Artificial intelligence is doing something no one fully anticipated: it is eroding the traditional on-ramp into financial advice. The entry-level analyst and paraplanner work that once served as the training ground for new advisors — data gathering, analysis, report assembly, follow-up documentation — is increasingly being automated. That shift is arriving precisely as the industry faces what McKinsey & Company, in a February 2025 report, described as a potential shortage of roughly 100,000 advisors by 2034, driven by advisor retirements outpacing new entrants at a time of surging client demand.
The question facing firms is not whether AI will take on more of the analytical workload — that outcome is already in motion. The question is whether the next generation of advisors will still develop the judgment, communication skills, and client intuition that no amount of AI-generated output can substitute for. Four practitioners share how they are navigating that challenge now.
Joseph Conroy, founder of Harford Retirement Planners, a Bel Air, Maryland-based independent advisory practice affiliated with Private Advisor Group, takes an approach he compares to a medical residency. New advisors shadow established ones, following workflows, sitting in on client meetings, taking notes, and drafting follow-up summaries and emails from scratch.
"We pretend AI doesn't exist in the early learning stages of a new advisor," Conroy said. "They 'do things by hand' and once they master it, then AI can start to reclaim their time back."
The rationale is not nostalgia for manual work. It is about understanding the reasoning behind a recommendation before being allowed to delegate its assembly. AI has replaced much of the need for those foundational tasks, Conroy acknowledges — but those tasks are still how new advisors learn the "why" behind workflows and communication style.
The one task Conroy will not allow new advisors to hand to AI before demonstrating they can do it themselves: client email communications. The advisory business runs on trust and relationship, and clients can still detect when a message has been copy-pasted from a language model.
"We do not want to compromise the value in our recommendations with the distracting thought of 'did my advisor send this or did they just get lazy and have AI type this email?'" Conroy said. "To remove that potential distraction from the real valuable advice, we want to make sure clients know they are communicating with us and not a chatbot."
Chris McMahon, founder and CEO of MFA Wealth and Aquinas Wealth Advisors, sees a specific trap in relying on AI before a new advisor has developed their own interviewing instincts.
His concern is that AI makes recommendations exclusively on the hard data presented to it. A client who says they are comfortable leaving a surviving spouse with $50,000 per year will receive a plan built around that number — unless the advisor has been trained to probe further.
"An experienced advisor would be able to ask more probing questions and hold a mirror up to the client," McMahon said. In his example, the right follow-up question — noting that the couple spent $23,000 on travel last year and had set a goal of visiting one new state per year with their family — typically produces a very different answer. The client realizes the original figure does not reflect their actual goals, and the income replacement number moves to $100,000. Only then, McMahon says, should AI come in to build the precision plan around those goals.
"Without the teaching, mostly observing these interviews with experienced advisors, this skill set won't develop, and the next generation of planners will fall short of their mandate of helping people achieve their true goals," McMahon said.
On the timeline question, McMahon believes the traditional three-to-five-year analyst track is not disappearing — it is compressing. He now structures his junior advisors' days so that half is spent collaborating with seasoned advisors and half on analytical work. The result, in his view, is that the client-facing phase of a young advisor's career can begin in year two rather than year five.
Devon Klumb, director of sales at Betterment Advisor Solutions, the custodian and technology platform for independent RIAs based in New York City, makes a distinction that frames the whole debate differently: AI automating the assembly of analysis is not new. The profession has been offloading technical construction to software — amortization tables, tax projections, financial planning models — for decades. What is new is losing the incidental connection to the numbers that used to come along with doing that work.
"Not plugging in the inputs is different from not knowing them," Klumb said. "Even if a new advisor never builds the analysis from scratch, they still need to review the inputs and question the outputs."
His training approach moves the developmental work upstream rather than eliminating it. New advisors at his firm are placed in front of clients earlier than the traditional track, given a defined role in the meeting, and held accountable for assembling their own recommendations before those recommendations reach a client. The debrief then focuses on the client's reaction — what they hesitated on, what they moved past quickly — rather than the technical accuracy of the plan.
The task Klumb will not allow AI to touch until a new advisor has demonstrated they can do it unassisted is the discovery conversation: sitting with a client without AI tools and working out what that client actually wants their money to accomplish.
"What tells me if they are ready to do it without help is whether they can explain the resulting recommendation in plain language, without notes, and articulate why it fits that particular client," Klumb said. "If they cannot, more tooling only makes them faster at assembling advice that lacks personalization and conviction."
Jeff Gonyo, head of wealth management at Steward Partners, frames AI as an accelerant for training rather than a replacement for it — but insists the human development infrastructure must remain intact.
"Mentorship and team development are important factors developing real judgment and fosters accountability," Gonyo said. "The skill set has shifted from being investment based and transactional to a more comprehensive level of planning that requires FAs to know how to pull the complexities of one's life and wealth together in a coordinated plan."
Gonyo's firm is developing a proprietary AI platform called Steward Assist, designed to streamline the service model for mass-affluent clients while expanding the capacity of new advisors to manage more relationships simultaneously. The vision is an AI-augmented experience that remains fundamentally advisor-led.
He also pushes back on the idea that the analyst training track is being displaced. In his view, the entry-level path through client associate, analyst, or paraplanner roles remains the most effective way to build foundational business knowledge — AI will accelerate that experience, not replace it.
Before trusting a new advisor to use AI effectively, Gonyo requires them to have a fundamental working knowledge of how the business operates end to end — planning, investment principles, markets, and service models. Understanding those foundations, he argues, is also what makes it possible to develop the right AI prompts in the first place.
"We believe AI will help drive that experience to be faster and more efficient but not replace it," Gonyo said.
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