Latest / Hiring & Firing with Fexingo: Recruitment, Performance, and Workforce Decisions / Why Hiring for Potential Beats Hiring for Experience
Transcript
- Lucas: There's this assumption baked into most hiring — that the best predictor of future performance is past performance in a similar role. And on its face, that seems logical. But there's a growing body of evidence that it's actually a pretty weak signal, especially when the role itself is evolving. Luna: You're talking about hiring for potential versus hiring for experience, right? I've seen the phrase thrown around a lot, but I'm not sure most teams actually know how to operationalize it. Lucas: Exactly. And it's not just a LinkedIn hot take. There's real data now. The Society for Human Resource Management — SHRM — published a study in late 2025 that tracked about 2,400 hires across multiple industries over 18 months. They split the group into two buckets: people hired primarily because they had direct, relevant experience, and people hired because the interviewers saw high learning agility and curiosity — even if their resumes weren't a perfect match. Luna: What did they find? Lucas: The hires made for potential had 12 percent higher retention over that period. And they reached full competency roughly 8 percent faster than the experience-based hires. Which is counterintuitive — you'd think someone who's done the exact job before would ramp faster, but apparently that advantage fades quickly. Luna: And before we go deeper — Lucas, I know these conversations take real work to research and produce. If listeners find value in them, a couple of dollars a month at buy me a coffee dot com slash fexingo genuinely keeps this show ad-free and independent. That's the whole engine. Lucas: Yeah, it really does. And it's not about big pledges. Even a few contributions add up and let us keep digging into studies like this one. So if these episodes have changed how you think about hiring even a little, that link is the best way to keep them coming. Luna: Alright. Back to the data — what made the potential hires faster to competency? That seems surprising. Lucas: The researchers think it's because the potential hires came in with a learning mindset. They asked more questions, sought feedback aggressively, and adapted to the company's specific processes rather than trying to replicate what worked at their old job. The experienced hires, by contrast, sometimes brought bad habits or assumed they already knew the right way. Luna: So experience can actually be a liability if it comes with rigidity. Lucas: That's the risk. The study's authors called it 'the curse of expertise' in fast-changing environments. If your industry or role is relatively stable — say, a regulatory compliance officer doing the same quarterly filings — then experience probably wins. But in marketing, tech, product management, any role where the tools and strategies shift every couple of years, potential starts to outperform. Luna: Let me push back a little. I've seen companies swing too far the other way — hiring for 'culture fit' or 'vibe' and ending up with people who are charming but can't do the job. How do you assess potential without just falling for a good interview performance? Lucas: That's the critical question. And the SHRM study actually identified three specific signals that correlate with high potential: learning agility — which is the speed at which someone can pick up a new domain; cognitive flexibility — how well they handle ambiguous problems; and what they called 'productive persistence' — basically grit with a feedback loop, not just stubbornness. Luna: Those are all pretty abstract. How do you test for them in a 45-minute interview? Lucas: One technique that's gaining traction is the 'novel problem interview.' Instead of asking someone to walk through their past accomplishments — which is just a storytelling exercise — you give them a problem they've never seen before, something from a different industry or a completely fictional scenario, and watch how they approach it. Do they ask clarifying questions? Do they propose a hypothesis and test it? Or do they freeze because there's no playbook? Luna: I've heard Google used to do something like that with their puzzle questions. But they dropped them because they didn't predict performance. Lucas: Right, the old 'why are manhole covers round' type questions. Those were gimmicks. The novel problem approach is different — it's not about a clever answer, it's about process. For example, you might ask a marketing candidate to design a launch strategy for a product that doesn't exist in a market they've never worked in. You're not testing their knowledge; you're testing their thinking architecture. Luna: So the key is that the problem is genuinely unfamiliar, not just tricky. Lucas: Exactly. And you pair that with what's called a 'learning story' interview. Instead of asking 'tell me about a time you succeeded,' you ask 'tell me about a time you had to learn something completely new in under a month — and how you structured that learning.' That's a much better predictor of potential than any credential. Luna: I can see that working. But there's a practical problem: most hiring managers aren't trained to evaluate learning agility. They default to 'has this person done this exact thing before?' because it's easier. Lucas: That's the biggest barrier. The SHRM study noted that even when companies say they value potential, their interview rubrics still weight experience heavily. They found that only about 15 percent of the organizations they studied actually had a structured process for assessing learning agility. The rest just hoped it would show up. Luna: So how do you fix that? Do you redesign your scorecards? Lucas: You have to. One company that did this well is a mid-size software firm called Everbridge — they revamped their hiring process in 2024. They removed the 'years of experience' requirement entirely from their job descriptions and replaced it with a short online assessment that measured problem-solving in unfamiliar domains. They found that their hires from non-traditional backgrounds performed as well as industry veterans after six months, and turnover dropped by 20 percent. Luna: That's a concrete example. But I imagine some roles still need deep experience. You wouldn't hire a neurosurgeon based on learning agility. Lucas: Of course not. The high-stakes, high-specialization roles are the exception. The argument is that for the majority of professional roles — especially in business, tech, and marketing — the half-life of specific knowledge is shrinking. What you knew about SEO two years ago might be obsolete. So hiring for the ability to learn has a longer shelf life than hiring for a static skill set. Luna: Let's talk about the risk side. You mentioned companies that over-index on potential and end up with underqualified hires. What does that look like? Lucas: It usually happens when the organization has no baseline competency filter. The most effective approach I've seen uses a two-gate system: first, a minimal threshold of relevant skills — you need to be able to do the job on day one at a basic level. Then, once that's cleared, the decision is driven by potential signals. If you skip the first gate, you end up with people who are eager but can't execute. The data shows that works about 60 percent of the time, which isn't good enough. Luna: So it's not either-or. It's a sequence. Lucas: Exactly. Competency is the floor. Potential is the ceiling. You need both, but most hiring processes only look at the floor and mistake it for the ceiling. Luna: One thing I've noticed is that hiring for potential tends to favor younger candidates, because they have less experience to evaluate. How do you avoid age discrimination? Lucas: That's a really important point. The data actually shows that older workers can have high learning agility too — it's not an age-linked trait. But interviewers have unconscious bias. The fix is to use structured, skills-based assessments rather than relying on the interviewer's gut feeling. When you standardize the novel problem exercise, age doesn't predict performance on it. Luna: So the assessment itself needs to be blind to demographics. Lucas: Right. And that's where ai driven assessments can help — if they're built carefully. Some companies are using natural language processing to analyze how a candidate works through a problem, looking for patterns like question-asking frequency and hypothesis generation, not keywords from their resume. But you have to audit those tools for bias too. Luna: If a company wants to start moving toward potential-based hiring, what's the single most impactful change they can make this quarter? Lucas: I'd say remove the 'years of experience' requirement from your job descriptions. It's the single biggest lazy filter. Studies show it screens out many high-potential candidates while not actually improving hire quality. Then, replace it with a short, realistic job preview — a task that simulates the actual work. That gives you a much better signal of who can do the job and who can grow. Luna: And don't forget to train your interviewers to recognize learning behaviors rather than just nodding at big-name past employers. Lucas: Exactly. Because if your hiring managers are still asking 'where did you work before?' and leaning back, you haven't changed anything. The shift has to happen in the room. Luna: Lucas, any closing thought on this? I'm curious whether you think potential-based hiring will become the norm or remain a niche approach. Lucas: I think it becomes the norm in high-change sectors within five years. The ROI is too clear: better retention, faster ramp, broader talent pools. The companies that figure it out first will have a real advantage in hiring markets that are still tight. The ones that keep leaning on 'five years of experience required' will struggle to fill roles and wonder why their new hires don't adapt. Luna: That's the kind of edge most businesses can't afford to ignore. Lucas: Exactly. Thanks for listening, everyone. We'll be back next week with another angle you can actually use.