Institutional Investor Monitor – Q1 2026: Cautious Optimism and Portfolio Resilience are key
- Best-in-class over consolidation: Investors overwhelmingly prioritise selecting the best manager for each mandate. While they recognise potential fee savings from manager consolidation it is not a driver of choice.
- Trust and alignment matter: At the final stage, softer factors such as trust, clarity of communication, and alignment with institutional objectives often determine the outcome.
- Fees are important but not decisive: Investors will negotiate and exclude outliers, but are willing to pay higher fees for managers they strongly believe in. In private markets especially, fee flexibility is often limited.
Even if the message is primarily one of “keep calm and carry on”, investors appreciate channels of communication being open, transparent and frequent.
Addendum AI Adoption: Interest High, Implementation Cautious Artificial intelligence is on the agenda, but adoption among investors remains limited and uneven. Most institutions are using AI only in incremental, low-risk ways, for productivity tasks such as summarizing documents, proofreading, or supporting research. In some cases, AI is being used to review legal documents or compare fund terms, but rarely as a core input into investment decision-making. Barriers to adoption are significant. Data security is a concern, especially for public institutions which manage sensitive personal information. Others cite a lack of internal resources or technological infrastructure. We sometimes forget that many institutions, while they may invest billions of dollars, only have small investment teams and make up only a fraction of their organization’s overall headcount. Investors in these types of organizations acknowledge they are unlikely to be early adopters. But even among larger investors it’s clear that AI adoption for investment purposes is a slow burner. One mega-plan we spoke to described how their internally built AI model is only able to view internal sources of information, or other information which is directly fed to it as it’s not able to look at external data sources. In contrast, expectations for asset managers are much higher. Investors increasingly expect managers to have a clear AI strategy, whether for investment research, operational efficiency, or portfolio company value creation, and they want to learn more from them. Two managers stand out as leaders, both of which have longstanding expertise in quantitative or machine learning strategies. However, most managers are perceived to be in early adopter stages, often deploying AI for internal efficiencies (e.g. summarizing research or automating workflows) rather than generating differentiated investment insights. Some investors are still wary of overhype. There is concern that both managers and markets may overestimate the near-term impact of AI. | ||
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