The finance hiring environment is shifting significantly because of artificial intelligence (AI), which now touches every stage of the job application process, from résumé writing to interview prep, and beyond.
As an editing and refinement tool, AI can assist job applicants in communicating their experience more effectively, polishing their résumés, and preparing more confidently for interviews. But when AI creates or exaggerates nonexistent experience, also called hallucinations, problems arise. “Where it gets tricky is when the tool stops editing and polishing and starts inventing new experience,” according to Linda Muneka, Vice President, North America, Management Resources Practice Group, Robert Half.
On this episode of the Reshaping Finance podcast, Muneka, who leads tactical execution and operation initiatives and go-to-market strategies for more than 90 offices across North America, shared how AI is changing the way accounting and finance professionals apply for jobs, the challenges this creates, and how to distinguish between AI-enhanced communication and genuine expertise.
Quantity over quality is a challenge in hiring
Generative AI has made it easier for candidates to mass apply for roles, which in turn is overwhelming hiring managers and extending recruitment timelines. A slower recruitment process can cause a host of issues for organizations, including losing top talent to faster-moving competitors. The challenge now is less about candidate quality and more about navigating a sea of polished, AI-assisted applications.
With many applications enhanced (and sometimes exaggerated) by AI, even the strongest, most qualified candidates get caught in slower, more rigorous vetting processes.
According to a Robert Half survey, 67% of HR leaders said reviewing AI-generated applications had slowed their hiring, with 20% reporting delays of over two weeks. In fast-paced finance teams, those delays can mean losing sought-after professionals.
Résumés still matter, but vetting is essential
A résumé has traditionally been a key indicator of capability and experience. With generative AI, résumés have become more polished and more similar, creating the need for hiring managers to place greater weight on interviews, follow-up questions, and evidence of real experience rather than relying on keyword-optimized applications.
As you study AI-assisted résumés, however, patterns become clearer over time. According to Muneka, AI-polished résumés almost never admit to a bad day or a bad week. “My advice to hiring managers when it comes to résumé vetting is to read the résumé as the list of claims you’re going to test against, and every bullet point is a future interview question.”
Details and the willingness to admit what didn’t go perfectly set genuine candidates apart. Instead of just reciting achievements, candidates may need to take practical assessments, participate in scenario-based interviews, and provide project portfolios.
Specificity is becoming a new signal of credibility. Employers increasingly look for concrete examples: project details, challenges encountered, mistakes made, and how candidates responded.
For example, instead of “I led ERP implementations,” what stands out is, “I owned GL workstreams in a NetSuite migration for 14 entities, and go-live slipped a month because intercompany mapping wasn’t ready, so I rebuilt the elimination rules.”
Revealing genuine expertise: The art of the follow-up questions
Follow-up questions are an important way to separate genuine expertise from polished AI-assisted responses. AI can help candidates rehearse standard questions, but “somebody who has lived it tends to get more specific every time you ask a follow-up question, while somebody who didn’t will get more and more general,” Muneka says.
According to Muneka, “It’s in the art of the follow-up question because the first answer can be rehearsed fairly easily and maybe with AI’s help, and that's fine, but you have to move on to the next question to get to that specificity … By the third question, you usually have a fairly good understanding of what the person in front of you represents.”
Hiring teams are now also testing follow-up questions with their own staff and focus on scenarios only a professional with direct experience could answer. This deliberate, scenario-based vetting is becoming the norm for high-performing teams, and helps ensure that a single hiring misstep doesn’t quietly turn one vacancy into several as top team members pick up the slack.
The future of accounting and finance recruiting
Recruitment is likely to move from “what you can say” to “what you can show.” In the next few years, hiring will focus more on work simulations, portfolios, and live problem-solving.
Finance professionals may soon be asked to show templates, checklists, or process improvements they’ve built. At the same time, AI fluency, ethics, and oversight capabilities will become increasingly important.
The skills that will gain value are judgment, ethics, clear communication, and the ability to interrogate AI-generated work, not just produce it and make something sound good. Organizations that balance efficiency with human discernment will ultimately win the race for talent.
For more actionable insights on hiring in the age of AI, listen to the full Reshaping Finance episode Can We Still Trust the Resume? Hiring in the Age of Generative AI and discover how you can adapt your hiring, or your job search, to the realities of AI.
Additional resources:
AI and Recruiting: How Finance and Accounting Leaders Can Reduce Hiring Risk
The Risks of AI-Generated Resumes — What Finance Leaders Should Know
Reimagining Performance: Incentive Design for Human-AI Collaboration
AI and the Changing Way of Working Part 1: How AI Redefines Management Accounting
AI in the Public Sector: A Force for Good? Or an Evolving Cautionary Tale?