Tulsa is spending real effort on a problem many growing regions share: finding and developing skilled workers. The Tulsa Regional Chamber has been building a talent pipeline for high-demand fields including engineering, accounting and industrial machinery maintenance. Tulsa Tech is also helping employers design CareerTech apprenticeships around the skills they actually need.
AI changes the design problem. Companies can now automate more of the routine work that once gave new employees their first exposure to a profession. That raises productivity, but it also removes practice unless leaders deliberately replace it with better practice.
Stanford’s August employment update used ADP payroll data covering millions of U.S. workers through June 2026. Employment among workers ages 22–25 in highly AI-exposed occupations was about 19% below where it would be if it had kept pace with similarly aged workers in less-exposed occupations. The adjustment appears mainly through reduced hiring, especially where AI use tends to automate human tasks. Experienced workers show no comparable gap.
Tulsa employers should treat that finding as a workflow-design problem.
In aerospace, AI can organize maintenance records, draft inspection notes and surface likely causes of a fault. A junior technician still needs supervised time diagnosing the unusual failure, deciding what evidence matters and explaining why a repair is safe. In accounting, AI can prepare a reconciliation, but the employee should investigate anomalies and defend the treatment of an unusual transaction. In software, AI can generate code while the junior developer learns to test edge cases and reason through failures.
Those are not leftovers after automation. They are the apprenticeship.
Tulsa’s Chamber recently highlighted work-based learning, internships and mentoring as ways to broaden the regional talent ecosystem. Employers can build on that infrastructure with a simple operating rule: every hour saved by AI should create some measurable amount of higher-value practice for newer workers.
Managers do not need to preserve repetitive tasks for tradition’s sake. They need to preserve the progression from observation to assisted work to independent judgment. Senior employees should review exceptions, explain tradeoffs and hand over increasingly consequential decisions as competence grows.
Then measure the result. Alongside output and labor hours, track the time it takes a new employee to handle a defined class of cases independently. That tells leaders whether AI is building capability or simply reducing headcount at the bottom.
Tulsa wants a deeper talent pool. AI can help create one if employers use productivity gains to accelerate learning instead of eliminating the work through which people become experts.
About the author: Gleb Tsipursky, PhD, is 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