ICI urges asset managers to use AI preventively

Asset managers are being urged to treat AI in asset management as a preventive tool rather than a quick fix, after the Investment Company Institute released a new framework on October 29, 2026.

The document, prepared by the institute’s Financial Innovation Advisory Committee, calls for a shift from reactive error correction to proactive risk avoidance across all operational layers.

Intelligent operating model outlined

The framework, titled AI and the Future of Asset Management Operations: Considerations for an Intelligent Operating Model, proposes three linked goals: stop recurring operational mistakes before they happen, keep a real-time view of activity, and surface risks early enough for staff to act.

Authors Andrew Kayiira and R.J. Rondini illustrate the concept with a scenario where a settlement issue is identified and fixed before trade details are sent out, giving fund teams a clearer risk picture in time to intervene.

One concrete use case involves large shareholder redemptions. Currently, liquidity decisions rely on data spread across trading and shareholder-servicing groups, which staff must assemble manually as events unfold, creating delays and potential mismatches.

Connecting fragmented systems

Operational teams still piece together information from siloed platforms, a burden that grows as firms launch new products, expand distribution channels, and adapt to tighter settlement windows under T+1 rules.

In valuation, a questionable security price can skew a fund’s net asset value. An AI-enabled workflow could pull relevant data before the calculation runs, allowing specialists to review issues ahead of time instead of scrambling after the fact.

By automating data aggregation, firms can reduce the investigative effort per incident and lower the frequency of settlement exceptions, according to the paper’s recommendations.

Incremental adoption strategy

The report advises firms to start small: pick a recurring operational problem, test an AI solution, and measure improvements such as fewer exceptions or reduced manual effort.

Success in these pilots could pave the way for more advanced “agentic AI” systems that execute multi-step tasks with limited human input, though the study cautions against wholesale transformation before proof of concept.

A Mercer survey of asset managers in 2026 found that 55 percent had integrated AI into at least one investment process, but most deployments remain in front-office research rather than back-office operations.

Targeting the middle and back office aligns with the paper’s focus on areas where unresolved errors can directly affect investors.

While AI can streamline processes, the framework stresses that technology should augment—not replace—human judgment, preserving the expertise needed for complex operational decisions.

Regulatory backdrop

During an October 16, 2026 Stanford roundtable, SEC Division of Investment Management Director Brian Daly urged advisors and fund sponsors to “enthusiastically innovate in a responsible and thoughtful manner,” rather than waiting for regulators to set AI boundaries.

The institute interprets Daly’s remarks as a prompt for the industry to lead, stating that firms should answer with a fundamentally better way to operate.

Regulators are closely monitoring AI use in valuation, compliance workflows, and trade execution, making early governance structures valuable if scrutiny intensifies.

Fitch has warned that gaps in AI governance could expose advisors to new cyber threats, reinforcing the need for clear oversight alongside technological advances.

Human expertise remains central

The paper positions AI as a support layer, aiming to give seasoned professionals more time and better information to tackle complex challenges, not to automate away essential judgment.

Addressing the operational-capacity gap

The model seeks to narrow the widening divide between the increasing complexity of asset management and firms’ ability to operate efficiently. It targets gaps where current processes cannot keep pace.

This approach is part of an industry-wide shift, as wealth-management firms invest in AI to improve operations and decision-making, with the goal of creating a more efficient and effective asset management process.