Asset managers have a new playbook for AI - and catching mistakes is just the start
The Investment Company Institute issued a new framework on October 29, 2026, calling on asset managers to rethink how they apply artificial intelligence - not as a reactive tool that patches problems after the fact, but as a proactive system that prevents them from arising in the first place.
The paper, published by ICI's Financial Innovation Advisory Committee (FIAC) and titled AI and the Future of Asset Management Operations: Considerations for an Intelligent Operating Model, outlines a three-part approach that connects information across fragmented systems, flags emerging risks earlier, and frees experienced professionals to focus on decisions that require human judgment.
The model is intended to address a widening gap between the complexity of modern asset management and the operational capacity of firms to manage it.
"Imagine identifying and correcting a settlement problem before sending the trade details out the door, or giving fund professionals a clearer picture of emerging risks while there's still time to act " ICI’s Andrew Kayiira, Director, Technology & Financial Innovation and R.J. Rondini, Senior Director, Securities Operations, wrote in the viewpoint accompanying the paper.
From disconnected systems to connected intelligence
The core argument in the ICI framework is that technology has helped firms manage complexity but hasn't fully solved the problem. Operational teams still spend significant time piecing together data from siloed systems - a challenge that grows more acute as firms launch more products, extend into new distribution channels, and face tightening settlement windows under T+1 rules.
The paper introduces what ICI calls an Intelligent Operating Model, built around three objectives: preventing recurring operational errors before they occur, maintaining a broader view of activity across the business in real time, and surfacing potential risks early enough for employees to act rather than react.
Consider a straightforward example cited in the paper: a large shareholder redemption. The liquidity decisions that follow can depend on information held across trading and shareholder-servicing teams - information that, under current models, staff must assemble manually as events unfold.
An AI-enabled system, the ICI argues, could bring that picture together automatically, giving decision-makers context in the moment rather than after the fact.
The same logic applies to valuation. If a security's price looks questionable ahead of a fund's net asset value calculation, an AI system could gather the relevant data before the calculation runs, giving specialists more time to assess the issue rather than scrambling to respond after it has already affected the fund's NAV.
What the ICI wants firms to do
The paper does not call for wholesale transformation. Instead, it recommends a practical starting point: identify a recurring operational problem and test whether AI can improve on current results. Success might mean fewer settlement exceptions, less investigative time per incident, or the ability to scale operations without adding proportional headcount.
The ICI framed this incremental approach as the most credible path to more ambitious capabilities, including what the industry is now calling agentic AI - systems capable of executing multi-step tasks with limited human intervention.
That framing echoes a broader shift in how the wealth and asset management industry is positioning AI. As Wealth Professional has reported, wealth management firms are investing heavily in artificial intelligence but overwhelmingly failing to measure or generate the new revenue they say AI should produce..
A regulatory signal firms should heed
The paper was released alongside a pointed challenge from the SEC's Division of Investment Management. Director Brian Daly, speaking at a Stanford roundtable on October 16, 2026, urged investment advisors and fund sponsors to "enthusiastically innovate in a responsible and thoughtful manner" rather than wait for regulators to define the acceptable boundaries of AI adoption.
The ICI interpreted that challenge as a call to act. "Director Daly has challenged the industry to lead," the paper states. "We should answer that challenge with a fundamentally better way to operate."
That regulatory context matters for advisory firms and their asset manager partners. The SEC has been watching how AI is deployed across investment operations - including valuation, compliance workflows, and trade execution - and firms that build out governance structures now are better positioned to demonstrate accountability if scrutiny increases.
A Mercer survey of asset managers in 2026 found that while 55 percent had integrated AI into at least one investment process, adoption remained uneven and largely concentrated in front-office functions such as research and idea generation. The ICI's paper pushes attention to the back and middle office - the operational layer where problems, if left unaddressed, can affect investors directly.
Human judgment remains central
Throughout the framework, ICI is careful to position AI as a support layer rather than a replacement for professional expertise. The goal, as the paper describes it, is to give experienced professionals more time and better information to tackle complex challenges - not to automate away the judgment that investment operations require.
That distinction is likely to resonate with financial advisors and wealth managers who are already navigating questions about how far AI should be trusted in client-facing and portfolio contexts, especially amid concerns including a warning from Fitch that AI governance gaps leave advisors exposed to a new wave of cyber threats.
The ICI model reinforces that boundary. Connected information, clear governance, and human accountability are described throughout as essential elements of the framework.
