Building an AI Tech Stack That Works for You and Your Clients & Closing Remarks

There's no debate that agentic AI has the potential to profoundly impact how financial advisors and wealth managers acquire, retain and deepen engagement with clients. What's often overlooked in the what-if scenario planning of the agentic AI era? Building an AI tech stack that works for financial advisors and their clients. In this panel discussion, industry experts will talk about why it's critical to align technology with specific workflows, compliance requirements and personalization objectives. As the talk on use cases heats up, here's what our panelists say that industry players need to consider in building their AI tech stack:







·  Identify business goals and use cases first, including client engagement (personalized recommendations, behavioral insights, automated reporting), advisor productivity (intelligent CRM, smart task prioritization, etc.), compliance and risk (KYC/AML monitoring and trade surveillance), and portfolio management (dynamic rebalancing, AI-driven allocation, tax optimization).







·  Core components of the AI tech stack, including the data layer (data sources, integration tools, warehouse or lake/lakehouse), intelligence layer (machine learning/AI models, agentic AI and model ops), and application layer (client-facing tools, advisor dashboards, conversational interfaces).







·  Security, compliance and ethics, including data privacy, regulatory compliance (SEC/FINRA, GDPR/CCPA adherence in data handling), explainability and bias mitigation.







·  Integration and interoperability, including open APIs to connect with wealth platforms, workflow compatibility, and third-party plug-ins.







·  Governance and monitoring, including model governance, performance monitoring and human-in-the-loop needs for higher risk recommendations or compliance review.







·  Testing and feedback loops, including advisor feedback, client insights on usage, satisfaction and conversion improvements, and testing for UI/UX and model performance pre-rollout.







·  Scalability and future-proofing your tech stack with a cloud-native architecture, modular stack and the ability to plug into large language models (LLMs) and multi-agent frameworks as the field evolves.