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The increased interest in AI is driven by vast changes in the distribution landscape. In the last five years, more than three-quarters (79%) of asset managers have made substantial changes to their coverage strategy. Among the top reasons for change are new management (42%) and distribution of a new product line such as alternatives (33%)-a key trend proliferating across the industry.

"Where AI may have its greatest application when determining advisor segmentation and coverage strategy is through the identification of actionable data amid huge amounts of information," says Andrew Blake, associate director. "The overwhelming amount of data asset managers have at their disposal is not very helpful unless it can be properly analyzed and implemented into sales processes. Identifying growing advisor practices and efficiently targeting them is a work in progress for most asset managers," he adds.

Another potentially strong use case for AI will be the distribution of alternative investments. As traditional managers increasingly acquire alternatives capabilities, AI can help determine effective approaches to targeting advisors and reaching untapped client segments. "Cerulli recommends asset managers explore AI use cases to ensure they are competitively using data to take a targeted approach to their coverage model," says Blake.

Overall, effective use of AI by asset managers will start with quality data inputs that help these tools better provide personalized product recommendations and targeted marketing strategies. As the distribution landscape evolves and asset managers expand their product capabilities, AI tools may be able to help asset managers improve the efficiency of prospecting and penetrate new advisor practices.

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