AI Trends in Investment Management

London, 25 March 2025 - A recent roundtable organised by Axyon AI brought together voices from the financial sector to discuss whether AI is truly transforming investing or is the latest in a long line of overhyped technologies.

With representatives from traditional asset management, quantitative, and fintech investing, the 90-minute discussion navigated the complexities, promises, and challenges of AI in finance. In this article, we summarised the key topics raised by our panellists and guests during the roundtable discussion.

Roundtable - Featured Speakers

Key Topics of Discussion

📌 AI's  Most Immediate Impacts: Efficiency & Performance

AI’s primary contribution lies in driving operational efficiency. It empowers non-coders and fundamental managers alike to analyse broader datasets quickly, improving workflow and insight without consistently outperforming markets.

• AI can enhance speed and scope across financial services.
• Non-technical users can now access advanced technologies, levelling the playing field.
  
  
  

📌 Explainability and Trust: Non-Negotiables in Finance

AI models must be transparent and interpretable to gain traction in finance. Regulatory demands and client expectations mean black-box systems won’t suffice; explainability tools are helping, but there’s still ground to cover.

• Finance requires models that are auditable and justifiable.
• Explainability fosters trust, especially critical to explain potential underperformance. 
• Axyon Lens provides a wide range of reports, from high-level strategy performance to a detailed examination of model predictions including: Strategy performance, risk & performance attribution, feature group importance & performance contribution, individual input features importance & performance contribution, pattern & interaction analysis and raw data; amongst many.

📌 Predictive AI vs Generative AI: Choosing the Right Tool for Investment Decision-Making

Generative AI excels at handling unstructured data and creating content, but when it comes to forecasting market behaviour, Predictive AI—built on Auto-ML frameworks—offers the precision, repeatability, and robustness essential for decision-making in noisy financial environments.

• Generative AI is best suited for content creation, not alpha generation.
• Predictive AI identifies subtle patterns to forecast asset performance.
• Axyon AI offers a differentiated edge with finely tuned Predictive AI models that seamlessly integrate into investment processes across quantitative, fundamental, and wealth management domains.

📌 The Human-Machine Collaboration Is Key

Successful adoption of AI hinges on collaboration between finance professionals and technical experts. The most effective results come when investment intuition is combined with AI capabilities, bridging the gap between traditional and quantitative approaches.

• Domain knowledge and tech expertise must work hand-in-hand.
• “Quantamental” investing reflects the new hybrid model powered by AI.
• Isolated innovation efforts without integration fall short of value.

📌 AI vs. the Index
The panel explored whether an AI-enhanced index could outperform an equally AI-supported active manager. While passive investing sets a high benchmark through customisation and transparency, active strategies must now evolve with precision and adaptability to remain competitive and justify their cost.

• Active managers must leverage AI to deliver targeted, dynamic exposure; with an aim of out-performing the benchmark.
• Axyon AI combines Predictive AI with systematic rigour, offering proven, taylored strategies backed by live clients since 2018, which consistently outperform.

📌 Rethinking Scale: Why Focus and Agility Matter More Than Size in AI-Driven Investing

The panel challenged the assumption that scale guarantees success in AI-driven investing. Instead, strategic clarity, agility, and effective use of accessible technologies were seen as more decisive than having the largest teams or most powerful infrastructure.

• Effective deployment of AI depends more on focus than sheer size.
• Democratised access to tools reduces the advantage of scale.
• Success comes from adaptability and smart application—not just resources.
  
  

📌 Efficiency – From Silos to Synergy: How AI Is Merging Breadth and Depth in Investment Analysis
AI is collapsing the traditional divide between breadth and depth in investment research. Fundamental and quant managers alike are now leveraging AI to access richer insights across broader universes, redefining what it means to gain a sustainable information edge.

• Fundamental managers can now scale analysis across vast stock universes using AI.
• Quants are gaining depth through adaptive models capturing sentiment and behavioural data.
• Predictive AI is driving a convergence of strategies, enhancing agility and insight.

📌 Where do We Go from Here? A Call for Purposeful Innovation

Collaboration with AI-focused fintechs offers a practical path forward. Rather than building in-house capabilities from scratch, firms can leverage partnerships—but only if both sides deeply understand the investment problems they aim to solve.

• Partnerships enable scalable, specialised AI integration.
• Understanding the business problem is key to successful AI use.
• Axyon AI is a nimble, focused Predictive AI firm that delivers innovative, client-specific solutions with speed and precision through long-term partnerships, and robust financial market expertise.
  
  

📌 Final Thoughts

AI is not replacing investment managers—but it is reshaping the role. Those who thoughtfully embrace AI as a tool for insight, efficiency, and collaboration will outperform those who resist. The future belongs to the adaptive.

• AI changes how professionals think, work, and compete.
• Success depends on thoughtful, collaborative adoption.
• Investment managers using AI will outpace those who don’t.