AI Adoption: Three Key Compliance Strategies for Investment Managers

Establish Governance, Resist the Urge to AI Wash, Put Substance Over Hype

By Suma Chander, Partner

Artificial Intelligence (AI) is currently driving the dialogue across many aspects of our lives. While the uses of AI are still being debated and formed, there is widespread consensus that AI’s impact across the investment management industry will be profound. We agree.

The promise of value in efficiency is irresistible, coupled with a significant fear of non-adoption and missed productivity gains. However, recent research and trends in technology indicate that business leaders are placing pressure on technology teams to invest and adopt AI tools, but without providing clear goals and frameworks to guide them. In our opinion, that’s a critical mistake that could snuff out the bright promise of AI in any industry.

AI: A Marathon … Not a Sprint

Implementing AI should be an iterative and exhaustive process — more a marathon than a sprint. While we’re not suggesting to slow the pace of innovation, we do have some recommendations on how to train for this AI marathon to help you remain compliant and go the distance.

Based on our own experience and knowledge of the rapid evolution in technology and conversations with chief technology officers, academics, regulators and our clients, below are three key strategies to consider when adopting AI in the investment management space:

  1. Establish an AI Governance Framework
  2. Resist the Urge to AI Wash
  3. Always Put Substance Over Hype

AI Governance Framework

Think of governance as the starting block from which your AI implementation will accelerate. As you move forward with establishing a governance framework, ask yourself the following questions:

  • What impacts of AI do we now see or imagine in our space today?
  • What are the ultimate goals of use that we should define to help guide implementation?
  • Who should “own” AI oversight? Should oversight be use- or goals-based, or reside under central control?
  • What processes and tools should be considered and used in developing the framework?
  • How do we stay current with AI to allow our uses and capabilities to expand and evolve?

Resist the Urge to AI Wash

While failure due to risk aversion has been a pattern in times of innovation and change, recent examples concerning AI seem to show a reverse tendency, with AI going at a faster speed and magnitude, pushing companies toward risk attraction.

In the past, identifying customer problems and developing hypotheses to determine solutions was a standard practice before AI. Many investment companies are now marketing “AI” in their strategies — touting AI technologies as a selling point for their products, strategies and services to attract investors. Resist the urge to do this. Stay closer to a risk aversion philosophy for your investors, which has likely served both you and them well in the past. Resist the urge to attract investors with misleading claims of AI (AI Washing).

Put Substance Over Hype

Attracting investors by AI Washing is catching the attention of industry regulators, including the U.S. Securities and Exchange Commission (SEC) which is now examining if such marketing claims hold true. The SEC is scrutinizing funds and companies claiming the use of AI. Misleading advertising about the use of AI technologies in investment strategies has led to significant demand for transparency on the use cases and implementation of AI technologies.

The SEC’s actions reflect concern that the term AI could be loosely used or exaggerated which can lead to misleading investors. Regulators want transparency to ensure that the pitch of AI capabilities is accurate and remains in compliance with marketing regulations. The investment industry should prepare for better disclosures around AI. Some considerations:

  • Technology Transparency: Disclose and explain how AI is being used in your products, services and investment strategies, including tasks, type of technology [e.g., natural language processing (NLP), large language model (LLM), machine learning (ML)].
  • Substantiation: Be prepared to provide evidence on how you are implementing AI with case studies of its impact, development, implementation, team details and data. Explain clearly how you are using existing AI tools and how you are leveraging the technology for investment decisions.
  • Result Focus: Emphasize results, explaining how investments are benefiting from the use of AI technology to validate that it’s not AI Washing but actually improving efficiency and returns. Make sure to disclose potential risks to data privacy or strategy.
  • Compliance: Ensure that marketing and regulatory requirements around data privacy, security, bias and market movements are being met. Confirm that all the necessary documentation of the investments has complete disclosure around AI and that it’s correctly represented.

Getting It Right

As AI continues to evolve — with all of us responsible for its governed progress — maintain a thoughtful, transparent, explainable adoption and prioritize substance over hype to build the trust of investors.

Contact Us

PKF O’Connor Davies is proactively helping our clients and the industry at large understand the broad concerns AI presents. We offer AI guidance on strategy, frameworks and implementation with respect to SEC regulations, such as the marketing rule, and provide insight regarding what is around the corner tomorrow and further into the future. We would be pleased to speak anytime to share our thoughts. Contact your client service team or:

Suma Chander
Partner | 212.286.2600