How investment managers are using AI for fund research in 2026

These are not low-value activities in principle. The decisions that follow them matter. But the process of assembling the raw material is manual, slow, repetitive and it is quietly consuming time that investment teams cannot afford to lose. 

Investment management firms across the UK and Europe are beginning to quantify that cost. What they find is rarely a surprise, but it is consistently uncomfortable: a meaningful share of skilled analyst time is spent not on analysis, but on the work that precedes it. 

AI is changing that. Specifically, purpose-built AI research tools embedded in the platforms investment teams already use. The question for 2026 is no longer whether this shift is happening. It is whether your firm is part of it.  

Where investment teams lose the most time 

The research workflow at most investment management firms follows a recognisable pattern. A question arrives from a portfolio manager, a compliance officer, a distribution team preparing for a client meeting. Someone needs to know how a fund compares to its peers on OCF. Or how a competitor’s transaction costs stack up. Or what the regulatory data shows for a specific share class. 

The answer exists. It is in the data. But retrieving it, validating it, formatting it, and making it ready for use takes time. Often hours. Sometimes days, depending on the complexity of the question and the number of systems involved. 

A member of the FE fundinfo team described the problem directly in a recent webinar on AI in investment management: “Your team spend hours on manual fund research, consumer duty reporting, peer comparisons, fund launches.” 

The teams carrying this burden are not slow or under-resourced. They are working within systems that were never designed for the pace at which decisions now need to be made. The research infrastructure has not kept up with the research demand. 

The consequence is not just lost time. It is delayed decisions, slower responses to distributors, and strategic insight that arrives too late to be acted on.  

What AI-powered fund research looks like in practice 

Nexus AI’s fund intelligence agents address this directly. Rather than requiring analysts to navigate multiple systems, run manual queries, and assemble data by hand, the conversational interface lets them ask questions in plain language and receive sourced, reasoned answers in seconds. 

The scope covers the queries that take up most of the research day. Benchmarking charges. Comparing risk-adjusted returns across competitors. Pulling regulatory data for consumer duty reporting. Checking peer performance figures ahead of a distribution meeting. Each answer is generated from FE fundinfo’s golden source of data, 9 million verified data points across more than 100,000 active funds and 300,000 share classes globally. 

Critically, every output is auditable. As Andrew Flook, Head of AI Innovation at FE fundinfo, explained in the same webinar: “Every answer is reasoned and explained and every figure is cited back to the data golden source.” That means the analyst who asks a question does not just get a number. They get a sourced, dated, verifiable answer they can use directly in a board presentation, a pitch book, or a regulatory submission. 

This matters in a regulated environment. Plausible-sounding data assembled from unverified sources creates risk. Data grounded in a verified, industry-specific source does not. 

The practical impact of moving research from hours to seconds compounds over time. Across a fund range and a full working year, investment management firms using fund intelligence tools are reclaiming more than 50% in time spent on research. That is time returned to the activities that require genuine analytical judgement: interpretation, strategy, and client engagement.  

Why the data source determines the quality of the output

A question that investment teams ask when evaluating AI research tools is how to know whether the output can be trusted. It is a reasonable question. Generic AI tools trained on publicly available internet data will generate answers about fund performance, fees, and regulatory data. Some of those answers will be accurate. Many will not be. The tool has no reliable way to tell you which is which. 

Purpose-built AI for investment management works differently. The output is only as reliable as the data underpinning it. FE fundinfo’s approach grounds every query response in its golden source: the same dataset that powers 550,000 regulatory documents produced annually and has been built over 30 years serving regulated financial institutions across 75 jurisdictions. 

The distinction matters for two reasons. First, it determines accuracy. Second, it determines auditability. A research tool that cannot tell you where a figure came from, and when it was last verified, cannot support the standards of evidence that investment management requires. 

In practice, the Nexus AI conversational interface attaches a source citation and an “as of” date to every data point it returns. The analyst knows exactly what they are working with. So does the compliance officer reviewing the output.  

What this means for marketing and content teams 

The research problem is not confined to investment and compliance teams. Marketing and content professionals in investment management face a version of the same challenge, and it surfaces most clearly in distribution. 

Content teams need accurate, current competitive data to support the materials that sales and distribution teams take into market. Factsheets, pitch books, fund comparisons, regulatory disclosures. The data underpinning these materials needs to be sourced, verified, and up to date. Assembling it manually introduces delay, inconsistency, and the risk of presenting figures that have already moved. 

Fund intelligence tools shorten this cycle considerably. When a distribution team needs to know how a fund’s fees compare to its top five competitors before a meeting, they can get a citeable answer in seconds rather than commissioning a manual data pull that may take days. When a content team needs current peer comparison data for a factsheet update, they can query the same source that powers the rest of the firm’s regulatory output. 

This also addresses a recurring pressure point in fund distribution. Distributors ask questions in meetings and expect immediate, sourced answers. Investment management firms that can respond in real time, with data they trust, are better positioned than those that promise to follow up. 

Speed of response is not just an efficiency benefit. In distribution, it is a competitive one.  

The shift that is already under way 

Investment management firms are not waiting for AI research tools to mature before adopting them. The shift described in FE fundinfo’s recent webinar on the AI-orchestrated investment manager is already visible in how leading firms are restructuring their research workflows. 

The firms pulling ahead are not necessarily the largest. They are the ones that have moved from treating AI as an assistant for individual tasks to embedding it in the workflows where research and insight are produced. Conversational access to verified fund data. Automated peer comparisons grounded in the golden source. Competitive intelligence available in real time during client meetings. 

For teams still working from manual processes, the gap is growing. The research question that takes a competitor two minutes to answer is taking your team two days. That difference shapes distribution outcomes, investment decisions, and client confidence. 

Nexus AI is purpose-built for investment management professionals who need to move from question to insight without the overhead of manual research. It is embedded directly in the Nexus platform, operates inside your secure environment, and draws exclusively on FE fundinfo’s verified data.