use cases to assess director quality, map director connections, and deepen their company performance analysis. None said they are using AI to make voting decisions today, and a few said pilots show limits in consistently interpreting and applying policies. Still, particularly given the current headwinds facing proxy advisors, some said they are accelerating internal R&D in this area and future use cases could conceivably include proprietary AI proxy voting agents.
What boards should do
What boards should do: Encourage management to make disclosures machine-readable and comprehensive so that they are fully available to investors as they rely more on AI for scanning and synthesizing information. Ask management how they are using AI to anticipate and prepare for investor questions.