Investment in Technology by Fund Managers Could Potentially Fall Short on Enhancing Productivity

In the fast-paced world of asset management, the value of Artificial Intelligence (AI) is becoming increasingly apparent. A role-based approach to automation, embedding virtual agents and traditional automation seamlessly with human roles, is crucial in realizing this value. This approach focuses on change management and adoption, ensuring a smooth transition into the digital age.

Portfolio managers are already leveraging AI tools to refine strategies, narrow investment options, and optimize portfolio construction. Analysts, too, are using AI-powered research assistants to synthesize data from earnings calls, financial reports, and conferences, accelerating the insight generation process.

However, a statistical measure known as the R2 value, at 1.3%, suggests a virtually non-existent relationship between spend and productivity in asset management. This finding is supported by McKinsey's report, which states that technology costs have become increasingly sticky, and revenues have become unpredictable in the industry.

The report, based on firms representing 70% of global assets under management, highlights the major global players responsible for the majority of managed wealth. The study involved interviews with senior executives from leading asset managers in the U.S and Europe and found no clear correlation between higher technology spend and improved productivity.

Despite this, McKinsey suggests that AI could help asset managers recover margin levels. A mid-sized asset manager with $500bn in assets under management could capture 25% to 40% of total cost base in efficiencies through AI.

Effectively embedding AI into the organization is necessary to address margin pressures and unlock value, but doing so requires a step-change in approach to these technologies. On average, asset managers allocate 60% to 80% of their technology budget to "run-the-business" initiatives, leaving only 20% to 40% for change-the-business operations, with 10% to 30% of this portion directed toward firmwide digital transformation.

Enhanced risk models and automated reporting are supporting a more data-driven investment approach, while in investment management, generative AI is transforming the way insights are generated and decisions are made, with an 8% efficiency impact.

The full report, providing a comprehensive analysis of the role of AI in the asset management industry, can be accessed here. It's clear that for the asset management industry, embracing AI-driven transformation is essential to address mounting margin pressures and unlock significant value.

Investment in Technology by Fund Managers Could Potentially Fall Short on Enhancing Productivity