AI Adoption Accelerates in Wealth and Asset Management as Firms Seek Competitive Edge
Wealth and asset management firms face rising customer expectations, cost pressures, and regulatory demands. To meet these challenges, many are adopting artificial intelligence (AI) technologies like Machine Learning and Generative AI. These tools are already helping improve advisory productivity, increase assets under management, and enhance client experiences.
Info-Tech Research Group’s recent blueprint, Build and Select AI Use Cases for Wealth and Asset Management, offers a practical framework and detailed AI use case libraries. This resource guides firms on how to identify, prioritize, and implement AI initiatives that deliver clear business value.
Why AI Matters Now
The wealth and asset management sectors are undergoing significant change due to demographic shifts, wealth transfer, and demand for greater personalization. AI adoption is picking up pace as firms look to stay competitive and meet evolving market needs.
Info-Tech Research Group’s research reveals four key areas where AI delivers measurable benefits:
- Increased advisory efficiency
- Reduced operating costs
- Growth in assets under management
- Improved profitability
The most successful outcomes come from choosing AI projects that align strongly with business goals and have manageable implementation complexity.
How AI Enhances Wealth and Asset Management
AI is already improving portfolio management by offering personalized investment advice, optimizing trading strategies, and boosting client engagement. It also helps firms streamline compliance, detect fraud more effectively, and increase operational efficiency.
Still, many firms face challenges such as unclear AI strategies, limited internal expertise, and immature data capabilities, which can slow progress.
"AI is no longer an experimental technology for wealth and asset managers; it's a competitive necessity," says Christine West, Senior Managing Partner at Info-Tech Research Group. "Firms that focus on the right AI use cases will be better positioned to deliver personalized services, improve efficiency, and seize new growth opportunities—all while staying compliant."
A Clear Framework to Select and Implement AI Use Cases
Info-Tech Research Group outlines a straightforward approach to help firms overcome common obstacles like unclear direction and regulatory hurdles:
- Identify AI Use Cases: Bring together cross-functional teams to review business objectives and capabilities. Assess top AI opportunities to create a business-aligned list of potential projects.
- Prioritize AI Use Cases: Use a scoring tool to evaluate each opportunity’s business value and implementation complexity. Plot these on a value/feasibility grid to focus on high-impact, achievable projects.
- Prepare for Implementation: Ensure systems and data are ready for AI. Address security and compliance requirements and avoid sharing sensitive data on public cloud platforms.
Key Areas Where AI Drives Value
The blueprint also highlights six primary areas where AI can deliver measurable benefits across wealth and asset management:
- Customer Experience: Personalize and improve responsiveness to build trust and engagement.
- Advisor Experience: Automate routine tasks to free advisors for strategic client interactions.
- Operational Efficiency: Streamline processes and improve accuracy to reduce delays and costs.
- Risk Reduction: Detect patterns and anomalies to manage risks proactively.
- Revenue Growth: Optimize marketing and personalization to grow assets and client base.
- Cost Optimization: Scale services through automation to control costs while increasing output.
"Many firms see AI’s potential but struggle with where to start," explains West. "By focusing on measurable outcomes and prioritizing projects with strong business value and feasibility, wealth and asset managers can speed up adoption and ensure responsible implementation."
The guide includes extensive AI use case libraries covering topics such as alpha generation, ESG investing, regulatory compliance, sentiment analysis, client onboarding, and portfolio optimization. Leveraging these insights helps firms move efficiently from planning to execution, turning AI investments into real gains in assets, profits, and efficiency.
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