The landscape of active asset management is undergoing a significant transformation as firms face mounting pressures on profit margins. After decades of success driven by substantial fees and increasing assets under management, the industry now confronts formidable challenges. The rise of passive investing has notably impacted revenues, while costs associated with generating alpha continue to soar due to the need for extensive resources and complex infrastructure.
As firms attempt to adapt, traditional cost-cutting measures often prove inadequate. This is influenced by various external factors, including regulatory requirements, cybersecurity threats, and the need for technological advancements. Consequently, asset managers find themselves in a precarious situation: on one side, they grapple with declining fees and sluggish capital inflows; on the other, they must manage escalating operational costs.
The role of technology in active management
Initially, technology was viewed as the solution to these challenges. However, the reality has often been disappointing. Many firms have invested heavily in artificial intelligence and automation but remain hindered by outdated systems that consume valuable resources and add complexity to operations. A staggering portion of technological budgets—between 60% to 80%—is allocated merely to sustaining existing systems, leaving limited resources for genuine innovation.
Overcoming resistance to change
Even when modern tools are implemented, they frequently encounter resistance from staff. Portfolio managers and analysts may be reluctant to adopt new technologies due to concerns about their roles and potential loss of control. Therefore, it is crucial for leaders in asset management to foster a culture where AI enhances human expertise rather than replaces it. The goal is to empower teams to focus on higher-value decision-making instead of tedious data collection.
Reimagining the investment process
To break free from the constraints of legacy systems and reduce the cost of generating alpha, firms must rethink their entire investment process. This entails creating a new model—a conceptual alpha factory—that integrates human insight with technological capabilities. Drawing from over two decades of experience managing large institutional portfolios, a comprehensive blueprint has been developed to significantly lower the cost of alpha by addressing fundamental issues.
A real-world example
For instance, during a live run in October 2025, this model successfully identified an unusual valuation discrepancy in IHI Corporation, a Japanese firm, which conventional screening methods had overlooked. The prompt allowed for a swift evaluation of the company’s fundamentals, enabling the portfolio manager to make a well-informed decision. This scenario exemplifies the practical application of the Human+AI investment framework, highlighting its capacity to enhance the alpha generation process.
The architecture of this new alpha factory is structured around four essential pillars, ensuring transparency and accountability in the collaboration between human judgment and machine intelligence. Importantly, this model places human decision-makers in control throughout the investment process, rather than relegating them to a mere oversight role.
Future-proofing active asset management
Investors remain eager to outperform the market, but their willingness to pay high fees for subpar results has waned. By effectively managing the costs associated with generating alpha, active managers can once again present a compelling value proposition against passive alternatives. The imperative for investment leaders, particularly Chief Investment Officers (CIOs), is clear: the future will favor those who innovate their workflows rather than simply acquire new tools.
Starting with pilot processes rather than products enables teams to efficiently scale alpha generation while maintaining profitability. Notably, these cost reductions do not compromise performance. By relieving human experts from time-consuming data tasks, they can concentrate on driving alpha through strategic insights. Early outcomes from pilot models indicate that achieving competitive performance alongside an optimized cost structure is feasible without necessitating increased staffing or technology expenditures.
To maintain this competitive edge, a dynamic approach is essential. With new AI models emerging regularly, it is vital for CIOs to adopt a rigorous routine of continuous evaluation, testing, and integration of the most effective tools. Firms that successfully blend human expertise with AI at scale will emerge as leaders in the evolving realm of active management, ultimately cracking the code of alpha generation and solidifying their market position.