Fundpath recently hosted its latest Leaders in Fund Selection breakfast at The Ivy for a private discussion on the future of active management.
The session, on the 14th of April, was led by Euan Munro – one of the industry’s most experienced voices, with a career spanning the creation of GARS (Global Absolute Return Strategies) at Standard Life Investments through to leading Aviva Investors and Newton Investment Management.
The focus: what AI actually changes in practice for investment strategy, portfolio construction, and the role of the fund manager.
While specific contributions remain confidential, several themes stood out.
A Structural Shift, Not Another Tool
Asset management has always evolved alongside technology. From early technical analysis to platforms like Bloomberg, each wave has improved access to data and efficiency of execution. AI feels different. Rather than enhancing human decision-making, it introduces the possibility of systems that can independently process vast datasets, identify patterns, and generate outputs at a scale no human team can match. The shift is subtle but profound: from tools that support decisions to systems that shape them.
Reframing the Role of the Investor
A useful distinction emerged between two core elements of investing:
- Interpreting the past, where machines already excel
- Imagining the future, where humans still hold the advantage
AI’s strength lies in pattern recognition, data mining, and probabilistic analysis. But markets are not purely statistical constructs – they are shaped by discontinuities, narratives, and events that have no precedent. This reframes the role of the investor. Less time spent analysing data, more emphasis on judgement, interpretation, and the ability to navigate uncertainty.
From Analyst Teams to AI Systems
One of the more practical implications is how investment teams may evolve. The traditional model where large analyst teams feed into a portfolio manager begins to look inefficient when AI can replicate much of the analytical layer. In its place, a different structure emerges: AI systems handling idea generation, portfolio modelling, and monitoring, with human oversight focused on decision-making and accountability. The result is not necessarily fewer people, but different roles with greater weight placed on experience and judgement.
Explaining AI-Driven Strategies
As new AI-enabled strategies emerge, a parallel challenge becomes clear: how they are understood. For advisers and intermediaries, the challenge is not just technical comprehension, but interpretation – distinguishing between genuine innovation and superficial application. For end clients, the challenge is even more fundamental: trust. The ability to clearly articulate how these strategies work, and where their limitations lie, will become a defining capability for asset managers.
Compliance as a Leading Edge
While much of the focus sits on portfolio construction, some of the most immediate impact may be operational. AI enables full-scale monitoring of trading activity, moving beyond traditional sampling approaches. This has the potential to remove longstanding frictions in compliance, distribution, and client servicing but also raises expectations. Firms that fail to adopt these capabilities may find themselves structurally disadvantaged.
Pressure on Economics and Positioning
If AI lowers the cost of generating insight, it inevitably challenges the economics of active management.
Fee compression feels less like a possibility and more like a direction of travel. At the same time, new categories are likely to emerge:
- AI-enhanced strategies delivering more efficient beta
- Alternative return streams with low correlation to traditional markets
- Bespoke portfolio construction at scale
The competitive landscape may shift accordingly, with increasing pressure on firms that lack the resources to invest in AI infrastructure.
Implementation is Key
Not all AI is created equal. Generic models trained on open data sources may struggle with the nuance required for investment decision-making. By contrast, specialised systems built on proprietary datasets within controlled environments are likely to prove more effective. This introduces a new axis of competition: not just investment philosophy, but data ownership, system design, and the ability to integrate AI meaningfully into the investment process.
The discussion reinforced a simple but important point: AI does not remove the need for active management – it changes where value lies within it.
In a world of abundant data and increasingly powerful systems, the differentiator may no longer be access to information, but the ability to stand over decisions with clarity, conviction, and judgement.