After spending a decade helping shape talent strategy as a business professor and now working inside an AI-first organization, I see hiring very differently than I did even a few years ago. Managing people, evaluating fit and understanding how teams evolve have always been central to my work, but the pace at which expectations have shifted in the age of AI has been unlike anything I have seen before.
Across the past years, I have interviewed candidates in product, analytics, engineering, operations and strategy. One thing is clear: hiring the right people today is more challenging than it was even two years ago. And it is not because there are fewer applicants. It is because there is less alignment between what modern AI-driven organizations actually need and what traditional education, resumes and interviews were designed to evaluate. What follows is my firsthand perspective on why hiring has become more complex and how leaders can rethink their approach.
The talent mismatch: Plenty of applicants, not enough readiness
A major shift I continue to observe is the growing gap between traditional training and what AI-era roles expect. Many candidates come with strong conceptual knowledge but limited exposure to working with AI tools or navigating environments where systems evolve faster than processes can catch up.
The McKinsey State of AI report supports this shift, noting that AI adoption is advancing faster than most organizations can reskill their workforce.
I see the same thing in interviews. Candidates understand concepts, but applying them in fast-changing, ambiguous environments is where the gap reveals itself.
This is where mindset becomes a decisive factor. In AI-influenced settings, adaptability and curiosity matter as much as domain expertise. The professionals who thrive are the ones who experiment, explore and stay hands-on with new tools even when the roadmap is unclear. One question I often ask is simple but revealing: When technology shifts, how do you keep up? The strongest candidates talk about tinkering, testing and learning by doing.
The AI polish effect: Why evaluating true capability is harder
Generative AI has made it incredibly easy for people to present themselves as polished and prepared. With AI-drafted resumes, refined stories and rehearsed interview answers, candidates can sound more impressive than ever before. This creates what I call the AI polish effect. It blurs the boundary between genuine capability and AI-assisted presentation.
I have met candidates who articulate AI concepts eloquently yet struggle when asked to break down an unfamiliar problem independently. Because of this, the way I evaluate talent has evolved. I listen less for confidence and more for thinking patterns. I look for willingness to take things apart to understand them, comfort in admitting what they do not know, the ability to reason through new situations and initiative in exploring emerging tools.
This aligns strongly with insights from BCG’s “AI Is Outpacing Your Workforce Strategy”, which highlights that adaptive learning and experimentation are becoming key differentiators among high-performing talent. BCG’s broader perspective in “AI Is Changing Recruitment” reinforces the growing mismatch between traditional hiring practices and the realities of AI-powered organizations.
A revealing insight: Many professionals still misunderstand AI
Another theme that shows up consistently is how often candidates underestimate the influence AI will have on their roles. Many still view AI as an optional enhancement rather than a core part of future workflows.
Gallup’s “Manager Support Drives Employee Adoption” illustrates this gap clearly, showing that many employees who do not use AI believe it simply would not help their work, while only a small minority cite lack of access as the real barrier. Discomfort with change and uncertainty about how to use AI safely remain common.
This shows up clearly in interviews. Some candidates imagine AI will sit on the sidelines, while in reality it is reshaping speed, expectations and decision-making across every function. For CIOs and technology leaders, this insight matters. Hiring now involves evaluating not just technical fluency, but a candidate’s awareness of how AI will shape their daily work and their willingness to grow with it.
Why interview coherence matters more than ever
As roles evolve, alignment among interviewers has become essential. When one interviewer expects deep AI expertise and another values learning potential, feedback becomes misaligned and promising talent can slip through the cracks. A shared hiring philosophy is no longer a nice-to-have. It is becoming a significant competitive advantage.
Evolving expectations: What strong candidates want today
Across roles, high-performing candidates increasingly look for autonomy, clarity, speed and meaningful work. They want to contribute to something that matters and see results quickly.
Culture also plays a major role. Skilled talent hesitates when an organization feels rigid or unclear. Many candidates now ask more about decision-making, ways of working and team dynamics than they do about day-to-day tasks. They want to understand how work gets done, not just what the job description says.
This is consistent with Deloitte’s insights on AI’s impact on careers, which emphasize the growing importance of helping workers continually reinvent themselves to keep pace with technological change.
What CIOs can do next
As AI shifts the definition of talent readiness, CIOs are uniquely positioned to reshape hiring strategy. Three actions stand out:
1. Redefine what “qualified” means
Instead of filtering for exact tool proficiency, evaluate candidates on reasoning, adaptability and their ability to learn at the pace AI evolves. The most future-ready hires are often those who lean into ambiguity rather than avoid it.
2. Build interview teams that reflect the skills you want to hire
Ensure that interviewers understand the organization’s AI maturity level and the differentiators that matter most. When interview panels are aligned, candidates receive clearer signals and hiring decisions become more consistent.
3. Treat AI literacy as a cultural expectation, not a niche capability
Hiring candidates who acknowledge AI’s inevitability and who are willing to expand their workflows with it accelerates organizational readiness and reduces friction during transformation.
These actions help CIOs avoid the misalignment that slows down hiring and creates onboarding challenges later.
Looking ahead: Hiring for adaptability, not perfection
AI is evolving so quickly that nobody can be fully prepared for every tool or workflow. This makes adaptability one of the most valuable traits to evaluate. The candidates who stand out are the ones who say, “I haven’t done this yet, but I can figure it out.”
Ambiguity is now part of most modern job descriptions. Some people thrive in it. Others find it destabilizing. Identifying this early helps build teams that are naturally suited to AI-driven environments. Hiring well in this era means prioritizing adaptability, curiosity, experimentation, cultural clarity and coherent evaluation. Problem-solving now matters more than polish. Learning speed matters more than perfect experience.
Evolving how we hire
Hiring in the age of AI is more complex, not because talent is lacking, but because what organizations need has fundamentally changed. The professionals who succeed today are curious, adaptable and comfortable navigating ambiguity. They value culture, clarity and purpose. They flourish under leaders who understand the pace at which technology is reshaping work.
Adaptability is now as important as skill. Curiosity is just as valuable as capability.
If we want to build teams ready for the future, we must evolve how we hire in the present.
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Read More from This Article: Plenty of talent, too little readiness: How AI is forcing CIOs to rethink hiring
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