The Productivity Paradox: Why AI Is Making Advisors Busier

According to Kevin Hughes, president of financial planning at Advyzon, “At the task level, AI is clearly giving time back.” Hughes recognizes several productive use cases for these tools, such as extracting key data, generating insights, and interpreting tax statements and documents.

“However, at the workflow level, the picture is more nuanced,” continues Hughes. “In most cases, [AI] tools are still operating as an additional layer in the tech stack rather than being integrated into core systems,” which he believes often adds extra steps for advisors.

“AI tools are the spreadsheets of tomorrow,” explains Trevor Coyle, product manager at Libretto. “In the right hands, they can save time and scale workflows, but applied to the wrong problem, they can slow you down. We’re still early in that learning curve. The biggest productivity gains will likely come as AI is embedded into the tools advisors already use.”

As it currently stands, John O’Connell, founder and CEO of The Oasis Group, believes that “At best, [AI tools] can reduce the overtime spent by advisory firm team members catching up on paperwork.”

Sean Brown, founder and CEO of YCharts, has a rather positive outlook on the matter, but believes that the user has a certain degree of control in the productivity level: “When properly leveraged, AI is absolutely giving time back. Our engineers are now producing nearly twice as much code, with quality, hours, and satisfaction all holding steady.”

“I would not characterize AI tools as giving time back,” says Henry Zelikovsky, founder and CEO of Softlab360. He feels that these tools increase user curiosity and engagement, so “as the length of time spent in these interactions becomes endless, we do not sense that we gained free time – we reused it.”

Reiterating the time-saving paradox is Sean Sandys, chief technology officer of Syntax Data, who believes, “If we think of AI primarily as a work-item accelerator, we quickly discover that acceleration alone does not create time; it creates capacity, and capacity tends to get filled.”

Raising Expectations and Expanding the Scope of Work

To understand the full impact of AI on advisor productivity, it’s important to look beyond speed alone. The more relevant question is whether efficiency gains are creating meaningful breathing room or simply raising expectations and expanding the scope of work.

Even as AI accelerates individual tasks, many advisors are not experiencing a reduction in workload. Instead, they are using that capacity to take on more work, which can lead to a more intense and compressed workday.

Aside from AI tools that solve consistent, specific pain points (such as AI notetakers), AI creates “more outputs to validate, more context to manage, and more back-and-forth between systems,” Hughes explains, referencing a phenomenon researchers dubbed intensification.

Coyle echoes a similar sentiment, noting that “When verification is time-intensive, AI can expand the workload rather than reduce it.” Even when AI is used for summarizing work, drafting client emails, or other menial tasks, the advisor is stuck reviewing CRM data, factchecking, and humanizing the work – which may take just as long as producing the first draft in some cases.

Intensified Time Leads to Burnout

Researchers split this AI-driven work intensification into three main forms: task expansion, blurred boundaries between work and non-work, and more multitasking. Each of these sounds great, in theory, but can quickly become unsustainable for employees.

Advisors are increasingly aware of these risks and, in some cases, are already taking steps to protect their teams and establish healthier boundaries.

Speaking from his personal observations, Brown predicts that this intensification of work will likely lead to widespread burnout: “AI can dramatically accelerate output, but without the right balance, it risks creating unsustainable expectations.” Brown spent a summer streamlining workflows to reduce loan processing time from a full day to about one hour, but found that “pushing the team into a constant sprint mode” had its fair share of consequences, where “productivity surged in the short term, but over time, people burned out. It reinforced that efficiency gains have to be sustainable.”

According to Sandys, “This is key to developing a sustainable process rather than managing an ever-increasing list of work items.” Namely, “The advisors who push back against [intensification and burnout] reexamine which tasks should exist at all, not just which ones can be done faster.”

AI Adoption Is Uneven Across Firms

“[AI] adoption is uneven. Some advisors are leaning in and building AI into their daily workflows, while others are still experimenting,” notices Hughes, who goes on to say that the rate at which AI is evolving adds an extra layer of complexity for many advisors — making it challenging for firms to keep up while maintaining consistency and control.

Advisors and staff are adapting to the use of AI at different speeds, which makes it difficult for firms to establish consistent policies and practices.

Also observing discrepancies in how advisors are implementing these tools is Sandys: “Advisors' experiences with AI adoption span a wide spectrum. Many are still developing a deeper understanding of how AI fits into their day-to-day workflows, while others are starting to see how AI is changing the nature of their business.”

Fragmentation is a huge challenge that Hughes identifies, noting that “Advisors are often using AI within point solutions that only see a subset of client information.” He points out obvious discrepancies of a tool that generates insights without CRM context, or a tax system without portfolio holdings or tax lots data.

Thoughtful Implementation Makes the Difference

Firms that take the time to evaluate use cases, test outcomes, and define how AI fits into existing workflows are better positioned to realize meaningful benefits while avoiding unnecessary complexity. Having a structured and intentional implementation process is proving useful for many advisors so that they do not feel so overwhelmed with all the new AI tools.

“Our experience with using AI in software development has been paced and gradual,” explains Zelikovsky. “We did not rush to gain productivity. We set aside time to learn about AI benefits in our work, to compare and contrast the quality of the AI results across tools, and to determine what we can delegate to AI.”

Zelikovsky uses AI for reasons other than trying to frantically save time: “We practice using AI as a conversation partner, an intelligent engineer with whom ideas can be discussed and tried out. Doing more in the same period, on a mass scale, is not what we want to achieve.”

Coyle outlines three steps. First, “start by designating an AI champion responsible for evaluating use cases and integrating AI into advisor workflows.” The second is to “distinguish between AI-builders and AI-users – those who design workflows versus those who use them.” Third, “avoid AI FOMO. Many advisor tech tools are rapidly embedding AI, so firms don’t need to solve every problem themselves.”

According to Hughes, “Firms need to shift their focus from experimenting with AI tools to rethinking how work actually gets done. The biggest gains won’t come from layering AI across isolated tasks, but from embedding it into complete advisor workflows,” which he says starts with evaluating if their tech stack is prepared to support AI and allow it “access to the full breadth of client data needed to produce accurate, actionable insights.”

How Firms Are Keeping It in Check

Implementing AI tools is one challenge. Establishing clear expectations for how those tools are used is another.

Firms that place greater emphasis on education, accountability, and governance are experiencing greater success. This includes reinforcing that humans remain responsible for AI-generated outputs, setting usage guidelines, and helping teams understand both the benefits and risks of these tools.

Brown explains the two-pronged approach YCharts is taking: “First, we’re clear that accountability stays with the individual,” so blaming mistakes on AI is unacceptable. “Second, we prioritize education. Similar to how we try to teach our kids about the pitfalls of social media, we help our team understand the risks of AI overuse, from ‘work slop’ to losing a sense of ownership. Fortunately, our team tends to absorb those lessons a bit better than my teenagers.”

Additionally, it’s important for firms to set and achieve goals, explains Zelikovsky. Specifically, “goals that, when reached, create new conversations among people, raise awareness, yield ideas that spawn proof of concepts, and drive results at a faster pace with less effort are what keeps the unruly use of AI in check.”

Recommendations From the Experts

Many of our clients at Impact Communications are highly involved in AI research, implementation, and innovation. Many are on the ground experiencing these tools firsthand, both in their own practices and while working with others. They’re speaking at industry events, such as AI University and the T3 Technology Conference, and positioning themselves as leaders on the topic.

O’Connell recommends first understanding the cost of administrative tasks by “taking the fully loaded costs (salary and benefits) of the team member completing that tasks, divide by 2080 billable hours in a year, multiply that hourly rate by the amount of time to complete administrative tasks, and you have a baseline cost for administrative tasks.”

“Then use AI to reduce the time to complete the same tasks. Measure the time for the human in the loop, multiply that by the fully loaded hourly rate, and you have the new task cost. Subtract this new cost from the original and you have a savings in hours and dollars. Then you can account for the cost for the AI tool and you have a savings to the firm.”

According to Hughes, “Firms need a more unified architecture.” Ideally, with “a centralized data model, connected workflows, and an execution layer where AI can operate with full context and appropriate controls.”

He understands that unification is where integrated platforms have a structural advantage: “By bringing together CRM, portfolio management, planning, reporting, and workflows in a single environment, AI can move beyond assisting with isolated tasks and begin performing meaningful work on behalf of the advisor in a way that is consistent, auditable, and actually reduces workload.”

Zelikovsky believes “we should use [AI] wisely within our personal and professional spheres. We can still control our pace. It is to our benefit to control it, to order it, to use the momentum gained to think rather than to run to the finish line.”

Taking a Disciplined Approach

So, is AI giving advisors time back or simply asking more of them? As with most things in wealth management, the answer depends on how thoughtfully these tools are implemented and integrated into advisors’ broader workflows.

The opportunity is real, but so is the risk of unintended complexity. Firms that take a disciplined approach by embedding AI into existing systems, setting clear expectations, and maintaining human oversight will be best positioned to realize its benefits. Ultimately, AI should serve as an enhancer of advisor capacity and client experience — not a quiet driver of burnout.

Jonny Swift is president of Impact Communications, a full-service PR and marketing firm serving a select group of independent financial advisors, WealthTech firms, and allied institutions in the financial services industry.

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