Commentary: Hampton Roads Needs An AI Skills-to-Work Ladder

By Dr. Gleb Tsipursky

Old Dominion University brought employers, educators, workforce professionals and community leaders together Thursday September 17 for its inaugural Workforce Development Conference under a timely theme: “Future-Ready Workforces: Developing Talent for a Changing World.”

The event gave Hampton Roads a useful opportunity to ask a harder question about artificial intelligence. How will we know whether people are becoming more capable at work rather than simply becoming more familiar with AI tools?

That distinction matters because AI can create the appearance of skill very quickly. A worker can produce a polished draft, summary or analysis in minutes. Yet the output still may contain unsupported claims, missed context or subtle errors. If the worker cannot identify those problems, the technology has increased speed without necessarily increasing competence.

The same problem appears in training programs. Organizations often count attendance, completed courses or tool usage because those measures are easy to track. They reveal little about whether someone can use AI reliably on the actual tasks that matter in a job.

Hampton Roads has the pieces to do better. Norfolk State University’s digital technology career pathway explicitly centers artificial intelligence, data analytics and cybersecurity, while its career services connect students to employers and emerging technical roles. The next step is to make the transition from learning to dependable performance visible.

Employers and educators can do that with a four-stage skills-to-work ladder.

First, establish a baseline. Give a learner or employee a representative task before AI training and measure the quality of the final result, the total time required and the corrections an experienced reviewer has to make. That creates a real comparison point.

Second, repeat a comparable task with AI, but require supervised review. The learner should explain what the tool did well, what needed correction and why. This stage turns verification into part of the skill rather than an invisible cleanup chore performed by someone more experienced.

Third, move the learner into exception ownership. Routine AI-assisted work is the easy part. Competence becomes clearer when the normal process breaks down, the evidence conflicts, the requested task falls outside the standard pattern or the AI produces an answer that sounds plausible but should not be trusted.

Fourth, create a path to independent signoff. People who can consistently verify output and handle exceptions should eventually take responsibility for consequential work themselves. That progression matters because many of the routine assignments now being automated once served as the practice ground where new workers developed judgment.

The measurement should continue after training ends. Retest a comparable task 30 days later. Count the whole workflow, including checking, corrections and rework. Ask whether the employee still uses the process without being prompted and whether final quality has improved. A faster first draft can be valuable, but it should not be mistaken for a stronger worker when someone else has to spend the saved time fixing mistakes.

This approach is especially useful for a region trying to expand opportunity. Students and early-career workers across Hampton Roads need more than exposure to AI. They need a credible route from assisted work to professional judgment. That matters for the Black communities the New Journal and Guide has served for generations because access to a tool has limited value without access to the experiences that build expertise and career mobility.

The same ladder helps employers. Organizations adopting AI need people who understand the work well enough to challenge weak output, recognize unusual cases and know when a human decision is required. Those capabilities make AI adoption more dependable and reduce the hidden costs of verification and rework.

Hampton Roads already has universities, employers and workforce organizations focused on preparing people for a changing economy. The region can make that effort stronger by agreeing on a simple standard: AI training counts when it transfers into better real work. A skills-to-work ladder would give employers and workers a practical way to prove that it does.

Gleb Tsipursky, PhD, a behavioral scientist, CEO of Disaster Avoidance Experts, and author of The Psychology of AI Adoption at Work: From Resistance to Results (Georgetown University Press, 2026). https://disasteravoidanceexperts.com/aibook