AI in Investment Management: 3 Ways It’s Powering Sustainable Investing

Adoption of AI in investment management is moving well beyond the realm of quantitative funds. According to Mercer, 91% of asset managers are either already using AI (54%) or planning to integrate it (37%) into their investment strategy or asset-class research.¹

So far, most of that adoption focuses on enhancing existing capabilities: expanding data sets, generating new investment ideas, and accelerating analysis. In most cases, AI supports human judgment rather than replacing it. More than half of AI-integrated teams report that AI informs their decisions, while only a fifth say it proposes actions directly.²

For sustainable investors, this augmentation model is especially valuable. As regulations tighten and data demands grow more complex, AI can help investment teams stay ahead without losing control over the decisions that matter most.

The infographic below highlights three practical ways investors are using AI in investment management to achieve sustainability goals. These applications are already being used by leading firms and are shaping daily investment workflows.

3 Ways AI Is Already Powering Sustainable Investing

AI is changing how investors assess risk, conduct research, and meet reporting demands. These three use cases show where it’s delivering value today.

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The Challenge: Spotting controversies before they hit the bottom line

Clarity AI found that major sustainability controversies can reduce market value by up to 11.8% depending on severity and issue type.³

AI as the Solution

Natural language processing (NLP) tools like Clarity AI’s Controversies solution help investors monitor emerging risks and respond with timely insights. By analyzing 250,000 news items daily across 200 countries, the tool classifies incidents into 39 categories and assigns severity scores based on the issue, the company’s response, and potential risk to enterprise value.

The Challenge: Scaling research across thousands of companies

The traditional model is time-consuming and limits coverage. If a team of analysts can only deeply assess a few hundred companies, thousands more remain in the dark.

AI as the Solution

Generative AI tools are starting to close the gap, helping analysts cover more ground, faster, with consistent depth and quality. Studies suggest efficiency gains of 30–40% when Generative AI tools are used for tasks like summarizing reports, benchmarking performance, and identifying key sustainability risks.⁴ This frees up time for higher-value analysis and decision-making.

The Challenge: Meeting complex, evolving disclosure requirements

Manually preparing reports drains time and resources as evolving requirements, shifting templates, and data updates demand constant attention.

AI as the Solution

Generative AI can handle much of this complexity, freeing up human expertise for higher-value work. Clarity AI’s automated reporting solution, for example, cut average reporting time by more than 80%, enabling compliance teams to meet expectations faster, with more confidence, and without pulling focus from their core responsibilities.⁵

Conclusion

The examples in this infographic show how AI is already making a difference in investment workflows. From improving sustainability research coverage to accelerating regulatory compliance, these tools are helping investors work faster and with more confidence.

But as adoption grows, human expertise remains essential. The true value of AI emerges when paired with human expertise. AI tools should not be treated as replacements for investment professionals, but as a tool to amplify their impact.As Neil Brown, Head of Equities at GIB Asset Management, pointed out in an interview on Sustainability Wired, investors still need domain expertise to interpret results, catch errors, and avoid being misled by flawed outputs. It’s this balance of speed and discernment that will define the next chapter of sustainable investing.