The pace of technological change is dismantling the idea that a degree or job title guarantees long-term relevance.
Formal education alone can’t keep up with the velocity of innovation, and static credentials or rigid roles are no longer enough, and as AI accelerates across the IT landscape, new graduates are entering the workforce with a growing sense that their formal education is already behind the curve.
A survey from Nexford University, which gathered responses from 597 recent college graduates, highlights the widening skills gap—and the professional anxiety—that comes with trying to build a career in an AI-driven economy.
According to the findings, 21% of recent graduates believe their degree is already outdated, given how quickly AI, automation, and data-driven technologies have reshaped employer expectations. An additional 22% say they would have chosen a different major had they understood the scale of AI’s impact on workforce needs.
This disconnect is now influencing how graduates approach the job search. The survey reveals that nearly two in five graduates lack confidence when applying for roles that mention AI or automation-related competencies.
Even more telling: one in four avoids applying to these jobs altogether, even though AI literacy is becoming foundational across IT, cybersecurity, analytics, cloud operations, and software development roles.
The impact is also financial, with nearly a quarter of recent graduates saying their lack of AI-related skills is limiting their earning potential, a concern that aligns with broader IT salary trends: roles that require AI fluency, machine learning fundamentals, or automation tooling consistently command higher compensation and faster career progression.
How Can Grads Keep Up?
Fadl Al Tarzi, CEO & co-founder at Nexford University, says when so many graduates already believe their degree is outdated, IT leaders should treat that as hard evidence that the skill cycle in technical roles has accelerated beyond what traditional education can keep up with.
“The takeaway is hard to ignore--too many traditional universities are still training for a pre-AI workforce,” he says. “That doesn’t mean degrees have lost their value. Rather, higher education must build durable fundamentals and, most importantly, the ability to learn quickly.”
Tarzi says however it does signal that the half-life of technical skills is shrinking fast, and relying on a degree alone as proof of job readiness is no longer realistic.
“IT leaders need to assume that even smart, motivated new hires are entering the workforce underprepared for AI-driven environments—and that continuous, employer-led learning is now a baseline requirement, not a bonus,” he says.
Greg Fuller, vice president of Skillsoft Codecademy Enterprise, says early-career professionals should focus on core capabilities that create adaptability and confidence in evolving technical environments.
These include:
+ Data literacy: Understanding how information moves and informs decisions
+ Automation fundamentals: Gaining comfort with scripting and workflow optimization
+ AI-assisted development: Learning to collaborate with AI tools responsibly while maintaining security and quality
+ Cybersecurity awareness: Protecting systems as complexity grows
“Critical thinking skills are also key, as they help validate outputs and ensure the technology supports the organizational strategy,” Fuller says.
What are Essential AI-Adjacent Skills?
Every modern IT role benefits from strong data reasoning: understanding how data is structured, how to judge its quality, and how to interpret AI outputs responsibly.
Workflow automation and API literacy help employees streamline processes without needing deep engineering skills, and AI-assisted coding allows even junior technologists to contribute faster by pairing with copilots for debugging, documentation, and refactoring.
“Add in cloud awareness, basic prompt engineering, and responsible AI practices, and you have the real foundation of today’s IT skillset,” Tarzi says. They are practical, adaptable capabilities that help workers use AI confidently instead of fearing it.
He adds the real “future-proof” skill comes down to learning how to learn—because tools will change, but that skill is what will keep workers employable.
Mike Capone, CEO, Qlik, says the pay premium is not going to people who can recite every new model by name--it’s going to people who can make those AI tools reliable in real workflows.
“For modern IT roles, that starts with understanding how data and work move through your organization, from the first request to the outcome,” he says.
It includes being comfortable using assistants to generate code, tests, or documentation and then instrumenting what you build so you can see cost, latency, and error rates.
From Capone’s perspective, another foundational skill is learning how to check and challenge machine generated output instead of accepting it at face value.
“Finally, IT professionals who understand privacy, compliance, and portability will stand out,” he says. “They will be trusted to design systems that can evolve as tools and regulations change.”
How Can Employers Reach Out?
Tarzi says if graduates are avoiding roles that mention AI, then employers need to rethink how they communicate and support skill-building from the moment a job is posted.
“Job descriptions should clearly distinguish between the skills applicants must bring on day one and the AI tools they’ll learn after they’re hired,” he says.
He argues degrees still signal a baseline of commitment and core knowledge, but employers should stop using them as a proxy for AI readiness.
“Companies that normalize learning on the job, and design pathways that make that learning achievable, don’t just reduce anxiety; they dramatically widen their talent pipeline at a time when too many candidates are self-selecting out,” Tarzi says.
Fuller says he agrees organizations have a responsibility to bridge the gap between emerging technologies and entry-level confidence.
“That starts with recognizing that skills, not titles, are the foundation of the future workforce,” he says.
He explains structured programs should begin with core concepts like data, algorithms, and ethics before advancing to technical specializations. Combining technical depth with human skills such as communication and critical thinking prepares professionals for real-world challenges.
“Upskilling isn’t optional; it’s a strategic imperative for retention, resilience, and competitiveness,” Fuller says.