Last summer, four Penn State students built an artificial intelligence (AI) sales coach for our home remodeling company. It was an instant success, and now it analyzes 100% of sales calls across 21 states, a notable increase from the 8% that three full-time quality assurance employees could manually review, with the cost of analysis dropping to a fraction of what we once spent.
One of those students accepted a full-time job offer. The other three are still working part-time through graduation, helping to build the company’s next system: computer vision software that watches warehouse cameras to predict and prevent worker injuries.
This is how we’re approaching workforce development. Not by searching for fully trained AI experts in a talent market that can’t supply them, but by giving students real operational problems, providing mentorship and oversight, and letting them build production systems from day one.
Businesses should embrace this sort of partnership to develop the AI workforce they actually need.
The skills gap meets the labor shortage
The workforce problem has two parts. According to Springboard’s 2024 State of the Workforce Skills Gap report, 70% of corporate leaders report a critical skills gap in their organization that negatively impacts business performance. At the same time, sectors like construction face severe labor shortages. Ninety-two percent of firms report difficulty finding qualified candidates, with the industry needing 439,000 additional workers in 2025 alone.
The obvious solutions don’t work at scale. Companies can hire external technical talent, but that averages $23,450 per role and takes 10 weeks to fill positions. And while internal upskilling costs less, it requires structured programs that most organizations lack. Only 6% of companies have trained over a quarter of their workforce on generative AI tools, despite 48% of workers expressing concern about falling behind without that training.
Educational institutions haven’t bridged the gap. Just 51% of U.S. high schools (in 2021) taught computer science, and university programs often lag industry needs by years. Students may graduate with theoretical knowledge, but they lack experience building systems that operate in production environments.
Academic partnerships structured around actual business problems address both sides of the equation.
What makes these partnerships work
Sustained pipelines are created from both sides, with companies providing real problems and production data, and students building working systems while learning.
When projects have genuine business stakes, that’s when these partnerships can really show their value. Students working on theoretical exercises gain theoretical experience. Students building systems that deploy gain practical fluency with production constraints, user feedback, and operational requirements that don’t exist in academic settings.
Industries facing technical skills gaps and physical labor shortages tend to benefit most. For instance, construction, home services, and manufacturing are sectors that need bodies and brains simultaneously.
Computer vision applications for safety monitoring represent one example. Around one in five U.S. workplace deaths occurred in the construction industry in 2023, yet most safety protocols still rely on manual inspection. AI systems that analyze camera footage for PPE compliance, hazard detection, and injury prediction can reduce incidents while requiring fewer safety personnel.
The talent pipeline created through these partnerships addresses workforce challenges that pure automation cannot solve. Students gain experience building AI systems for industries they might not have otherwise considered, which allows companies to develop employees who understand both the technical systems and the operational context.
Replicating the model for success
The approach scales beyond any single partnership. Companies facing skills gaps require three components: operational problems where automation creates measurable value, access to academic programs with relevant technical focus, and internal capacity to provide feedback and context to student teams.
The problems work best when they’re narrow enough to complete in months but significant enough to matter to the business; for example: automating quality assurance coverage, building predictive models for equipment maintenance, or developing computer vision for compliance monitoring. These are projects with clear deliverables and defined success metrics.
Academic partnerships exist across most major universities, but effectiveness varies widely. Programs in computer science, data science, information systems, and engineering typically include capstone projects or internship requirements. The difference lies in whether those projects connect to actual business operations or remain academic exercises. Companies that provide production data, real constraints, and end-user feedback see better outcomes than those offering vague problem statements.
Internal capacity doesn’t require large teams. Someone needs to define the problem, provide context, and give feedback. In many cases, that’s the manager who would otherwise be doing the work manually. Here, the time investment shifts from execution to guidance.
Industries with both labor shortages and safety concerns see compounding benefits. A warehouse manager who would otherwise conduct manual safety inspections can instead guide students building automated monitoring systems. The resulting tool continues working after the student graduates or moves to a full-time role, allowing the company to gain both the system and potential future employees who understand the operation.
The return on investment (ROI) appears in multiple places: functional tools that reduce operational costs, access to talent before they enter the broader job market, and reduced hiring expenses compared to competing for experienced practitioners. For students, the return comes as practical experience that makes them immediately employable and often leads directly to job offers.
The workforce you need must be ready on day one
Most students graduate with technical knowledge but rarely with experience applying it to real systems. They’ve studied the concepts but haven’t deployed tools under pressure, built models on messy data, or watched their work affect actual operations.
Academic partnerships change that equation. Students working on real business problems — with constraints, feedback loops, and end users — graduate as contributors, not trainees.
The AI workforce companies need exists. It just needs the chance to get hands-on experience early. Companies that provide production environments now will have functioning technical teams while competitors are still posting job applications.
