The program has been a resounding success not just because USC Upstate asked for industry support, but by embedding industry right into the program.

Engineering, like many sciences, requires laboratory learning for students.  This usually occurs in a laboratory environment on the university campus and includes hands-on learning opportunities for students.

USC Upstate does the laboratory experience differently.  Each week, Industrial Engineering students at Upstate go out to real manufacturing settings and get a hands-on experience within an actual industry environment.  With this innovative educational method, students get exposure to up to ten different companies per class, and get real hands-on experiences in various industries, all within a single semester.  This provides opportunities for students to learn more – and at a faster pace.

For companies who partner with USC Upstate, they get to share their company’s products, environment, and culture, with prospective engineers – who are also prospective future employees – and potential future customers.  The companies, because they have a hands-on experience, also can get some real engineering support with a very low investment – just one or two three-hour sessions with a group of future engineers.

Industrial Engineering students have opportunities to run robots, run automated guided vehicles, analyze job functions and make recommendations on improvements, learn how to analyze the quality of coffee, check and splice cables, assemble drive units, and much more.  These examples were all in just one class and were real manufacturing environment experiences that drove rapid learning for students.   

For many companies, the relationships with students did not end after that brief session.  Many of the companies who participated in the program have already hired some of these future engineers as interns.  Companies saw the immediate value of the education that these students were receiving, and put them to work!

This model, called “Lab-in-Industry”, has been so successful that it will be incorporated in all future engineering programs at the University, and it is being considered for other programs at the University as well.  To show how valuable it has been for the industry partners, ALL partners volunteered to take part in the program again.  They see that minimal investment, coupled with a strong partnership, provides current and future value.

USC Upstate is improving the way engineering is taught, and industry is part of that change.  Visionaries from industry are helping to make that happen.  Companies today have to look beyond the traditional education and hiring processes and look for opportunities to innovate with partnerships with higher education.  That effort will lead to better opportunities to connect to the talent needed in the future.

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If higher education’s relevance is being rewritten, what does that actually look like in practice?

During a recent Upstate Talent Forum discussion, education and workforce leaders focused less on defending higher education and more on how institutions are adapting to meet changing workforce needs.

Their examples revealed a common theme: the strongest education partners are becoming more flexible, more connected to employers, and more responsive to workforce demand.

AI Is Accelerating Change and Exposing Slow Systems

Artificial intelligence will reshape jobs, industries, and workforce expectations. Some roles will disappear. Others will evolve rapidly.

The challenge discussed by panelists was not whether higher education remains relevant, but whether institutions can adapt quickly enough to industry change.

Examples included AI innovation labs, AI-ready curriculum development, and new partnerships designed to shorten the gap between education and workforce needs.

CEO takeaway: The strongest education partners will be the ones capable of adapting at the speed of industry change.

Relevance Now Looks Like Pathways and Partnerships

The conversation became most practical when leaders discussed what modern workforce alignment actually looks like:

• reverse transfer pathways
• internships and co-ops
• work-based learning
• service learning
• accelerated degree models
• employer-connected curriculum
• short-term credentialing opportunities

The focus was clear: create lower-risk, faster, more flexible pathways tied directly to workforce outcomes.

CEO takeaway: The most relevant institutions and industries are partnering to build clearer connections between learning and work skills. This includes building direct pipelines with employers to meet regional talent needs.

What CEOs Can Do Next

The organizations making the greatest impact are building stronger alignment between education and industry through faster pathways, clearer outcomes, and more direct connections between learning and work.

For business leaders, the opportunity is no longer simply to support education. It is to actively help shape the talent ecosystem their organizations will depend on in the years ahead.

That can start with practical steps:

• • Create one meaningful work-based learning opportunity with measurable outcomes
• • Partner with an institution on a short-term credential tied to a workforce need
• • Build mentorship opportunities for first-generation and emerging talent
• • Ask education partners for a direct, business-focused point of contact
• • Participate in conversations that help align regional workforce priorities

Higher education is adapting under pressure from technology, demographics, economics, and changing workforce expectations. The institutions creating the greatest impact are not working alone. They are building stronger partnerships with employers, communities, and regional leaders.

The question is no longer whether higher education is relevant. The question is which regions, institutions, and employers will work together fast enough to build the workforce and communities the future demands.

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This is frequently and colloquially referred to as “Bring Your Own AI” (BYOAI), although there is a difference between the two. BYOAI is when employees pay for their own preferred AI tools and leverage these personal AI tools to complete professional tasks. Shadow AI, on the other hand, includes BYOAI and a more expansive set of tools, models, extensions, and agents within an underground AI ecosystem.

CEOs are now faced with the challenging and seemingly impossible task of “locking the digital gates” and preventing a complete governance collapse, which in turn can lead to unprecedented and profound security risks and breakdowns. Agentic AI, where autonomous agents hold the potential to run amok, has significantly increased the challenges of accountability, transparency, responsibility, and privacy. This is uncharted territory for the IT department and cybersecurity leaders.

The Compliance Paradox: High Output vs. Hidden Risk

Treating Shadow AI as a technology problem rarely works because Shadow AI is not a technology issue. It is a behavioral attitude or preference, frequently embraced and perpetuated by high-performing, efficiency-focused employees. Highly productive employees are eager to get structured and routine tasks done so that they can focus their attention on high-visibility projects. When the possibility of layoffs looms, anxious employees feel the pressure to get more done with less. Toxic environments, understandably, often lead to an increased, expanding, and persistent reliance on Shadow AI. Employees also want to keep close to their chest any advantages they gain from AI tools, lest they be considered less competent or, in extreme cases, “cheating the system.”

Employees may stray outside the system to quickly build cutting-edge skills, either to widen the performance gap between them and other employees, prepare to land a new job, or impress their current employer. The more proficient an employee is in AI, the more prone they are to try new tools. In many organizations, the approval process for AI tools is slow, bureaucratic, and time-consuming, failing to keep up with the dynamic needs of employees or the rapid speed of AI change. In short, high performers use Shadow AI because they get a massive, hidden boost in output. This essentially creates a productivity premium for Shadow AI users, who are often viewed as highly capable and productive, while simultaneously creating serious data risks for the organization.

Senior leaders, on the other hand, may be lulled into believing that nothing problematic is occurring and assume that current governance processes are adequate. As monitoring and tracking systems weaken, employees assume that the organization may not explicitly approve, but is not necessarily opposed to, Shadow AI.

Why Rigid Policies Fail:

We are on a slippery slope and, therefore, any potential solutions must be aligned with the culture of the organization and the needs of employees. A top-down approach, or a “thou shalt” approach, is not likely to succeed in the long run.

In my next blog post, I will take a deeper dive into potential solutions. In the meantime, here are a few questions to reflect on:

• 1. How frequently do you survey employees to determine their shifting needs for AI tools?
  2. How efficient and effective are your approval processes for AI tools?
  3. Is your AI tool inventory aligned with your AI strategy?
  4. Is your AI governance a living document that employees understand and embrace?
  5. What and how do you currently track AI tool usage?
  6. What happens when evidence surfaces that employees are using unauthorized tools?

Managing Shadow AI demands avoiding a punitive approach and seeking ways to genuinely understand and align the productivity needs and career trajectory of your employees with your core governance philosophy, policies, and procedures.

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