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Dr. Gleb Tsipursky, PhD
By Gleb Tsipursky, PhD,
Ventura County businesses are already learning how to make AI more useful. On Sept. 15, the Ventura Chamber of Commerce hosted a session on making AI tools better reflect a business’s own voice. That is a sensible move from experimentation toward practical adoption.
But faster adoption can create a hidden workforce problem. If employees are judged mainly by the quality and speed of AI-assisted output, managers may lose visibility into whether people understand the underlying task well enough to catch a plausible mistake.
That matters because AI often fails in ways that look competent. A polished answer can contain the wrong assumption, omit an important constraint, or apply a rule that does not fit the situation. The skill that becomes more valuable as AI improves is not merely prompting. It is knowing when the output should not be trusted.
Ventura County employers and educators should use a four-rung AI skills ladder.
The first rung is baseline competence. Before someone relies on AI for a recurring task, the person should be able to perform a representative version of that task without AI assistance. The goal is not to preserve inefficient manual work forever. It is to establish that the worker understands what a good result looks like.
The second rung is verified assistance. Let the worker use an approved AI tool, then require independent checking of the most consequential claims, calculations, sources, or recommendations. A faster answer counts as progress only when the worker can show why it is reliable.
The third rung is exception handling. Give the worker an AI output containing a realistic flaw: a bad assumption, an invented source, a customer request that falls outside policy, or a number that does not reconcile. Can the person spot the problem, correct it, pause the workflow, or escalate it to the right human?
The fourth rung is accountable ownership. The worker should be able to explain what the AI did, what the worker checked, what judgment was applied, and who remains responsible for the final decision.
This does not require a new bureaucracy. Employers can build the ladder into short work samples for common tasks. A marketing employee might revise AI-generated copy after checking product claims. An office administrator might use AI to summarize a document, then identify omissions. A supervisor might review an AI-generated recommendation and explain where human approval is required.
Ventura County already has infrastructure that can support this approach. The Ventura County Community College District’s Economic and Workforce Development operation connects local businesses with customized training and a pipeline of skilled graduates. That creates a natural place to align training with observable workplace standards rather than generic AI familiarity.
The region also has reason to care about who gets access to those first rungs. Oxnard College is hosting the Ventura County Regional Men of Color Conference on Sept. 18 with local colleges and universities. As AI changes entry-level work, access to supervised practice becomes an opportunity issue. People cannot demonstrate judgment if automation removes the tasks on which beginners traditionally learned.
The practical goal should be simple: use AI to accelerate work without obscuring whether workers are becoming more capable. Ventura County employers that can measure that difference will have a stronger basis for hiring, promotion, training, and responsible automation.
— 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
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