AI Is Changing Fund Management. Investors Need to Know What’s Changing Behind the Screen

AI has quickly established an integral role in fund managers’ investment processes. For most, it has already become an integral part of their investing machinery. Fund managers are using it to read more, screen faster, test risks earlier and monitor their portfolios across larger pools of data.  

For investors, understanding how fund managers use AI, what controls sit around it, and whether it improves their investment processes after fees, risk and human judgement are taken into account has never been more relevant. 

AI Has Moved from Experimental to Integral 

The strongest early use case for AI in funds management is in enhanced research productivity. 

In fact, a Barclays survey of 410 fixed-income investors found that 52% of fund managers primarily use AI for research. Hedge funds were more likely to use it to process and analyse large volumes of market data, at 44%, while 27% of them use AI primarily for modelling and risk analysis. 

In contrast, operations, compliance, reporting and direct investment decisions each accounted for only 10% to 15% across the investor groups surveyed.  

That suggests AI is changing the inputs to fund managers’ decision-making before it changes their final decisions. 

It’s easy to understand why. 

A portfolio manager can now ask AI to summarise company transcripts, compare management language across quarters, flag changes in broker forecasts, scan bond covenants, cluster supply-chain exposures, or identify which holdings might be most exposed to a new regulation.  

None of those capabilities remove the need for human judgement, although they do change the speed and breadth of the judgement being made. 

This is already widespread across the industry. The AI adoption curve is steep and almost universal.  

AIMA reported in September 2025 that 95% of surveyed fund managers were using generative AI in their work, up from 86% in 2023, while 58% expected to use it more in their investment processes over the coming year.  

It also found that 60% of institutional investors would be more likely to invest in a hedge fund allocating a meaningful portion of its budget to generative AI research and implementation.  

That’s an important point to understand. The fund managers who best master and optimise AI within their investment processes are already seeing a market advantage in terms of investor interest. 

In other words, this train has very much left the station. 

A Primary Benefit is Coverage 

Scarcity of resources used to be a defining factor in the way fund managers managed their research pipelines.  

Overworked analysts have limited time, so they used to have to choose which companies to cover, which calls to attend and which filings to read in detail.  

AI directly addresses that constraint. 

This is a particularly important development within the market segments where information is plentiful but fragmented. 

For example, small caps, emerging markets, credit markets, private assets and sustainability-linked investments tend to be less efficient markets which require fund managers to invest valuable internal resources into assessing filings, presentations, news, alternative data and direct company engagement.  

Historically, these are also markets in which doing the legwork has been associated with outperformance because there are less analysts and investors competing in these segments. 

With the help of AI, fund managers can now find patterns, themes, and relevant data points across those segments faster than any human team could manually. 

Performance Upsides? 

You may be thinking that this creates the potential for active funds to generate the type of outperformance that was unheard of before the rollout of generative AI. 

Possibly, but not necessarily.  

Here’s the rub… If every manager uses similar AI models trained on similar data, the market may simply become faster at digesting the same information. That may imply a shifting but still level playing field.  

The short-term upside is more likely to be an improvement in the research hygiene of fund managers’ investment processes. The less time analysts spend summarising and digesting material, the more time they can spend asking whether the conclusion is actually investable. 

Risk Management is Becoming More Continuous 

Another major area of change is risk management.  

Older portfolio risk systems were often periodic. They looked at exposures, factor risks and stress tests at set intervals.  

In contrast, AI can make risk monitoring more continuous, particularly when concurrently digesting live news, market prices, company disclosures and portfolio data. 

For a bond manager, that may mean faster alerts when a borrower’s language changes around liquidity.  

For an equity manager, it may mean mapping hidden dependencies between companies, such as shared suppliers, common customers or exposure to the same semiconductor cycle.  

For a multi-asset manager, it may mean testing how a portfolio behaves under different inflation, currency or liquidity regimes. 

For example, Northern Trust has described how it uses AI tools to collate company data and map relationships between businesses, with the aim of identifying connections that may be relevant to alpha generation.  

Human Fund Managers Still Have a Role to Play 

It’s important to highlight that thus far AI is enhancing the role of human fund managers rather than replacing them. 

The most credible AI users in funds management tend to use it to improve their process with a focus on removing uncertainties. 

But as you’ll no doubt have learnt in your own experience, AI isn’t perfect. 

Large language models can hallucinate. Data can be stale. Back-tests can be overfit. Models can reinforce consensus.  

If many managers use similar systems, AI may increase crowding rather than reduce it.  

So, while AI brings clear benefits to fund managers en masse, it also creates risks around transparency, accountability, bias and over-reliance.  

It creates governance gaps around AI innovation, including how firms identify and mitigate consumer risks.  

That effectively elevates governance into an investment issue, rather than just a compliance issue.  

What It All Means for Active Funds 

There are many interesting upsides AI presents to the world’s fund managers.  

It could make active management more competitive in some areas.  

If a manager can cover more securities, analyse more unstructured data and detect risk faster, it may improve the odds of identifying mispriced assets. As mentioned, that’s particularly relevant in less efficient markets such as smaller companies, credit, emerging markets, alternatives and private assets. 

But AI also raises the bar.  

If basic company research and data become valueless, investors may become less willing to pay high active fees for processes that do little more than repackage public information.  

As such, active managers will need to invest more effort in showcasing to investors where their edge comes from.  

They’ll also need to show investors how they generate and manage proprietary data, construct their portfolios, manage risk, secure management access and act as ethical stewards of capital, as well as demonstrating their sector expertise and judgement. 

An AI-Focused Checklist for Investors  

As a result of these shifts, before investing in an active fund, investors should now ask a few practical AI-focused questions: 

• Is AI part of the investment process, or only part of the marketing story?
• Does the manager explain where AI is used: research, risk, execution, compliance, reporting, portfolio construction, or idea generation?  
• Is there human accountability for decisions?  
• Are the fund fees justified by a genuine process advantage?  

More investors are reaching the conclusion that if a fund manager can’t answer these questions well, they may not be using AI thoughtfully or optimally. 

A New Era for Active Management 

AI is reshaping the global fund management industry at a speed we’ve not witnessed before. It’s changing what analysts can read, how managers test risk, how portfolios are monitored and how quickly information is absorbed.  

The best fund managers are becoming faster, broader and more disciplined. The weakest are becoming more inundated by the market’s noise, more generic and more dependent on tools they don’t fully understand.  

For investors, the opportunity is to become more demanding. AI capabilities should be properly assessed like any other part of the investment process: does it improve decision-making, reduce risk, justify fees and fit the role the fund is meant to play in a portfolio? 

Disclaimer: This article is prepared by Simon Turner. It is for educational purposes only. While all reasonable care has been taken by the author in the preparation of this information, the author and InvestmentMarkets (Aust) Pty. Ltd. as publisher take no responsibility for any actions taken based on information contained herein or for any errors or omissions within it. Interested parties should seek independent professional advice prior to acting on any information presented. Please note past performance is not a reliable indicator of future performance.