Artificial intelligence (AI)-related disruption has featured prominently in headlines in recent quarters, with industries like software, data services, and even asset management seen as vulnerable across global equity markets. In financial services, while AI is changing the tools investors use, it has not done away with the need for judgement in moments of uncertainty. In the following commentary, we explore why robust decision making, and structured manager selection and oversight, matter even more when narratives move faster than fundamentals.

Discipline in a noisy market 

Much is being written about artificial intelligence (AI) replacing human judgement in professional roles—including asset management. In a market saturated with AI narratives, the challenge is no longer gathering information but converting it into decisions: what matters, what can be ignored, and what to do next. Markets have reacted as if the disruption is imminent— penalising businesses seen as most exposed and lifting perceived beneficiaries to new highs. The result has been a wider spread in stock returns. 

The same divergence has been mirrored in outcomes at the manager level: In fast‑moving, narrative‑led markets, differences in positioning can translate into an unusually wide range of manager returns. As manager selectors, this is precisely when discipline matters most, so we resist being led by short‑term performance. When we select and oversee external investment managers, we use a structured framework across the same core dimensions—philosophy, process, people, product, portfolio, and performance—to build an investment thesis and monitor what would constitute a break. 

In this commentary, we bring these ideas together: how AI is changing the tools, why human judgement still shapes outcomes, and what that means for decision‑making, whether in stock selection or in selecting and backing the managers that we use in portfolios. 

Markets periodically enter phases when narratives move faster than fundamentals. We are in one of those phases today. AI has become a dominant lens through which investors interpret company prospects, contributing to sharp dispersion (the range of potential outcomes of investments based on historical volatility or returns) and abrupt rotations. Prices often move ahead of evidence, leaving investors to make decisions with incomplete information and uncertain paths. 

In environments such as this, it is easy to place too much weight on the loudest narrative or to assume that more sophisticated tools necessarily translate into better outcomes. A more durable response is to focus on what is being priced, what remains uncertain, and how much of the future is already embedded in valuations. That discipline matters at the stock level—and it matters just as much when deciding which managers to hire, retain, or replace.

Same philosophy, different people, different results

One reason we place such weight on “people” is that outcomes can diverge meaningfully even when philosophy and process look similar on paper. In periods dominated by a single theme, two managers can agree on the same broad opportunity set and operate with similar stated disciplines, yet deliver very different results because their people make different judgement calls—how to size positions, which risks to underwrite, how to express conviction, and when to adapt at the margin versus “hold the line.” 

The same dynamic is visible in polarised “AI winners vs. losers” markets. When attention narrows, small differences in interpretation and positioning can compound into large performance gaps. Importantly, dispersion is not always a clean signal of skill; it often reflects how each team expresses its philosophy through day-to-day choices under pressure. That is why our research seeks to separate decisions from outcomes and to understand the human behaviours that underpin the portfolio. Sharp underperformance is not, by itself, evidence of process failure. In a differentiated, concentrated strategy, it can be the price of being different—so the focus is whether the thesis and decision discipline remain intact. 

AI: acceleration, not abdication 

AI can improve the mechanics of investing. It can help teams read and summarise more broadly, search more intelligently, translate complex material, and monitor portfolios with greater speed. In other words, it can raise baseline efficiency by strengthening the process—making research coverage broader, monitoring more continuous, and documentation more repeatable. 

However, tools do not define objectives, set risk tolerance, or own trade-offs. People still decide what uncertainties matter, what evidence is sufficient, when an output is plausible, and when it is misleading. The practical implication is that use of AI will quickly become a weak differentiator. Most serious organisations will adopt similar platforms. The differentiator shifts upward to governance and judgement—how tools are integrated into a coherent process, how outputs are challenged, and who remains accountable. 

A practical precedent: Systematic investing 

Systematic investing has existed for decades. It is an advanced form of process engineering: Data is translated into signals, signals into portfolios, and portfolios into trades with defined controls. But it has never been “people-free.” From the outset, humans have designed the models, set the objectives, chosen the inputs, and governed how the system behaves when conditions change. Models embed assumptions, inputs can be noisy, and regimes can shift. A system can behave exactly as designed and still deliver poor outcomes if the environment moves or if it is applied outside its intended domain. Therefore, well-run systematic processes rely on people for oversight and accountability—setting objectives, defining risk limits, and deciding how the system should behave in stressed environments. More powerful tools do not remove the need for oversight; they increase the value of it.

Assessing “people” objectively

The “people” pillar is not about charisma. It is about whether the humans behind a strategy increase the probability of good outcomes, especially when markets are noisy and incentives can skew judgement. We assess this in a structured way. 

We start with experience and depth through cycles, looking for evidence of pattern recognition, awareness of pitfalls, and an ability to operate when conditions diverge from recent history. We then focus on accountability and decision ownership: Who is responsible for results, and is that responsibility clear enough to learn from mistakes and intervene when needed? 

We also examine whether team structure, resourcing, and tenure are fit for purpose—whether the organisation is sized and organised to execute the stated process, and whether instability threatens continuity. We look for a culture of constructive challenge and learning, including whether the team reviews decisions honestly rather than rationalising errors. Incentives and alignment matter as well. Compensation, ownership, and growth priorities can influence behaviour at the margin, especially when performance is under pressure.

Finally, we focus on implementation: where subjectivity is allowed in, and whether it is deliberate and consistent rather than ad hoc. When a decision sits outside the usual process, is the rationale explicit, ownership clear, and a defined moment of challenge built in before implementation? And do exceptions trigger reflection and tightening of the process, or do they quietly become the new normal? In short, we are trying to understand not just what the process says on paper, but how it is applied under pressure, and what prevents drift.

Adaptation without reaction

The interaction between people and process becomes most visible when the environment changes. When a dominant theme reshapes leadership, teams face an uncomfortable balance: staying open‑minded to genuine change while resisting the temptation to react to every headline. Some teams adapt thoughtfully at the margin—broadening inputs, deepening scenario work, or stress‑testing assumptions about competitive advantage. Others prioritise consistency, accepting that tracking error (which reflects how closely the returns of a fund or portfolio follows its benchmark) can rise when the market rewards exposures outside their style. Neither instinct is automatically correct. What matters is whether changes are deliberate and coherent, rather than performance‑driven drift. Process is only as good as the team’s ability to adhere to it, evolve it prudently, and recognise when incentives or narratives are pulling it off course. 

This is also why ongoing oversight matters. The risk is not that a process evolves, but that it drifts quietly, and usually under pressure. Our monitoring is designed to test whether decisions remain consistent with the stated philosophy and process, and to flag early signs that the underlying thesis may be weakening—not by watching performance in isolation, but by focusing on practical leading indicators (for example, signs of style drift or portfolio behaviour that no longer matches how the strategy is meant to work). Where appropriate, we set explicit re‑evaluation triggers so reviews and decisions can be proactive, rather than waiting for poor performance to force the conversation.

What this means in practice

The rise of AI does not change the fundamental objective: building portfolios from managers we believe have durable advantages and robust behaviours. What it changes is the texture of uncertainty and the speed of information.

The practical task is not to produce a perfect forecast of “the AI winners.” It is to apply a repeatable way to select and oversee decisionmakers who can navigate uncertainty when the market’s attention narrows and evidence is incomplete. 

At SEI, this premise is embedded in the way we conduct manager research. Decades of organisational experience sit behind a repeatable process that produces clear, decision‑relevant views. The work blends qualitative due diligence—organisation, team, philosophy, and edge—with quantitative analytics to test whether portfolio behaviour matches expectations. Monitoring is thesis‑driven and designed to surface early warning signs, allowing managers to be reassessed, resized, or replaced when the thesis breaks—not after the damage is visible in returns. 

That is why SEI’s framework starts with structure, treats people as central to how philosophy and process translate into portfolios, and maintains an oversight discipline grounded in thesis and leading indicators. 

Tools change, responsibility does not 

AI will improve efficiency, expand research coverage, and enhance aspects of implementation and monitoring. Over time it will become widely available. What will not fade is the need for judgement amid uncertainty. 

In investing, better tools do not automatically produce better outcomes. Outcomes are shaped by the calibre of decision makers, the discipline of the process, and the quality of oversight that keeps tools serving judgement, rather than quietly substituting for it. That is why people still matter, and why we believe our structured manager research and oversight helps build portfolios designed to be robust across a wide range of market environments.

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