Wealth management firms looking to scale with AI need more than a handful of enthusiastic early adopters. At an ADVISE AI discussion, Pax Financial Group President Janise Brooks and Highvault Consulting CEO David Quisenberry described an approach built on clear direction from leadership, employee-led use cases, training and small-scale experimentation.
The stakes are practical: Deloitte says advisors spend more than 70% of their time on activities unrelated to client service, and AI could free up as much as 50% of that time. That could give advisors more room to serve existing clients, bring in new ones and support firm growth. The session, moderated by Charles Schwab Director of AI Consulting Adam Moseley, focused on getting employees involved without making them feel threatened by the technology.
Make AI tools available across the firm
Pax provides Copilot to every employee and expects staff to reach a basic level of proficiency, Brooks said. Broad adoption cannot depend on a small group of internal champions. Employees can start with their own work needs, then look for ways to apply AI to advisor workflows.
Quisenberry likewise cautioned against concentrating expertise in a few people. Highvault’s experience is that tools need to be accessible across the team. Some employees may take time to get comfortable with them, he said, but that is not a reason to restrict AI use to a select few.
For advisory firms, firmwide access also means accommodating different roles and levels of experience. Opening accounts is only a first step; firms need baseline expectations and ongoing support if deployment is to move beyond a limited trial.
Leadership sets the goal; staff find the use cases
Leaders should explain what the firm wants AI to accomplish and what employees are expected to learn, Brooks said. But the most useful applications often come from frontline staff. In her words, direction comes from the top, while implementation needs to build from the bottom up.
Quisenberry said the involvement of the CEO and senior managers can shape how seriously employees take an AI initiative. Visible support can encourage staff to share ideas and help bring the right people into the conversation. If leaders are absent, employees may be less inclined to attend meetings, weakening the effort.
That does not mean managers should choose every use case. Leaders can set goals and provide resources, while employees identify the time-consuming, repetitive or frustrating steps in their daily work. Bringing those perspectives together helps turn AI projects into practical changes.
Start with basic training and simple applications
When Pax began integrating AI, it gave all employees an hour of foundational training, including an introduction to prompting. Workshops followed, showing staff how to build simple agents. After the sessions, some employees began designing tools on their own. One advisor built an agent for a nonprofit where they volunteer, and another employee then started experimenting as well.
Quisenberry suggested holding focus groups to gather tasks employees want AI to handle, then having the team select projects suitable for early development. The firm can help build an initial set of applications and later train employees to develop tools themselves. Starting with real work needs can also keep training connected to employees’ day-to-day responsibilities.
Build confidence with measurable time savings
Employee pain points tend to surface as AI adoption grows, Brooks said, and addressing them is important to winning support. She recommended starting small and treating incremental results as progress rather than expecting staff to overhaul their work from the outset.
Pax holds quarterly all-hands meetings where colleagues share examples of AI use. Brooks said one employee reported that a task that used to take four hours could now be completed in three minutes. The firm also tracks use of its AI tools and gives its most active users an “Iron Man Award.”
Some of the strongest users have been unexpected. Brooks recalled that an employee who was initially quiet and analytical later became an AI enthusiast. Firms may not know in advance who will drive adoption, she said, so employees across the organization need the opportunity to try the tools and put forward ideas. The approach does not have to begin with the most complex applications, but teams need to start and keep building from there.