As AI takes on junior-level work, CFOs are faced with the challenge of developing talent in their teams. Brian Beaupre, CFO of Teikametrics, has firsthand experience with this issue. For the first 10 years of his career, he built financial models in Excel and learned finance by developing the numbers behind major business decisions.
Beaupre notes that AI tools are now doing what he did for the first 10 years of his career. In a June interview, he advised younger finance professionals to get comfortable with AI and understand the data feeding it. However, he is now thinking about what happens when these tools do too much of the learning for them.
Developing Judgment
Beaupre’s concern is whether new graduates will develop the judgment needed to know when an answer is wrong. He believes that AI fluency is on its way to becoming a standard qualification for new graduates, but judgment is exactly what can’t be automated. “That struggle is where judgment comes from, and judgment is exactly what you can’t automate,” Beaupre said.
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Beaupre’s approach to evaluating candidates has changed as a result. He spends less time testing technical proficiency during interviews and instead asks them to describe a moment of adversity or a problem they faced without a clear answer. He believes that how someone tells that story and how self-aware and honest they are about what they got wrong along the way tells him more about their potential than any technical screen.
Jeff Seibert, co-founder of Digits, an AI-powered accounting platform, agrees that new accountants need to learn to make difficult judgment calls. He notes that accounting work includes decisions that cannot be resolved by following a clear rule every time and that folks who understand the space, complexities, and have the experience to make those calls are essential.
Seibert’s description fits the skills Beaupre wants to find in an interview. A candidate who can explain how they handled uncertainty may offer more insight into their potential than one who can complete a technical exercise. Beaupre described the risk that leaning on AI too early in a career erodes the muscle of critical thinking that only gets built by struggling with a problem, getting it wrong, re-learning it, and eventually solving it yourself.
For Seibert, the human role also comes down to responsibility for the result. “The AI can never take accountability,” he said. An accountant still has to build a relationship with the business owner and stand behind the numbers they provide. Beaupre is investing in AI and automation training for tenured employees at Teikametrics, but he notes that the approach varies because established staff have different ways of learning and different reasons for trusting, or questioning, a tool’s output.
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Retaining Knowledge
Beaupre wants to retain the knowledge and habits of his existing staff. Pairing them with newer hires who are comfortable using AI could help each group learn from the other. Experienced staff can show how they reach a decision, and new employees can help them work with tools that are becoming part of the job. However, the arrangement depends on balancing learning with getting work done and all the risks associated with that.
If an employee sees only the finished AI-generated model, they may miss the assumptions that made it useful or led it astray. A senior colleague who explains those choices can give the new hire a way to assess the next answer for themselves. Beaupre believes that the differentiator is building people who can recognize a problem worth solving, stay humble about what they don’t know, and earn the trust of the people around them as a real business partner. The value of that experience is already apparent in hiring discussions, with senior accountants being the most difficult role to fill.
According to Blake Oliver, host of The Accounting Podcast, staff accountants are deciding to leave the profession after a couple of years, which is why it’s hard to find seniors. AI may make that experienced layer more valuable if firms need senior employees to direct automated work and review the results.
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As Oliver put it, “Recruiting is now as important as business development” for an accounting firm whose work still depends on people, even as it uses AI. At Teikametrics, Beaupre is finding that some employees need repeated practice with AI tools, while others need to see them applied to a problem they already understand, and Bennett Thrasher is one accounting firm that has kept its entry-level hiring in place, despite the challenges posed by AI.
Building Trust
Blake Oliver discussed evidence that companies continue to hire at the start of the career ladder. Accounting hiring for recent grads is flat, while hiring for many other white-collar jobs has declined. Bennett Thrasher is an accounting firm that has kept its entry-level hiring in place.
At Teikametrics, Beaupre is finding that some employees need repeated practice with AI tools, while others need to see them applied to a problem they already understand. The AI and robotic process automation tools are the easy part, Beaupre said, emphasizing the importance of building people who can recognize a problem worth solving.
