AI won’t replace OCIO, it will separate leaders from the rest

Key takeaways

• AI has moved beyond experimentation, with broad survey evidence showing results meeting or exceeding management expectations¹.
• The competitive edge is no longer access to AI, but how effectively it is embedded across research, portfolio construction, risk management, and client service.
• Institutional investors can benefit from broader insight, faster decision-making, stronger risk monitoring, and more tailored portfolios at scale.
• The OCIO providers that benefit most will be those that combine AI scale with fiduciary discipline, governance, talent development and differentiated judgment.

Institutional investors have spent years hearing about the promise of artificial intelligence. That phase is giving way to a more practical question: not whether AI can create more scale, but whether that scale can be governed, validated, and translated into better fiduciary decisions. For OCIO providers, AI without discipline is not an advantage. It is simply faster complexity.

AI adoption is delivering real results now. For example, 94% of financial services companies reported AI-related ROI above expectations. More broadly, seven out of eight surveys, covering more than 42,000 business leaders across sectors, showed positive results¹. That level of consistency is rare for any new technology adoption cycle and it signals a shift in how firms are thinking about competitive advantage.

In the OCIO context, this shift is particularly meaningful. Outsourcing has always been about scale, expertise, and governance. AI amplifies all three. It allows firms to process more information, make decisions faster, and deliver insights more consistently, but the frontier is no longer experimentation or access. It is execution: how AI is embedded into governed investment workflows, how outputs are challenged, and how human judgment remains accountable for decisions.

The scale of investment behind AI reinforces its importance. In 2025, cloud and AI-related capital expenditures rivaled the inflation-adjusted cost of the Apollo space program². At the same time, investment in information processing equipment and software contributed meaningfully to U.S. GDP growth in 2025.

Large technology companies alone are expected to continue spending hundreds of billions of dollars annually on AI infrastructure, with year-over-year growth rates that continue to accelerate. This is not incremental change. It is a structural shift in how the global economy allocates capital.

For institutional investors, this matters in three ways:

1. AI is influencing market returns directly through its impact on corporate earnings and productivity.
2. It is reshaping how investment decisions are made, creating a new competitive frontier for OCIO providers.
3. It also means OCIO providers need to evaluate AI in two ways at once: as an investment theme affecting markets, sectors, and managers, and as a capability changing the investment process itself.

Manager research has historically been one of the most resource-intensive parts of institutional investing. It requires the synthesis of large volumes of data, both quantitative and qualitative. AI is transforming this process by dramatically increasing speed and breadth of analysis.

On the quantitative side, AI tools can digest large datasets, identify patterns, and generate summaries across thousands of products and strategies. On the qualitative side, large language models can analyze manager commentaries, pitchbooks, and transcripts to surface key themes and inconsistencies.

In private markets, the opportunity may be especially visible, because information is often fragmented across portals, emails, and inconsistent reporting formats. AI can support ingestion, normalization, summarization, discrepancy detection, and draft client outputs, with human validation at each step. At Russell Investments, our recent manager conversations suggest these tools are moving from pilots into production, with firms using AI to extract information from data rooms, accelerate screening, prepare investment committee materials, update models, summarize calls, and evaluate secondary opportunities more quickly.

For manager research, the result is not just efficiency, but improved coverage and consistency. However, these benefits come with risks. Poor data inputs can lead to flawed outputs, and overreliance on AI can weaken critical thinking. The strongest firms are addressing this by embedding AI within governed research workflows, rather than treating it as a standalone productivity tool.

AI is also expanding the toolkit available for portfolio construction. It can support idea generation, scenario analysis, and even initial valuation work. Investment teams can test more hypotheses, explore more scenarios, and respond more quickly to market changes.

However, the use of AI in portfolio construction introduces new challenges. One of the most important is signal crowding. As more firms use similar tools, the uniqueness of investment insights may decline. This can lead to alpha decay as opportunities are identified and exploited more quickly.

There is also the risk of narrative convergence. If multiple firms rely on similar AI-generated insights, their views may become more aligned, reducing diversification benefits. Synthetic data, if not carefully constructed, can also introduce biases or misleading conclusions.

For institutional investors, the implication is clear. The presence of AI is not enough. What matters is how it is used, how outputs are validated, and how firms maintain differentiated thinking. AI may make weak differentiation more visible. If AI standardizes baseline analysis, differentiation will depend less on producing more analysis and more on knowing which insights matter.

AI is not just enhancing investment processes. It is reshaping the structure of investment teams. Research suggests that early-career roles are particularly affected, with firms hiring fewer junior employees as AI takes on more routine tasks³.

At the same time, organizations are investing heavily in reskilling and training. AI literacy is becoming a core competency, and firms are developing new models for mentorship and on-the-job learning. This shift reflects a broader trend toward higher-skill, higher-judgment roles.

There is also a deeper apprenticeship question. Much of investment judgment is built by doing the hard work: writing research notes, preparing briefing materials, building models, testing assumptions, and learning from mistakes. If AI removes too much of that work too early, firms may produce faster analysts but weaker investors. Leading firms will need to use AI to accelerate development without eliminating the work that builds judgment.

For OCIO clients, this matters because it affects the depth and structure of the teams managing their assets. Firms that invest in talent development and integration are more likely to realize the benefits of AI. The question for clients is not only what technology an OCIO provider uses, but how its people are learning to use it responsibly.

One of the most important applications of AI is in risk management. Traditional approaches rely on periodic reporting and review cycles. AI enables a shift toward continuous monitoring, where risks can be identified and addressed in near real time.

This includes detecting inconsistencies across documents, identifying unusual patterns in performance, and monitoring exposures more dynamically. The ability to surface risks earlier can significantly improve decision-making and reduce downside outcomes.

However, these capabilities depend on data quality and governance. Without proper controls, AI can amplify errors rather than mitigate them. Firms with mature governance frameworks are focusing on explainability, validation, and oversight to ensure that AI enhances rather than undermines risk management.

Institutional investors have long sought more tailored investment solutions, but delivering personalization at scale has been challenging. AI is changing that by improving data integration and analytical efficiency.

OCIO providers can now align portfolios more closely with client-specific objectives, whether that involves liability matching for DB plans, participant outcomes for DC plans, or spending policies for nonprofits. Healthcare systems, with multiple pools of capital, can also benefit from more customized approaches.

The key advantage is scalability. AI can allow firms to deliver more tailored solutions without significantly increasing operational complexity, making personalization more accessible across client segments. It can also make client reporting and communication more portfolio-aware, connecting market events, manager updates, and exposures to the specific issues that matter for each client.

The most important takeaway is that AI itself is no longer a differentiator. Access to AI tools is widespread. What separates leading firms is how effectively they integrate those tools into their investment processes.

That integration requires more than technology. It requires governance, training, and a clear understanding of where AI adds value and where human judgment remains essential.

For institutional investors, the question is not whether an OCIO provider uses AI. It is how they use it, how they manage its risks, and how it improves outcomes. Clients are not outsourcing judgment to a machine; they are outsourcing complexity to a fiduciary.

As baseline analysis becomes faster and more standardized, AI will make the gap between providers more visible. The real differentiators will be proprietary data, manager access, original insight, governance, culture, talent development, and the ability to translate research into better portfolio decisions. In that sense, AI is not making OCIO less relevant. It is raising the standard for what best-in-class OCIO must be.

¹Morgan Stanley / AlphaWise, IBM / Harris Poll, Ernst & Young, Google, IBM / Oxford Economics, Snowflake, MIT / Nanda, Enterprise Technology Research, Boston Consulting Group

²Dreier (2022), Minneapolis Fed, Morgan Stanley Research

³Erik Brynjolfsson, Bharat Chandar, and Ruyu Chen (2025) Canaries in the coal mine? Six facts about the recent employment effects of artificial intelligence.

Seyed Mahdi Hosseini Maasoum and Guy Lichtinger (2025) Generative AI as Seniority-Biased Technological Change: Evidence from U.S. Résumé and Job Posting Data.