Every wealth management firm is talking about AI - yet only a few are truly prepared to capitalize on it. Emigrant's Mark Bruno sits down with Addepar's Janeen France in this episode of RIA+ to discuss the foundational work required before AI can deliver meaningful value. Their conversation covers data strategy, operational readiness, leadership, and the cultural shifts that separate firms experimenting with AI from those building sustainable competitive advantages through smarter technology and better organizational execution.
Summary
- Data Readiness is Key: The main difference between firms that are prepared for AI and those just experimenting is data readiness and workflow documentation, not tool selection. Firms that skip foundational data infrastructure will face issues like 'garbage in, garbage out' when scaling AI. A clean, centralized data foundation is essential for AI to work effectively.
- Adoption Follows Workflow: Treating AI adoption as a training problem is a common mistake. Adoption follows the workflow, not announcements. To drive change, align on a clear problem statement, focus on people and processes first, then technology. Design AI to fit into existing workflows to reduce the 'swivel chair tax' of jumping between systems.
- Assign a Process Owner: A common failure is not assigning a specific person to own the process outcome. This person should deploy the workflow technology and ensure old behaviors stop. Success in the first 90 days is critical, and having a champion with high 'change EQ' helps win over teams by addressing fears and showing how AI gives time back.
- Reframe AI as Cognitive Offload: Advisors may see AI as a threat, but it should be reframed as a tool for cognitive offload, not headcount replacement. AI handles repeatable work, freeing advisors for higher-value relationship work. The robo-advisor example shows AI raises the bar for advice, but only humans deliver the trust and judgment clients need.
- AI Adoption Indicators: Addepar's AI, Addison, has seen over half of its 1,400 firms use it, with 4,000 users growing 30% month-over-month. It compresses meeting prep from 20 hours to minutes and automates alternatives data management. This shows AI is already delivering operational leverage and freeing up advisor time.
- Culture of Innovation: CEOs should model AI experimentation and give explicit permission to try new things. Start with low-stakes, high-repetition workflows to build confidence. Avoid mandating adoption with metrics, as it leads to box-checking. Instead, share visible wins and create dedicated innovation time, like hackathons, to encourage curiosity.
- Data as a Strategic Asset: Treat data infrastructure as a strategic asset class. Documenting processes and cleaning data is unglamorous but essential for AI. Firms with organized data are more attractive to investors and better positioned for succession. The 'slow down to speed up' approach, like Addepar's Databricks rebuild, enables rapid AI development.
- 12-Month AI Roadmap: For a strategic AI roadmap, first connect your data foundation into one governed place, then layer intelligence for answers, and finally move to automation and agents. Do this in order. Also, consider build versus buy: building custom AI has hidden costs and risks, so use enterprise platforms. Hire operational leadership 6-12 months ahead of growth to scale effectively.