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Gauging the Trails Left Behind by AI Implementation and the Challenges That Remain the Mix

Info-Tech Research Group has officially published the results from its Build and Select AI Use Cases for Wealth and Asset Management report, where it was revealed that AI implementations are producing tangible results in four key areas, including increased advisory efficiency, reduced operating costs, increased assets, and improved profitability.

Going by the details, this particular report reveals that, even though AI's advent is already playing a big role in the context of providing personalized investment advice, optimizing trading, boosting client engagement, streamlining compliance, enhancing fraud detection, and increasing operational efficiency, it continues to suffer from certain challenges.

These challenges include unclear AI strategy, limited in-house expertise, and immature data.

Many firms see AI's potential but struggle with where to start, said. Christine West, a Senior Managing Partner at Info-Tech Research Group. By aligning AI initiatives to measurable outcomes and prioritizing high-value, high-feasibility use cases, wealth and asset managers can accelerate adoption while ensuring responsible, compliant, and sustainable implementation.

Focused on addressing the same, Info-Tech's report proposes a clear roadmap, which begins from identifying AI use case. This includes procuring cross-functional stakeholders to review business goals and capability maps, assess top AI opportunities, and build a business-aligned list of potential use cases.

The next step relates to prioritizing AI use case, a step which mandates the use of a structured scoring tool to identify high-value opportunities with low implementation complexity, mapping them on a value/feasibility grid.

Rounding up the roadmap would be preparation for implementation. Here, one must ensure systems and data are AI-ready, address security and compliance requirements, as well as avoid sharing sensitive data on public cloud platforms.

Turning our attention towards some of the possible areas where AI can deliver measurable business value, they include customer experience, as the technology can really goes the distance here to deliver personalized, responsive, and transparent services that build client trust and engagement.

Then, there is the prospect of enhancing advisor experience. You see, AI can come in handy to automate routine tasks to free advisors for higher-value client interactions and strategic decisions.

The technology in question can also bolster operational efficiency, leveraging streamlined processes and more precise outcomes to reduce delays and operational overhead.

Hold on, we still have a few bits left to unpack, considering we haven't yet touched upon the potential for risk reduction. We get to say so because AI can detect patterns, flag anomalies, and address potential risks before they escalate.

We also haven't touched upon the use case of revenue optimization, which includes optimizing marketing, outreach, and product personalization to expand assets under management and client base.

Finally, there is a call for cost optimization, something which is achieved on the back of scaling services through intelligent automation, thus keeping costs in check while increasing output.

AI is no longer an experimental technology for wealth and asset managers; it's a competitive necessity, said West. Firms that identify and prioritize the right AI use cases now will be best positioned to deliver personalized experiences, unlock operational efficiency, and capture new growth opportunities while staying compliant in a fast-changing regulatory environment.