From Models to Markets:
AI's Growing Adoption in Investment Management
Is AI in Investment Management Finally Earning Its Place?
London, 23 March 2026 - Last week, at the Royal Automobile Club in London, we gathered professionals who face this question daily, leading to a practical and insightful conversation.
For the second year, Axyon AI hosted its annual roundtable, From Models to Markets: AI's Growing Adoption in Investment Management, bringing together senior practitioners from across the industry to move beyond the hype and discuss what is actually working, what is not, and where the journey is headed next.
Moderated by Anders Kirkeby, Head of Innovation at SimCorp, the event featured an exceptional panel of voices: Sam Zief from JP Morgan Private Bank , Sorin Ionescu from Deutsche Bank , Richard Taylor from BlueCrest Capital Management , Enrico Piccolini from Generali Asset Management, and our own CTO and co-founder, Jacopo Credi.
The Question Everyone Is Really Asking
At Axyon AI, we believe AI’s primary role is to augment human intelligence, not replace it. Whether through Predictive AI - our core expertise - or the new possibilities of Agentic AI, our focus is helping investment professionals gain better insights, make smarter decisions, and deliver improved client outcomes.
This belief shaped the morning’s tone. The goal was not to celebrate technology for its own sake but to reflect on its wise use - to spark ideas, challenge assumptions, and explore how AI can truly enhance alpha generation.
So, has the industry arrived? Not quite. But it is getting there.
From Experimentation to Habit
AI is no longer a novelty; it is starting to deliver tangible results. Yet its widespread adoption in investment decision-making is still in the early stages. The key question for the morning was clear: how do we turn AI from an experiment into a habit?
- A key distinction emerged between AI as a tool and AI as a transformative force—one that, like social media before it, may not just extend our capabilities but reshape the environment around it entirely.
- The direction of travel is clear: away from rules-based models and towards multi-model approaches combining short-term equity signals, macro regime classifiers, and asset-class-specific models.
- The human layer remains indispensable: translating single-stock AI signals into fund-level or ETF implementation still requires experienced judgement that no model fully replicates.
- One example is an agentic AI system that processes investment ideas submitted by email and returns fully quantitative, auditable KPIs within minutes, with humans involved throughout.
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Two Very Different Conversations
Not all AI adoption is equal. The panel drew a sharp and practical line between two categories of application that demand very different approaches.
- Workflow automation: client communications, document processing, meeting summaries — is already deployed at scale
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Investment decision-making demands a higher bar: longer track records, stricter governance, and greater fiduciary caution
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The industry is in the "saucer" of the ROI curve: investments have been made, but the full impact on productivity is still ahead
The Build vs. Buy Question Has No Easy Answer
- Building in-house offers control but carries significant opportunity cost and time risk
- Buying from specialist vendors delivers speed, established infrastructure, and proven explainability
- For operational efficiency, scale beats perfection: a capable model deployed to thousands outperforms a bespoke one used by a small team
- When choosing a technology partner, what matters most is domain expertise, explainability, and a proven track record in production — exactly what Axyon AI brings to investment teams through its specialist, client-tested AI models, consistent track-record and end-to-end transparency frameworks
Explainability: The Black Box Is Opening
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The "black box" perception of AI remains one of the greatest barriers to client adoption. Progress is being made, but the tension between model complexity and transparency is real.
- End-to-end explainability frameworks now connect strategy performance, model behaviour, and raw data into a single auditable chain
- A conceptual challenge remains: if a pattern can be explained simply, is it truly novel alpha? The tension between complexity and explainability is unresolved. Rigorous error analysis and problem decomposition help, but no easy answers exist.
Data Quality Is the Real Constraint
- Wide, deep, representative, and point-in-time data is the primary ingredient of any successful AI application in finance
- More data is not always better — curated, high-quality subsets increasingly outperform sheer volume
- Generalist AI systems without domain-specific data consistently underperform specialist models for core investment use cases
Bias Is Everywhere — and Must Be Actively Managed
- AI does not introduce bias into an objective process; it codifies and scales biases already present in the data
- For instance: models trained on recent, US-centric data may carry subtle geographic preferences they were never designed to have
- Rigorous, ongoing bias detection is not a nice-to-have — it is a non-negotiable component of any production AI system
Accountability Cannot Be Automated
- Regardless of how capable AI systems become, human responsibility for their outputs must be clearly assigned
- AI should operate within defined decision spaces — with controlled data access, audit trails, and governance equivalent to those applied to human employees
- The human-AI partnership is not a transitional phase. It is the destination.
The Future of Roles: Evolving, Not Disappearing
- AI is displacing certain tasks, but the complexity of what remains demands skilled, experienced professionals
- Bridging commercial needs and data science capabilities is becoming one of the most critical functions within large organisations
- The focus is not on whether jobs will disappear, but on how they will transform.
Final Thoughts
The firms that will lead the AI adoption are not necessarily the largest or the best-funded, they are the ones willing to ask the right questions, start with the right data, and move with the right partners.
If that conversation resonates with you, we would love to continue it.