South Dakota should use AI to strengthen apprenticeship programs
South Dakota is deciding what skills its workforce needs next. The Workforce Development Council’s August 25 agenda included an Essential Skills Certificate, Hot Careers and new career-and-technical-education standards. That is exactly the right moment to decide what happens when artificial intelligence takes over the routine work through which young employees once learned a job.
The newest Stanford Digital Economy Lab employment analysis raises the stakes. Using ADP payroll data covering millions of U.S. workers through June 2026, the researchers found employment among workers ages 22 to 25 in highly AI-exposed occupations about 19 percent below where it would be if it had kept pace with similarly aged workers in less-exposed occupations. The comparable gap was 15 percent in the July 2025 data vintage. The adjustment appears mainly through reduced hiring rather than higher separations, and experienced workers show no comparable gap.
South Dakota employers should respond by redesigning entry-level work before deleting it.
The state already has a useful model. Registered apprenticeship in South Dakota combines paid work with structured training and lets employers build programs around the skills their jobs actually require. AI should extend that logic into offices, professional services, health administration, finance, agriculture, manufacturing support and other knowledge-intensive work.
Start with the task map. Let AI handle routine preparation: first drafts, basic research, scheduling, standard summaries, document formatting and predictable data pulls. Then move junior employees sooner into work that develops judgment: verifying an output against source material, spotting an exception, testing a recommendation, asking a customer the clarifying question, explaining a decision and escalating the case that does not fit the template.
Next, make coaching a measured production activity. If AI saves a team 10 hours of preparation each week, assign a portion of those hours to senior review. Give experienced employees responsibility for showing new hires how they distinguish a plausible answer from a reliable one, how they recognize an unusual case and how they communicate uncertainty without freezing a decision.
Finally, add one workforce metric to every AI project: time to independent competence. Cost, hours and output still matter. They do not tell leaders whether the organization is building the next generation of people who can handle the hard cases. A company that cuts junior labor today and discovers three years later that nobody learned the work has booked a short-term saving and created a long-term capability deficit.
South Dakota’s workforce strategy already recognizes that skills grow through work, standards and employer involvement. The AI era calls for the same discipline. Automate routine preparation, then use the resulting capacity to accelerate apprenticeship. The goal should be fewer wasted hours and faster development of people who can make sound decisions on their own.
Gleb Tsipursky, PhD, a behavioral scientist, is CEO of Disaster Avoidance Experts, and author of The Psychology of AI Adoption at Work: From Resistance to Results (Georgetown University Press, 2026).
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