Opinion: Many employers still mistake credentials for capability. Entrepreneur Sabri Suby argues that as AI makes information more accessible, problem-solving has become more important than which university you attended
One of the biggest hiring mistakes businesses still make is confusing credentials with capability. We’ve all been there: you hired someone impressive on paper – great university, solid qualifications – but six months later, they’re struggling.
Why? Often it’s because the most capable employees aren’t necessarily the most qualified; they’re the people who can solve problems, adapt to change, and make decisions when there isn’t a clear answer.
This gap is becoming even more apparent with the proliferation of AI. Gaining knowledge used to be expensive. A degree meant that you had information that not everyone could access. Tools like ChatGPT and Claude have completely collapsed the scarcity that, to be frank, universities were built on.
Now, AI gives almost everyone access to the same information, so the advantage is no longer what you know, but how you think. And the gap between qualifications and performance is widening, because the skills that degrees traditionally signalled are now largely commoditised.
Qualifications, tenure and polished deliverables are becoming weaker indicators of future performance. So, what becomes more valuable in this environment?
What I’m looking for in new hires
Inside my own business, the shift in what drives performance is clear. AI has already taken over many routine tasks in knowledge-work environments, from drafting content and analysing spreadsheets to creating SOPs, forecasting cash flow and synthesising large volumes of information.
So these are no longer strong signals of capability. If a candidate can produce a polished deliverable because they know how to prompt an AI model effectively, that’s a useful skill to have. But it doesn’t tell me how they’ll perform when the output they receive contradicts the narrative.
What matters more now is something much harder to measure: judgement, adaptability, problem-solving and independent thinking.
If I had to pick one skill above all others, it would be problem-solving. There is almost no problem today that can’t be solved with resourcefulness, critical thinking and access to the right AI tools.
I’m far less interested in where someone studied than whether they can figure things out. AI can teach you how to do almost anything. What it can’t teach is the curiosity and initiative needed to ask the right questions and find the answer.
This mindset now influences how I evaluate candidates. If someone submits a generic AI-generated cover letter or appears to be reading answers from AI during a video interview, it’s an immediate red flag.
The issue isn’t that they’re using AI. The issue is that they’re outsourcing their thinking. The candidates who stand out use AI as a tool while still demonstrating original thought, judgement and personality.
What top performers are doing differently
The pattern I keep seeing, both within my business and across businesses I work with, is that the highest performers are using AI to move faster on tactical tasks, while staying deeply engaged in the strategic stuff. They’re using AI to amplify their judgment, as an adjunct, rather than replace it.
Today, someone can upload hundreds of call transcripts or operational reports and quickly identify recurring themes, blind spots and opportunities for improvement. Tasks that once required teams of analysts can now be completed in a fraction of the time.
The difference is in how that information is used. Average performers ask AI to tell them what to do. Top performers use AI to challenge their thinking. They’ll develop a strategy first, then ask AI to critique it, identify blind spots, highlight second-order consequences or argue the opposite case. AI becomes a thought partner rather than a substitute for judgement.
The opposite approach is becoming increasingly common as well. Some professionals rely on AI to generate plans, recommendations and content without properly interrogating the output. The result is often generic work that looks polished on the surface but lacks original thinking. In many cases, the problem isn’t poor AI output; it’s that the person never applied their own judgement to begin with.
Top performers want to understand the underlying dynamics of a problem and then produce the deliverable. They’re comfortable making calls with incomplete information and adapting as they go.
The practical takeaway
Traditional education still matters. Great universities develop important foundational skills, not just for work, but also for life: structured thinking, exposure to diverse ideas, teamwork and human connections. What we need to look at more closely is hiring culture that confuses credentials with capability and doesn’t stop to ask, what drives performance?
We need to build hiring processes that test for adaptability and problem-solving, moving beyond the standard PDs of degree, tenure and job title, and create environments where independent judgment is rewarded.
For educators, the challenge is designing modern courses that continue to develop critical thinking, perspective and adaptability, alongside subject knowledge. For professionals, the shift might be the easiest one to implement. Learn how AI applies to your role, and build stronger judgment about when to trust the output.
AI will continue to make information cheaper and faster to access. What it won’t do is make decisions for you. The employers who learn to use AI as a tool while continuing to develop the judgement, curiosity, and problem-solving abilities that technology can’t replicate will be the ones to watch.
Sabri Suby is the founder of Australia’s fastest-growing digital marketing agency, King Kong. He is the author of international bestseller Sell Like Crazy, which has sold more than a million copies , and is a former investor on TV show Shark Tank Australia.
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