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Key Takeaways
- Many universities ban AI tools to protect academic integrity, but at work, they’re expected to leverage AI for speed, accuracy and innovation.
- To close the AI-readiness gap, leaders should audit their hiring lens, partner with higher education, upskill internally and model curiosity.
As a Generation Xer, some would say we were 30 years old when we were 12, and today, in our 50s, we’re still 30 years old. We matured fast, but many of us remain young at heart. We snowboard, watch superhero movies and tinker with new technology, especially AI (as a leader, I’m obsessed with figuring out how to integrate AI into our business to bring more value to clients and improve bottom-line results). Many of us also now have kids who are between late high school and early career.
When speaking with this remarkable young generation, it’s clear they face challenges. At work, they’re expected to leverage AI for speed, accuracy and innovation. Yet in school, many are told not to use AI, or risk serious consequences. This disconnect leaves them trained for one reality while graduating into another.
If you’ve ever welcomed an impressive new hire who can code, research and present — yet freezes the moment you ask them to prompt an AI model or integrate its output into a project — you know this isn’t just a recruiting hiccup. It’s a collision between two worlds: the academy still navigating boundaries, and the workplace already operating at AI speed.
And when your next great hire arrives unprepared for how work actually works, that gap becomes your problem. This is no longer just an education policy debate. It’s a talent and leadership-readiness challenge every entrepreneur must navigate.
Related: 3 Ways to Prepare Your Business for an AI Future
Two worlds moving at different speeds
Companies are revamping entire workflows around speed, insight and automation. Technologies that took years to adopt now rise in months. Gallup reports that the use of AI at work has doubled in just two years, from 2023 to 2025. Leaders aren’t waiting. They’re investing in teams that can use AI to sift data, draft strategy, prototype ideas and scale execution. The statement that “one does not lose their job to AI, but to the person that knows AI” is now true more than ever.
Meanwhile, universities largely stand firm on tradition. Many ban AI tools to protect academic integrity. Don’t use it for writing, design, coding, analysis, etc., “or else!”
Provost Dr. Dave Bolman at the University of Advancing Technology (UAT), which has long prided itself on marrying innovation and education, acknowledges the challenge. “Hesitancy in incorporating AI into university learning comes in part because the technology is new and not all students and faculty have skills in these evolving tools,” he says. “Inconsistent knowledge creates barriers to designing generative AI into learning experiences.”
A 2024 survey by the American Association of Colleges & Universities and Elon University supports this, revealing that one of the biggest obstacles to generative AI adoption in higher education is faculty unfamiliarity. One reason? Many tenured professors and long‑time academic leaders are rewarded for longevity and traditional paper‑publishing, so they may lack the incentives and training to embrace AI. Another possibility: When learners rely too early on generative AI without proper scaffolding, they may miss out on developing critical‑thinking and reasoning skills, which institutions worry about.
So, you have a talent pipeline giving you graduates who excel at traditional academic skills, but are not yet fluent in the tools your team now uses daily. The cost isn’t just time. It’s slower innovation, more re‑training and a competitive disadvantage.
Consider a hypothetical, but realistic, calculation: If a new hire with AI literacy delivers, say, 20% more output in their first six months compared with one who lacks it, and the average starting salary is $70,000, then the productivity gap could amount to roughly $14,000 in lost value (or more when factoring in ramp‑up time, training costs and missed opportunity).
The employer’s dilemma: Talent that can’t keep up
On the employer side, the ripple effects are already visible. Companies are adjusting onboarding programs, increasing internal training budgets and reconsidering how quickly new talent can contribute. Graduates may arrive with strong academic credentials but might lack fluency in AI workflows: prompting, evaluating outputs and integrating generative tools into real-time problem-solving.
And students know they’re behind. According to a 2024 survey from the Digital Education Council, 58% report lacking sufficient AI knowledge and skills, and nearly half don’t feel prepared for an AI-enabled workplace. The gap is real, and it’s shaping who gets hired, who gets promoted and which companies keep their edge.
This isn’t an isolated frustration. It’s a systemic disconnect. As Dr. Bolman explains, “Industries our students are graduating into have moved quickly to incorporate generative AI into their workflow. Expectations for complexity and efficiency are rising to a point where, for employees to be hired and retained, they must have AI abilities.”
That disconnect is redefining what “prepared” means in the modern workplace.
Related: The Surprising Strategy Smart Leaders Use to Outpace Disruption
What leaders can do now with the absence of universities taking fast action
To close the AI-readiness gap, leaders can act long before universities catch up. Here are four steps you can implement today to align talent with the way work actually happens:
1. Audit your hiring lens
Traditional job descriptions rarely reflect the reality of AI-driven work. Instead of focusing only on degrees or years of experience, evaluate candidates for their ability to use AI tools to explore ideas, identify patterns and make informed decisions.
Companies such as HubSpot now ask candidates to demonstrate how they use AI in their workflow because it reveals how quickly someone can adapt to the pace of modern work. In fact, one recent HubSpot job posting for a creative director mentioned the ability to proactively test and integrate AI to advance the team.
2. Partner with higher education
You don’t need a Fortune 500 budget to influence how students learn. Consider the capstone sponsorship program at Carnegie Mellon University’s Integrated Innovation Institute, which matches industry partners, including startups and small companies, with student teams to solve real business challenges as part of their curriculum.
Sponsors participate directly by mentoring students, defining problem statements, funding or supporting project work and attending final presentations. Programs such as this give students firsthand exposure to workplace expectations while helping employers shape a future talent pipeline that’s more aligned with real-world needs.
3. Upskill internally
Even the most ambitious graduates arrive with uneven AI experience, so companies benefit from building internal fluency themselves. According to the World Economic Forum’s global employer projections for the next five years, technological skills are expected to grow faster than any other skill category, with AI and big data ranked as the most rapidly rising skills.
In response, 85% of surveyed employers plan to adopt upskilling as their primary workforce strategy between 2025 and 2030. Prioritizing short, focused training that helps employees use AI in real tasks not only accelerates performance but also reduces the ramp-up time for new hires entering AI-enabled roles.
Related: 5 Essential Skills for Building AI-Ready Teams
4. Model curiosity
Teams take their cues from leadership. When founders openly explore new tools, test ideas and talk through what they’re learning, curiosity quickly becomes part of the culture. In my own experience, simply sharing how I experimented with a new AI workflow — what worked, what didn’t and how it changed my process — immediately encouraged others to try their own experiments. When leaders show they’re willing to learn in public, it signals that exploration isn’t just permitted but expected.
As a leader, you can’t wait for education to catch up. You have to lead the learning through mentorship, experimentation and courage. Your next great hire may arrive ready to talk theory, but they also need to know how work actually works.
The next generation is ready to work. Are you ready to teach them how?
