Artificial Intelligence Will Clarify What Asset Managers Are Paid For

• AI is changing how asset management creates value, not eliminating the need for it.
• The edge is shifting from generating signals to integrating them into client portfolios.
• Investment judgment and accountability remain the foundations of fiduciary value.

Artificial intelligence will not make asset management obsolete. It will make clearer what asset managers are paid for.

Clients do not hire managers for data access or forecasts. They delegate capital because investment decisions must be made under uncertainty and within competing constraints. Information must develop into portfolios that fit mandates, risk limits, regulation, and client objectives. Those decisions must also remain explainable when outcomes disappoint.

AI lowers the cost of processing information and increases the value of integrated investment platforms. The bottleneck shifts from generating signals to turning them into robust portfolio decisions. AI does not replace investment judgment. It changes where judgment adds value: deciding what is investable, what fits the mandate, and who stands behind the result.

For investment leaders, AI is therefore less about access to models than about the design of the investment process.

William Sharpe's arithmetic remains a useful starting point: before costs, the average actively managed dollar equals the average passively managed dollar; after costs, it trails. Lasse Heje Pedersen sharpened this for real-world markets, where market portfolios change and even passive investors must trade.

Robert Merton's functional view broadens the discussion. Financial functions are often more durable than the institutions that perform them. Applied to asset management, three enduring functions emerge:

• Process information
• Build portfolios under constraints
• Stand behind investment decisions

AI affects each function differently.

The implication is significant. Asset managers will no longer perform all three functions internally by default. AI and platform economics are unbundling the value chain. Information processing can migrate to data and model providers. Portfolio implementation can move toward indexing and direct-indexing platforms. Documentation increasingly belongs to risk and regulatory technology. Many firms already operate this way, relying on external providers while retaining responsibility for mandate design and final portfolio decisions.

AI's greatest immediate impact is information processing. Corporate filings, earnings transcripts, news, and regulatory documents can all be analyzed faster and at lower cost.

Information advantage does not disappear. It moves.

As foundation models and commercial data become widely available, model access alone becomes less valuable. Sustainable advantages increasingly come from proprietary data, regime awareness, institutional knowledge, and the ability to connect information to a client's specific mandate.

Evidence from Cao et al. points in the same direction. AI performs particularly well when information is abundant and standardized, while human analysts retain an advantage where soft information, industry expertise, or corporate distress matter most.

Grossman and Stiglitz help explain why: if information is costly, prices cannot fully reflect it, so informed investors must be compensated for acquiring it. AI reduces the cost of processing information, but not the scarcity of correct interpretation.

Unlike banks or insurers, asset managers do not transform risk on their own balance sheets. Their role is to construct portfolios within explicit constraints: benchmarks, tracking-error limits, liquidity requirements, risk budgets, regulation, and client objectives.

AI can accelerate research and improve portfolio construction. Systematic strategies already automate many investment decisions; AI expands the range of information those systems can incorporate.

The bottleneck, however, is not technical capability but mandate-level judgment.

An earnings-call summary is not yet a signal.

An AI-generated signal is not yet a position.

A position is not yet a portfolio decision until its weights, risk contribution, liquidity profile, and benchmark context satisfy the mandate.

That integration is where competitive advantage increasingly resides. Research, portfolio construction, risk management, execution, and reporting must function as a coherent system rather than as isolated tools.

Berk and van Binsbergen argue that investment skill is better measured by dollar value added than by percentage alpha. In an AI-enabled investment process, the first benefit may therefore be greater scalability rather than higher returns.

The third function is also the hardest to automate: accountability.

AI can generate hypotheses, challenge assumptions, and stress-test investment cases. It cannot assume fiduciary responsibility or explain a disappointing outcome to a client.

After 25 years in asset management, I can say that those conversations define the profession. During one discussion following several years of underperformance, what mattered was not model output but explaining which assumptions had failed, when they failed, and why we chose not to abandon the investment process under pressure.

AI can prepare that conversation. It cannot replace it.

Current industry practice reflects this reality. A 2024 Bank of England and Financial Conduct Authority survey found that three-quarters of responding UK financial firms already use AI, yet only 2% of reported use cases involve fully autonomous decision-making.

AI does not eliminate accountability. It changes how accountability is organized.

• Who validates models and data quality?
• Who determines whether an AI-generated signal is investable?
• Who manages dependence on external models and vendors?
• Who explains the resulting decisions to clients?

These remain investment decisions, not merely compliance exercises.

Regulators and practitioners are moving in the same direction. IOSCO’s AI/ML guidance emphasizes senior accountability, testing and monitoring, skills, third-party controls, disclosure, and data quality. The CFA Research Foundation volume AI in Asset Management, edited by Joseph Simonian, frames the issue more broadly: AI should strengthen, not supplant, human judgment, trust, and fiduciary responsibility. Gennaioli, Shleifer, and Vishny model trust as central to investment delegation.

In an AI-driven investment process, trust is earned through decisions clients can challenge and revisit.

Three implications follow.

First, deploying chatbots is not an AI strategy. Competitive advantage depends on integrating research, portfolio construction, risk management, execution, and reporting into a coherent investment process.

Second, validation becomes a core investment function. AI-generated signals require rigorous out-of-sample testing, regime analysis, economic plausibility, and explicit human oversight before capital is committed.

Third, asset managers increasingly become coordinators of specialist platforms. Data providers, model developers, index platforms, risk systems, and RegTech firms each perform part of the investment process. Competitive advantage lies in integrating those components into portfolios tailored to specific client mandates.

Large platform firms may benefit from scale and integration. Boutique managers may compete through proprietary information, domain expertise, and close client relationships. Firms caught between those models will face increasing pressure, offering neither distinctive insight nor scalable implementation.

AI will not eliminate asset management. It will unbundle it.

The core functions of the industry remain intact, but their distribution across managers, platforms, and specialist providers is changing. The scarce capability is no longer generating another signal. It is integrating information, constraints, and technology into investment decisions that are robust, explainable, and aligned with client objectives.

For clients, the question is not whether a manager uses AI. It is whether AI leads to better decisions that fit their mandate and can be explained when outcomes disappoint.

For investment leaders, the challenge is one of investment architecture: designing, validating, and owning the chain from data to decision. Competitive advantage will increasingly depend not on access to AI itself, but on the ability to integrate it into an investment process that is disciplined, accountable, and worthy of clients' trust.

Disclaimer

This piece is for information and discussion purposes only and does not constitute investment advice, a recommendation, or a solicitation to buy or sell any financial instrument. It reflects the author's personal views. The author served as Managing Director of Apo Asset Management GmbH until May 2026. The views expressed here do not represent an official position of Apo Asset Management GmbH.