Artificial Intelligence is no longer a buzzword in financial services. It has become one of the most powerful drivers of innovation in the investment management industry. From quantitative research to portfolio construction and advisor productivity, AI is reshaping how investment firms operate and compete. Yet, despite its potential, the industry is still in the early stages of a complex transformation journey.
Artificial Intelligence is no longer a buzzword in financial services. It has become one of the most powerful drivers of innovation in the investment management industry. From quantitative research to portfolio construction and advisor productivity, AI is reshaping how investment firms operate and compete. Yet, despite its potential, the industry is still in the early stages of a complex transformation journey. This article summarizes key findings from the first chapter of Artificial Intelligence: The Value is in Scale, a research paper developed in collaboration between MDOTM, a pioneer in AI-driven investment technology, and EY, a global leader in consulting and financial services transformation.
AI as a Strategic Enabler
The use of AI in investment management is often misunderstood as a cost-efficiency or automation lever. In reality, it is much more than that. AI has introduced a new paradigm in which investment decisions can be based not only on human intuition and traditional analytics, but also on the ability to process massive volumes of structured and unstructured data in real time. This blend of human and machine intelligence enables firms to identify investment insights earlier, react faster to changing market regimes, and scale portfolio customization in ways that were not previously possible.
The firms that have started leveraging AI are discovering that its impact spans the entire value chain. It enhances operational efficiency by reducing manual work and operational risk. It strengthens the investment process through predictive analytics, portfolio optimization and research automation. It elevates client experience and business development by enabling personalized advisory services, automated reporting, and intelligent client interaction.
Why Adoption Is Still Limited
Despite its transformative potential, AI is far from being fully integrated into the operating model of investment firms. According to industry surveys, only a small percentage of asset and wealth managers consider themselves advanced users of AI. Most firms are still experimenting, often through small-scale prototypes or pilots isolated within specific business functions.
The reasons behind this limited adoption are both strategic and structural. Many firms still lack a clear AI strategy and treat AI as a set of disconnected experiments, rather than a long-term driver of growth and differentiation. Legacy technology and fragmented data architectures pose additional challenges, limiting the ability to feed AI models with high-quality, consistent data. At the same time, firms often underestimate the importance of culture and skills: without equipping teams with AI literacy and confidence, even the best technology fails to scale.
Another factor slowing down adoption is regulatory uncertainty. The industry is still navigating evolving guidelines around AI explainability, model risk, data privacy and accountability. Many firms prefer to wait rather than move forward, even though AI is already transforming the competitive landscape.
From Potential to Reality
The path forward lies in rethinking the role of AI within the investment organization. The research from MDOTM and EY highlights a key insight: AI creates value only when it moves beyond experimentation and becomes fully embedded in decision-making workflows. This requires three fundamental steps.
First, firms must align AI initiatives with strategic objectives, linking each use case to tangible business outcomes such as alpha generation, scale, efficiency, or client engagement. Second, they must build a scalable technology and data foundation that supports AI at industrial scale rather than as a series of siloed projects. Third, they must invest in people, ensuring that portfolio managers, analysts, advisors and risk teams develop the skills needed to interact with AI confidently and responsibly.
This is not a short-term sprint. It is a progressive journey that requires investment, governance and a clear framework for value creation. But the direction is clear: AI is becoming a structural capability in investment management, and firms that embrace it early will shape the next generation of financial services.
In the next article, we will explore one of the major barriers highlighted by the research: the AI Pilot Project Paradox—why most firms get stuck in proof-of-concept mode and how to move from pilots to real business impact.
