Mandates on AI Projects Tighten as Asset Managers Focus on ROI, and Efficiency

Laurent Louvrier

The asset management industry is approaching a shift in how it evaluates and implements artificial intelligence. After multiple years of cautious experimentation, many firms are beginning to tighten the mandates of AI projects. The new focus: clear ROI, operational efficiency and improved time-to-value.

Having worked actively on such projects with asset managers, we’re seeing AI move beyond the innovation lab, and there is, overall, more collaboration between technology leaders and business heads. AI is expanding beyond internal innovation or IT teams, with broader involvement from product, compliance and client service teams. This cross-functional engagement is essential as AI moves from providing proof of concept to yielding a sustained operational advantage.

Buy vs. Build: Time-to-Value Wins

Senior leaders are applying pressure for faster decisions, and in many cases, this is accelerating the classic buy vs. build decision. Time-to-value and scalability are becoming key decision factors in AI adoption. In practice, this means that off-the-shelf AI tools with domain-specific applications are gaining traction over more time-consuming and resource-intensive custom development efforts. The cost and complexity of building in-house tools are becoming harder to justify when proven commercial solutions exist.

But choosing to buy doesn’t mean compromising functionality. Asset managers use AI for specific, high-impact functions that align closely with known operational pain points. A few of the most common examples are:

• Consolidating and normalizing large, unstructured datasets for investment research;
• Automating regulatory and client reporting workflows; and
• Extracting insights from private investment data.

These are not theoretical use cases. They are real-life examples currently being implemented by firms looking to stay competitive in an increasingly margin-conscious environment. All-purpose models may not have the domain-specific data or safeguards required to ensure predictable and compliant handling of sensitive material. Take the private investment market. Traditionally, this area has been clogged with time-consuming manual data entry and reconciliation. AI tools can now read and classify private asset documents, extract the relevant data and validate it against existing systems. This reduces manual effort and supports more consistent data delivery to clients and regulators.

Governance and Compliance Take Center Stage

One consistent thread: Successful AI projects are integrated into the workflow, not awkwardly layered on top. Operational context is vital—correct briefings and project scopes are essential. AI should not be an add-on; it should be designed to solve real business problems with minimal friction or complexity.

Firms are becoming more disciplined about governance. Questions of model explainability (or interpretability), data lineage and risk oversight are front and center. Stakeholders increasingly expect outputs that are not only accurate, but explainable, defensible and auditable. This is particularly important for asset management firms operating across jurisdictions or managing institutional mandates.

This typically means partnering with a specialty technology provider that is already used to the unique compliance and reporting requirements in this area. That provider offers tools designed to be easily deployed and aligned with operational risk management practices.

Looking Ahead: AI as a Competitive Advantage

So what’s next? AI adoption is poised to accelerate, driven in part by the current regulatory landscape. In the U.S., President Donald Trump’s administration is unlikely to introduce sweeping AI regulations in the near term, offering asset managers a more stable environment in which to implement AI tools with less concern about rapidly changing compliance rules. Meanwhile, Europe and the U.K. are also proceeding cautiously, giving companies the flexibility to deploy AI where it can create value for both investors and businesses.

For many asset managers, the strategic question is not whether to use AI; it’s where to start and how to scale. That shift—from exploratory to intentional—will define the next phase of AI adoption in this industry. For many firms, AI has moved beyond the experimental phase and is becoming a core operational capability and competitive lever. The firms moving fastest are those treating AI not as a moonshot, but as a practical tool to run smarter, leaner and more responsive operations.

AI will become more embedded across the investment lifecycle—from research and due diligence to post-trade compliance and investor reporting. The firms that benefit most will focus less on chasing hype and more on targeted use cases with measurable outcomes directly integrated into operating plans.

Laurent Louvier is a vice president of product for artificial intelligence at Confluence.

This feature is to provide general information only, does not constitute legal or tax advice, and cannot be used or substituted for legal or tax advice. Any opinions of the author do not necessarily reflect the stance of CIO, ISS Stoxx or its affiliates.

Tags: Artificial Intelligence, Asset Management