Artificial intelligence (AI) is rapidly transforming the landscape of global asset management, presenting both unprecedented opportunities and complex challenges for sophisticated investors.

The ascendance of agentic AI and the autonomous middle office

Asset management is moving beyond generative AI for content creation toward agentic AI, which can coordinate and execute discrete workflows across the middle and back office. In practice, these systems are beginning to support tasks such as compliance checks, reconciliations, reporting, and workflow triage, helping firms improve speed, consistency, and operating efficiency while keeping humans in the oversight loop.

McKinsey & Company estimates that AI could unlock efficiencies equivalent to 25% to 40% of an asset manager’s total cost base, while Boston Consulting Group (BCG) projects that agentic AI could reduce costs by 25% to 35% and automate a substantial share of execution tasks across the value chain.

In an industry facing ongoing margin pressure, AI is not just a technology initiative but a strategic response to structural cost compression. For institutional allocators, the relevance is clear: firms that can embed AI into the operating model effectively may be better positioned to protect margins, scale more efficiently, and improve consistency in governance and service delivery.

Unlocking value from unstructured data

That same imperative to improve efficiency is also reshaping the investment process itself, as firms look beyond operating leverage and toward better decision-making. In particular, AI is opening up new ways to extract insight from the vast pool of unstructured information that has traditionally been too difficult to analyse at scale.

Leading firms including State Street Global Advisors and BlackRock are applying natural language processing (NLP) and large language models (LLMs) to extract real-time actionable insights from earnings call transcripts, regulatory filings, and management commentary to surface signals that would be difficult to capture manually. Meanwhile, machine-learning models are being used to improve forecasting and screening across large universes of securities. State Street's application of XGBoost-based models for fundamental forecasting has demonstrated superior accuracy compared to traditional sell-side analysts across various regions.

As global fund managers leverage AI to mine this unstructured data, there is a general understanding that AI is not set to replace human judgment but rather enhance it. By helping portfolio managers and analysts process far more information than was previously practical, AI can improve idea generation, monitoring, and risk assessment across thousands of names and multiple data sources.

For institutional investors, that makes AI relevant not only as an efficiency tool, but as a capability that may influence the quality of both active management and operational diligence.

Precision ESG: Bridging the data quality gap

Beyond investment operations, AI is also beginning to improve how investors assess non-financial risks, particularly where ESG data quality has historically been uneven, incomplete, or difficult to verify.

By processing a wider range of inputs, including satellite imagery, news sentiment, supply chain information, and company disclosures, AI can provide a more granular and evidence-based view of climate and social risk.

AI is particularly valuable in ESG analysis because much of the underlying data remains fragmented and difficult to compare across companies, sectors, and regions. That makes it useful not only for screening, but also for ongoing monitoring, engagement, and risk assessment.

Research indicates that AI can improve ESG predictive accuracy by up to 30%, while market growth in climate risk analytics and related tools suggests rising demand from institutional investors. For example, thanks to AI’s enhanced analytical capability the climate risk management market is projected to expand from $8.7 billion in 2025 to over $100 billion by 2035.

Consequently, AI is transforming ESG from a mere compliance exercise into a fundamental driver of risk-adjusted returns, enabling the development of highly precise and impactful ESG investment strategies.

AI as a "mega force" in private markets

Given the preponderance of fragmented, non-reported unstructured data in private markets, the potential for AI to become a mega force in how fund managers analyse deals and projects, is enormous. Private equity firms are using AI to sharpen underwriting in data-sparse environments, accelerate portfolio analysis, and identify operational improvements inside portfolio companies. At the same time, allocators themselves are beginning to integrate AI into investment workflows and monitoring processes, which suggests the technology is moving from experimentation into institutional practice.

Private markets are particularly well suited to AI adoption because they remain information-rich but data-fragmented. Unlike public markets, where disclosures, pricing, and trading data are relatively abundant, private assets often rely on limited reporting, uneven KPIs, and unstructured company-level information. That creates a natural use case for AI tools that can help investment teams aggregate, normalise, and interpret large volumes of qualitative and semi-structured data more quickly than traditional workflows allow.

At the same time, the adoption curve is broadening beyond deal teams.

GPs are beginning to use AI across fundraising, portfolio monitoring, operational value creation, and exit preparation, while LPs are increasingly looking at how managers use AI as part of broader underwriting and operational due diligence.

In that sense, AI is becoming both a source of alpha and a diligence lens in its own right: allocators are no longer only asking where AI is being invested in, but also how effectively it is being embedded into the investment process itself.

The other side of the story is the infrastructure buildout.

BlackRock estimates that annual investment in this AI infrastructure could exceed $700 billion by 2030, representing over 2% of US GDP. Concurrently, S&P Global reports that venture capital investment in late-stage AI companies has tripled between 2024 and 2025.

Taken together, these dynamics suggest that AI is creating a new investable ecosystem within private markets. For institutional allocators, the opportunity lies not just in exposure to AI-enabled businesses, but in the durable, long-duration assets that underpin the buildout itself, offering potentially inflation-linked returns akin to traditional infrastructure investments.

The future of AI in global asset management

AI is no longer a peripheral innovation in asset management; it is becoming embedded in the operating model, the investment process, and the broader opportunity set facing institutional investors. Its importance lies not only in the efficiencies it can create, but also in the way it is reshaping how firms assess risk, generate investment insight, and allocate capital across public and private markets.

As adoption broadens, asset managers will need to ensure they use this technology with care. AI is reshaping risk management from a reactive, compliance-led function into a more proactive and strategic capability.

However, there is also a risk of “algorithmic herding”, where similar models drawing on similar data could lead to correlated positioning and more synchronised market moves.

For institutional investors, the task is therefore not simply to adopt AI, but to do so with the governance, discipline, and oversight required in a more complex and increasingly automated investment environment.

Discover the future of asset & wealth management operations at FundForum, where the future of wealth is defined.