Recent regulatory developments in the UK and EU highlight that policymakers increasingly recognise how divergent rules can affect the competitiveness of their domestic asset management industries and, by extension, asset owners. The rapid emergence of artificial intelligence (AI) in asset manager investment processes will vastly increase the impact of these disparities for all market participants.
MiFID II reshaped the economics of investment research in Europe. The net result was that many EU and UK managers opted to fund research via their profit and loss statements (P&Ls). As research was often the manager’s largest cost after compensation it resulted in an approximately 75% decline in external research spending.
This clearly reduced research access for EU and UK managers. Most US managers continued to rely on client-funded research models with relatively limited change, creating a substantial transatlantic divergence in research budgets and, consequently, a material information asymmetry.
In response, regulators have made it easier for managers to use client money for research to level the competitive playing field with US managers and to reduce the market risks in P&L research budgets.
In 2022, equities declined by around 20% and bonds by 15%; for a typical UK manager this translated into a fall in assets under management of about 20%.
Given the operating leverage in the asset management economic model, this resulted in an approximately 70% decline in pre-tax profits. It is this profit pool that must absorb research and ESG-related costs, exposing critical research budgets to unintended short-term market risks.
AI in the investment process
The arrival of AI into the investment process has the potential to vastly amplify existing research-related information disparities. Its impact on investment decision-making and operating models is likely to be so profound that the information gaps created in the MiFID II era may appear quaint by comparison.
The new technology presents both significant risks and substantial opportunities for asset managers and pension funds.
Early AI use cases for managers, including client chat, marketing support and document summarisation, are rapidly giving way to integration into the investment process as AI auditability and governance have improved.
Asset manager executive committees have rightly been cautious about exposing core investment processes to AI. But the medium-term question is no longer whether it will be used in investment organisations; it is whether it will be used well, under governance that supports both performance and control. The potential productivity gains are unprecedented – and the likely competitive implications are existential.
To capture AI’s benefits while mitigating its risks, a set of practical requirements and guardrails has begun to emerge, including:
- Walled gardens: AI models are run over curated, vetted content sets, typically combining external research with internal reports. Within a walled garden architecture, managers can also develop internal models without inadvertently training external AI systems on proprietary content.
- Source attribution: AI systems should be able to identify immediately the specific documents and data underpinning their analysis, enabling transparency, auditability and easier validation by investment teams.
- Workflow native design: specialist small language models designed for investment workflows can materially improve the quality, relevance and reliability of outputs.
- This architecture also forces an important discipline: clear data and content rights over what is ingested and how it is used.
- AI usage and governance are set to become central elements of the competitive landscape for asset managers. Investment consultants, fund selectors and pension funds will need robust frameworks to evaluate managers’ AI strategies, including architecture, governance, data policies and integration with investment processes.
AI-driven value creation
Beyond manager selection, pension funds with internal investment teams will need to determine how best to deploy the new tech within their own internal investment processes.
AI may also significantly reshape the traditional cost-versus-value debate for pension funds, particularly in the UK context.
The scale of potential AI-driven value creation suggests that modest increases in manager costs – whether to support research or AI development – could generate outsized improvements in investment performance and productivity.
“Asset manager executive committees have rightly been cautious about exposing core investment processes to AI. But the medium-term question is no longer whether it will be used in investment organisations; it is whether it will be used well, under governance that supports both performance and control”
In the UK, these developments sit squarely within the ‘Value for Money’ framework of the Financial Conduct Authority and The Pensions Regulator. But are UK pension funds maximising the value they receive from external managers by denying them a couple of basis points in research or AI costs?
If AI materially increases manager productivity and decision quality, then modest differences in fee levels may be trivial compared with the performance dispersion between funds that are frequently measured in thousands of basis points across multiple active categories.
The key question for trustees and CIOs is: are we optimising for the lowest visible cost line, or for the highest probability of sustained net outperformance delivered through a well-governed AI process advantage?
Historically, asset owners have been willing to pay high management fees for exposure to managers with demonstrable investment process advantages and/or superior information. As AI adoption in asset management accelerates, it is likely to widen competitive gaps between managers that deploy it effectively and those that do not.
For both asset managers and pension funds, thoughtful, well-governed engagement with AI is no longer optional.
Neil Scarth is chairman of KiteEdge
