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AI is no longer a question of if, but how fast and how far, in Wealth Management. AI is past the skepticism and real value is being delivered — especially for firms that prioritize wisely and execute deliberately. Unlike some emerging technologies that fizzled (think blockchain or the metaverse), AI is already improving advisor productivity and reshaping how firms operate.
For Wealth Tech leaders navigating this shift, the challenge is no longer belief — it’s prioritization and execution. What use cases come first? How to demonstrate value early? What tech, talent, and vendor strategies will actually scale?
This article lays out three strategic paths Wealth firms can take, depending on their AI appetite and business goals.
1. Fast Follower Strategy: Start with Advisor Efficiency
For firms not ready to bet big, but unwilling to be left behind.
Many firms — large and boutique alike — have the explicit technology strategy to be fast followers in the marketplace, thus minimizing the pressure to innovate and also managing execution risk, while quickly adopting proven tools and capturing meaningful gains. Natural language searches of internal data, drafts of simple client communications like Thank You notes, note taking apps that make call report filing a breeze, and client-ready summaries of published and proprietary thought leadership articles would fall into this category.
Firms like JPMorgan and Morgan Stanley have already launched very impressive co-pilot/ AI assistants to help advisors search internal data to find answers quickly and write simple client communication. Note taking apps have exploded over the last 12-18 months with a crowded vendor space from Jump AI to Zocks to Zeplyn -- each touting superior features that work across in-person and virtual meetings, and that accurately track tone, sentiment, and next steps.
2. Leverage AI to Deepen Impact: Growth and User Experience Strategy
For firms ready to commit more extensively and align AI with business strategy.
This tier of AI won’t just save time — it will strengthen trust, reputation, and differentiation. In my opinion, this is where you start seeing more value-added outcomes from AI, as firms gain confidence with the simpler use cases and are ready to commit to applications more integrated with daily workflows and experiences. These would include using AI to drive tailored recommendations, proactive outreach, and personalized journeys.
Peer benchmarking, next best action, self-serve for online statements, prospecting, trust distributions are some examples that come to mind. Generally highly opaque in the world of Wealth Management, but top of mind for most clients – what would others like me do in my situation? Benchmarking clients across similar AUM, age, life stage, goals, location, etc. can help make meaningful recommendations on Next Best Action, such as a Line of Credit for liquidity, a Donor Advised Fund for philanthropy, or SLATs/ GRATs to take advantage of asset appreciation.
Another such value-added AI use case would be taking note takers much further from merely capturing the synopsis of the meeting, to feeding tasks to the CRM, assigning action items to different team members, keeping the client informed about progress, and completing the circle by scheduling the next meeting and informing the agenda for that meeting.
3. Innovation Strategy: Use AI to Democratize Wealth Services
For firms with a bold vision to scale advice without sacrificing value.
AI could meaningfully contribute to the democratization of advice, planning, and investment management, by supporting scalability of services that are today quite manual and therefore high-cost and only accessible to the wealthiest segments. This would require rethinking how these services are delivered — scalable intelligence coupled with behavioral coaching from human advisors. This tier of AI has the potential to make high-quality wealth guidance radically more accessible and completely transform the Wealth Management business.
Some examples of tools in this category would be portfolio management alerts when asset allocation is meaningfully impacted (not just small drifts) with actionable recommendations, planning prompts that flag liquidity gaps or blind spots (have you planned for all lifestyle expenses, even the infrequent ones), auto-generation of trust documents based on minimal inputs, and effective tax planning. Many such applications are already starting to emerge (e.g., Wealth.com, Holistiplan, RightCapital), and widespread adoption will likely accelerate once some of the industry giants like Schwab and Vanguard adopt and advocate for them.
Don’t Just Plug in AI. Rethink the Model.
While AI tools can bring exciting opportunities and impact, any writeup on these would be incomplete without highlighting the prerequisites for any successful use case implementation.
Data integrity is non-negotiable.
Can your AI tools trust the data? Is it consistent across systems? Are there processes in place to maintain and continually enhance quality?
Strong cross functional collaboration will drive success.
AI projects sit at the intersection of technology, business strategy, data, risk and compliance, and operations. It is critical that these teams align on use cases and approach.
Explainability is a must for compliance and adoption.
If advisors can’t explain what the AI is doing, clients won’t trust it, and advisors won’t use it — and your compliance team won’t approve it.
Effective change management is foundational.
As for any new tool, education and training – for both clients and advisors – will be needed to evangelize the benefits, demonstrate ease of use, and alleviate concerns.
Supervised AI will win — at least for now.
Given the potential for current AI models to hallucinate, supervised implementations may be best. How will you ensure there is human supervision built in? Think augmentation, not automation. Human-AI collaboration is where real value lives.
Firms that scale in the next era of Wealth Management won’t just adopt AI. They’ll reimagine how technology and talent come together to create lasting value.
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