The AI Governance Frontier in Investment Management

The AI Governance Frontier in Investment Management
April 22, 2026 11 mins

The AI Governance Frontier in Investment Management

3 Questions on the AI Governance Frontier in Investment Management

Based on responses from 125+ investment managers, it’s clear that AI use in today’s market is now becoming mainstream and an AI governance maturity curve is emerging.

Key Takeaways
  1. AI uses and governance differ across the investment chain. Investment managers using AI more extensively tend to have stronger and more formal governance frameworks in place. Meanwhile, asset owners are trying to figure out what their oversight responsibilities might look like.
  2. As adoption accelerates, strong governance must keep pace. In their oversight role, asset owners, supported by their investment advisors, can start to take three key action steps.1
  3. For asset owners, now is the right time to conduct due diligence regarding manager use and governance of AI, begin asking the right operational questions, and standardize AI-related factors as part of the manager selection and monitoring process.

Based on responses from over 125 investment managers2 across asset classes and jurisdictions, it is clear that AI use is now mainstream. With the risks and opportunities from AI use becoming more apparent, and the regulatory environment fragmented across jurisdictions, good AI governance has advanced from an operational checkbox to something of strategic importance.

A clear maturity curve is emerging. Investment managers that use AI more extensively tend to have stronger and more formal governance frameworks. However, while some governance models have performed well in other spheres, such as pensions and corporate boards, it is unclear if traditional governance practices can deliver the necessary guardrails when it comes to a novel technology like AI. This article outlines how AI use in investment management is evolving and sets out three practical recommendations to help asset owners maintain effective oversight of managers’ AI use and its governance.

How are investment managers using AI?

The significant majority of managers2 responded that AI is used to enhance their investment processes, either to increase returns or drive efficiencies. While the types of AI tools differ widely, the philosophy for AI adoption is broadly consistent: they favor “augmentation” over “automation.” That’s to say, many support the idea that AI should be used not simply to replace their employees, but to empower their subject matter experts with additional, and more accessible, data points and investment research. For many, the goal of using AI isn’t necessarily more automation, it’s to enhance the quality of outputs. This is most notable as part of the investment management research process, where some managers commented that AI has a high “signal to noise ratio,” where you can get a lot of useful information relative to a small amount of noise or “junk.”

Managers favor tools that allow them to streamline workflows associated with data analysis while ensuring maximum control and transparency across the end-to-end process. For example, Large Language Models (LLMs), such as customized and ringfenced versions of ChatGPT, approved for internal use, might be used by investment analysts to aggregate and analyze large amounts of unstructured information, such as earnings transcripts, company filings, or news flow – tasks that are conceptually straightforward but tremendously time-consuming, and where outputs can be readily checked.

Some managers are also using AI to research structured data, such as market prices, economic indicators, or trade information, to identify patterns that might otherwise go unnoticed. Applications here range from spotting investment opportunities, timing trades, or optimizing implementation. Strikingly, many managers in this category already have a history of incorporating data science and machine learning into their investment processes, and few are planning to hand over the reins to machines completely.

Why is it important investment managers govern their use of AI, and are there any best practices?

AI risk awareness is high among investment managers. They are understandably as concerned about AI-related security and privacy risks as they are about AI quality issues. Blind AI use is perilous: LLMs, for example, are still prone to “hallucinations,” confidently presenting inaccurate, fabricated, or biased information. In the context of investment management, governance and oversight of AI use becomes even more critical. Investment management is a trust-based business, which can be quickly eroded by rogue trades, or careless use of proprietary or client information.

In response, many firms are prioritizing protocols for AI data privacy and malicious use, guidelines for responsible data sourcing, formal staff training on AI use, and the integration of AI policy into existing cybersecurity frameworks. AI governance structures at investment management firms are typically being built from the top down: a C-suite–appointed AI steering committee, council or working group, comprised of representatives from IT, Legal, Compliance, and other relevant business units. This group is often responsible for overseeing all AI use cases, ensuring they align with the firm’s objectives and risk parameters. Many are influenced by third-party guidelines such as the National Institute of Standards and Technology (NIST) or Microsoft’s Responsible AI framework. Tools are evaluated and controlled within existing information security processes. Training is provided, sometimes mandatory, for those staff members directly involved with AI-enhanced processes.

A clear maturity curve is emerging. Investment managers that use AI more extensively tend to have stronger and more formal governance frameworks in place. Larger managers, in particular, are building out more robust AI governance practices than smaller managers and are seeking broader benefits from AI use. Across regions, managers broadly align on core principles of AI governance—fairness, transparency, accountability, privacy and security, and human oversight—although there are notable differences around the formality of human supervision and the assessment of environmental impact. We can begin to categorize investment managers across a 2x2 matrix.

90%

While it is too soon to say that there are any best AI governance practices, nine out of ten managers surveyed currently agree that accountability is best supported by a strict human in the loop policy.2

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While it is too soon to say that there are any best AI governance practices, nine out of ten managers surveyed agree that, at least for now, accountability is best supported by a strict human‑in‑the‑loop policy: AI‑generated recommendations should always be reviewed before decisions are made. Many stipulate in their AI policy that they don’t allow unmonitored decision-making or actions via AI within any business unit.

What are the next AI-related action steps for investors?

For investors, ensuring AI use is well-governed is not merely a technological or operational imperative; it is an important step in enhancing resilience and positioning for success.

It’s important to note that AI uses and governance differ across the investment chain. For example, while investment managers tend to be direct users of AI in multiple areas of their workflow, their clients—asset owners, such as retirement funds, endowments and foundations are trying to figure out what their oversight responsibilities might look like as indirect users. There may also be some direct use of AI by asset owners in areas such as pension administration and member communication.

To oversee how AI is used, asset owners, supported by their investment advisors, may take the following 3 key steps:

1. Conduct due diligence regarding manager use and governance of AI.

2. Ask managers AI-related operational questions, for example:

  • Do you use AI in your investment process and for which asset classes?
  • What areas do you seek to improve with AI use?
  • How significant do you view the risks and opportunities of AI to your firm?
  • How do you verify / address potential bias in AI outputs and when and how does human oversight occur?
  • Does your firm have governance practices such as an AI policy (either standalone or as part of cybersecurity), protocols for handling data privacy in AI applications, guidelines for the responsible sourcing of data used in AI models or formal training for staff on how to explain and interpret the use of AI?

3. Standardize AI-related factors as part of the manager selection and monitoring process.

Conclusion

AI use is fast becoming mainstream for asset managers. As adoption accelerates, strong governance must keep pace. The firms that win will be those that embrace AI’s efficiencies while setting clear guardrails around its use, ensuring transparency and accountability at every step. For asset owners, now is the time to formalize expectations, ask the right questions, and strengthen oversight. Good AI governance is pivotal both for risk management and as a strategic advantage in an increasingly crowded digital investment landscape.

1 This report presents the results of Aon Investments’ Asset Manager AI Survey, conducted between October 2025 and December 2025. The survey included 126 asset managers across asset classes and jurisdictions. The goal of the survey was to understand if and how investment managers are using AI and the extent to which this impacts decision making and responsible practices.
2 Based on results of Aon Investments’ Asset Manager AI Survey, conducted between October 2025 and December 2025. The survey included 126 asset managers across asset classes and jurisdictions. The goal of the survey was to understand if and how investment managers are using AI and the extent to which this impacts decision making and responsible practices.
3 The Impact of Artificial Intelligence on DC Plans: 3 Questions with Beth Hanig

Aon’s Thought Leaders
  • Daniel Ingram
    Head of Responsible Investing, Aon Investments

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