In a recent CompTIA webinar, James Stanger, chief technology evangelist, shared a practical message for higher education: AI is changing how work gets done, and students need more than just an awareness of the technology. To be successful in today’s workforce, students need to be able to:
- apply what they know in real situations,
- use AI to solve problems,
- improve processes and workflows, and
- contribute value early.
Stanger drew on conversations with industry leaders and hiring managers to explore how employer expectations are changing and what academic programs should do in response.
For colleges and universities, the question has become how to prepare students for workplaces where AI is already part of everyday work. As roles change, students need to be ready to work in environments where people and AI systems share tasks, shape decisions, and influence outcomes.
The real shift is happening inside the work itself
One of Stanger’s clearest observations was that “it’s all about fixing workflows.” In other words, the most immediate impact of AI is not just on job titles. It is on the processes that connect people, tools, and decisions into completed work.
That shift is already changing what many roles look like in practice. Stanger shared an example of a developer who told him, “I don’t code anymore.” The point was not that technical knowledge no longer matters. It was that some responsibilities are moving upstream toward decision-making, validation, and improvement of AI-assisted output.
For educators, that means students need more than exposure to individual tools or isolated technical skills. They need to understand how work flows across teams and systems, how data is used, and where their decisions can improve outcomes. They also need practice identifying inefficiencies, asking better questions, and improving processes rather than simply completing tasks as assigned.
Stanger also emphasizes the growing importance of “speed to value.” Employers want graduates who can contribute meaningfully and quickly, not just complete tasks efficiently. They are looking for people who understand how their work connects to broader results and who can spot opportunities to improve the way work gets done.
Employers are prioritizing behaviors that create value quickly
When Stanger talks with hiring leaders, three traits come up repeatedly: “curiosity, agility, and creativity.” Employers are less focused on whether someone has seen a specific tool before and more focused on whether that person can apply knowledge in unfamiliar situations and still produce useful outcomes.
That shift should also change how academic programs think about student readiness. Employers are looking for students who can learn quickly, adapt to changing tools and expectations, and solve problems without a prescribed answer. In practice, that means preparing students to exercise judgment, not just demonstrate recall.
Just as important, Stanger makes the point that “AI skills don’t replace domain knowledge.” Strong foundations still matter whether students are preparing for careers in technology, healthcare, marketing, or other fields. What has changed is the expectation that they can pair that knowledge with practical AI use and apply it in context.
Where traditional academic models are under pressure
Many academic programs are already feeling this pressure. Traditional models often emphasize theory first and application later. In environments shaped by AI, that gap becomes harder to justify because employers increasingly expect students to translate knowledge into action more quickly.
Stanger is direct on this point: “It’s all about hands-on skill.” Applied learning is central to helping students build the kind of readiness employers now expect. Hands-on experiences help students move beyond understanding concepts in the abstract and start using them in ways that reflect real work.
Programs that rely too heavily on static content may struggle to show this kind of readiness. Students may complete coursework successfully and still lack the experience needed to operate effectively in dynamic, AI-enabled environments.
A more practical model for academic leadership
For higher education leaders, this doesn’t mean starting over. It means shifting toward applied capability while maintaining strong foundations. Stanger points to three immediate priorities:
- Embed applied learning throughout the program
- Integrate AI fluency across disciplines
- create clearer pathways from learning to workplace impact
Programs should give students repeated exposure to real-world scenarios that require judgment, problem-solving, and responsible use of AI. They should also help students understand how AI applies across all roles, not just in technical fields, and how that use connects to business processes, data decisions, and operational outcomes.
This is also where course design matters. Stanger’s point is that AI should be integrated into existing programs, not treated as a separate track. In practice, that might look like layering AI into existing courses, introducing shorter AI-focused modules, or incorporating dedicated coursework that blends foundational knowledge with hands-on application. The goal is to help students connect what they are learning to how work actually happens, and to align education more closely with real-world application and impact.
What educational programs should do next
Workforce readiness is becoming more closely tied to how work actually happens. It is no longer defined only by knowledge or credentials, but by the ability to operate and adapt within evolving systems. That creates an opportunity for colleges and universities to rethink how they help students demonstrate readiness before they enter the workforce.
Programs that combine strong foundations with applied practice can help students build confidence, adapt to change, and show employers they are ready to contribute. Educators don’t need to predict every change in AI. They need to give students the knowledge, practice, and confidence to work through change, use AI responsibly, and contribute in environments where workflows will continue to evolve.
Watch the full discussion with James Stanger to explore CompTIA’s ongoing work to support workforce readiness and employer-aligned education in an AI-driven environment.