Can artificial intelligence (AI) help stewardship resourcing?

Jessica Gao, associate director of research at the Thinking Ahead Institute, outlines how asset owners can use AI to solve a growing resource gap in stewardship activities.

In May, we published the stewardship resourcing report, in collaboration with UNPRI, titled Putting resources where stewardship ambitions are.

TAI estimate that the industry average is likely to be around or lower than 5 per cent of total resources. The report suggests that our industry should consider doubling its stewardship resources to meet the increased demands and needs. This includes expanding stewardship activities beyond listed equities and addressing a broader range of ESG issues, including systemic risks.

A common question I face when presenting our findings is: where will these additional resources come from? How can business leaders  allocate more resources to stewardship when overall resources are constrained? The latest TAI/Future Fund asset owner peer study shows that organisations recognise the need for change but struggle to find the necessary resources.

In our stewardship resourcing report, we proposed that organisations could potentially boost stewardship resources by adopting a more integrated approach and reallocating some resources from asset allocation. However, this is unlikely to be sufficient because:

• existing resources for asset allocation may already be stretched thin
• stewardship requires a different skill set that may not be covered by current resources
• training for stewardship roles demands both time and additional resources.

A different example, however, shows what can be done in terms of shifting resourcing. Following years of little meaningful change, global AI adoption jumped by over 30 per cent between 2023 and 2024, with the use of generative AI almost doubling. The financial services industry is making big strides in AI adoption, second only to the technology sector (though still far behind)[3].Could stewardship benefit from this increased AI adoption and its ability to refine complex processes and data sets?

Using AI in voting to free up stewardship resources

In its latest stewardship report, the £35 billion Brunel Pension Partnership, dedicated a page to the use of AI in its stewardship activities.

Brunel employed an AI-drive tool to analyse and compare the voting guidelines of around 20 asset managers and owners. It also used AI to assess the implementation of their voting guidelines and generate a quarterly report.

This approach improved the reliability of the reports and significantly reduced the time and effort previously required for manual checks. The report can then be used to address any inconsistencies in voting with service providers.

AI adoption in voting allows Brunel to free up resources to engage with investee companies and other core stewardship activities. Oliver Wright, responsible investment officer at Brunel, expects increased use of AI in stewardship as the technology advances and more tools become available.

Using AI with antimicrobial resistance (AMR) issues

AI is increasingly being used to address sustainability issues and systemic risks. For example, AMR is recognised by the World Health Organisation as one of the leading global threats to health and development[4]. It’s one of the top systemic risks that investors focus on through stewardship activities[5]. AI tools have been used to monitor and optimise livestock management, including disease control and to guide antibiotic use on farms[6].

Additionally, AI tools have been developed to predict potential antibiotic resistance[7]. These advances may reduce the time and effort investors need to spend on AMR-related engagement activities, allowing them to reallocate resources to other challenging areas, such as social issues.

Possible path forward

We are at the early stages of AI adoption. With the extraordinary surge in interest and investment in AI, organisations in our industry can unlock its significant potential and find the much-needed resources to drive change.

AI algorithms can analyse investment data, detect compliance issues, and track performance against ESG criteria with greater accuracy. This will enable investment managers to oversee their portfolios more effectively, utilising real-time insights generated by AI.  Key areas where AI can make an impact include:

• Data analysis and risk management: AI algorithms are excellent at predictive analysis and forecasting. They can rapidly process vast amounts of data to uncover hidden patterns, trends and correlations. AI-powered risk models can potentially better identify ESG risks that may impact a company’s long-term value. These tools may significantly reduce research time, allowing investment management teams to make informed decisions based on a deeper understanding of the companies in their portfolios.
• Engagement automation: AI tools, such as AI driven chatbox and virtual assistants,  could streamline communications and standard engagements.  Some engagement tracker tools are already available on the market for investors to leverage.
• Reporting and communication: stewardship reporting and communication currently consume a considerable portion of stewardship resources. By employing automated reporting, natural language processing and sentiment analysis, could some of these resources be redirected to other core stewardship activities?

At the same time, we need to remain mindful of the issues and risks associated with using AI. AI is only as good as the data it is fed, and, at worst, it can amplify incorrect or biased data.

It is crucial to build inclusive, people-centered AI solutions that enhance our decision-making rather than completely replace human judgment. AI should be a tool that supports and augments human expertise, not one that diminishes its value.

AI can play a crucial role in addressing the stewardship resources problem in the investment industry. By integrating AI, we can leverage technology to support human judgment and expertise, ultimately leading to better outcomes for investors, the industry and society as a whole.

Jessica Gao is associate director, research at the Thinking Ahead Institute.