AWS re:Invent 2025 - LPL Financial and Incedo: Accelerating Advisor Success with AI & Cloud (ANT102)

Modernizing Wealth Management with AI and Cloud: Accelerating Advisor Success

Opportunity: Transforming the Advisor Experience with AI

• Personalization has been a key challenge in wealth management, as clients are constantly changing their preferences and demands
• AI can take on the heavy lifting for advisors, automating 60-70% of their time spent on tasks like preparing for meetings and collecting documents
• This allows advisors to focus more on building relationships and providing value-added services to clients

Business Model Transformation

• AI presents an opportunity for LPL Financial to "leapfrog" a generation and close the capability gap with larger wealth management firms
• LPL can leverage AI-powered models and skip the traditional product and feature development cycle, going straight to more advanced, agile solutions
• This can drive margin expansion and revenue growth by improving the customer experience and operational efficiency

Optimizing Cost to Serve with AI

• AI can transform end-to-end workflows like account opening, commission processing, and money movement
• Automating exception cases and supervision tasks can significantly reduce the human effort and cost involved in these processes
• Personalized, AI-powered experiences for advisors can also help lift margins by streamlining their workflows

Challenges and Considerations

1. Data Ecosystem: Building a modern, event-driven data architecture that can support AI and analytics is a critical foundational step
2. Security and Trust: Ensuring the security and trustworthiness of AI-powered systems is paramount in the highly regulated financial services industry
3. Talent Transformation: Upskilling leaders and teams to embrace and work with AI is essential, as the required skill sets are evolving rapidly

Sequencing and Prioritization

• Focusing on the right sequence of initiatives is key, starting with the data and cloud foundation before moving to AI use cases
• Democratizing access to data and AI tools across the organization is crucial to build momentum and engagement
• Addressing security and trust concerns should be a priority to ensure the successful adoption of AI solutions

Talent and Organizational Transformation

• The next generation of talent will need to manage a combination of human and AI agents, requiring a shift in mindset and skill sets
• Hiring more ML engineering talent will become a priority, as opposed to traditional software engineering roles
• Leaders and teams need to actively embrace and learn AI themselves, setting the tone for the organization