DC Should Make AI Accelerate Apprenticeship

OPINION · By Dr. Gleb Tsipursky

Washington is moving quickly from talking about artificial intelligence to putting it into the workforce. In February, the District became the first major U.S. city to require responsible-AI training for government employees and contractors. The regional Talent Capital initiative now uses an AI agent to match displaced workers with jobs and tailored training across DC, Maryland and Virginia.

Those are useful moves. They prepare workers to use the technology and help people find opportunities. The next challenge sits inside the workplace itself: preserving the path by which inexperienced employees become experienced ones.

Stanford researchers analyzing ADP payroll data through June 2026 found that employment among U.S. workers ages 22–25 in highly AI-exposed occupations is about 19% below where it would be if it had kept pace with similarly aged workers in less-exposed occupations. The comparable measure was 15% in the July 2025 data vintage. The adjustment appears mainly through lower hiring rather than higher separations, and experienced workers show no comparable gap.

That pattern should change how Washington employers think about AI productivity. If a law firm, consultancy, contractor, nonprofit or agency automates the routine preparation once assigned to junior employees, the right response is not to erase the junior role. It is to redesign the role around the work that develops judgment.

A first-year analyst no longer needs to spend hours assembling a basic research summary if AI can produce a strong first pass. Give that analyst responsibility for checking the underlying evidence, identifying contradictions, testing the model's assumptions and explaining what the output means for a real decision. A junior communications employee can spend less time drafting standard copy and more time tailoring messages for difficult audiences, defending choices and learning from senior review. A new program officer can use AI to organize routine material, then spend more time evaluating exceptions, interviewing stakeholders and documenting why a recommendation holds up.

This requires experienced employees to play a more deliberate teaching role. Part of the time AI saves should be reinvested in coaching, review and structured exposure to difficult cases. Managers should assign progressively harder work instead of waiting for juniors to absorb expertise through repetition.

The measurement system needs to change too. Cost per task, hours saved and output volume capture efficiency. They do not show whether the organization is producing future experts. Add time to independent competence: how long before a new employee can handle a defined class of work accurately, explain the reasoning, catch errors and know when to escalate?

DC already treats apprenticeship as a workforce strategy. The Department of Employment Services describes apprenticeship as a combination of on-the-job training and classroom instruction, and its system connects employers with people seeking career pathways. That principle should move into white-collar and AI-exposed work as well.

Washington has an unusual concentration of organizations where professional judgment carries real consequences. Government agencies, federal contractors, law firms, policy shops, health organizations and associations all depend on people who can recognize nuance, challenge weak evidence and take responsibility for decisions. Those capabilities come from guided practice.

AI can make that practice faster. It can expose junior employees to more scenarios, generate alternatives for them to evaluate, surface exceptions for discussion and free senior staff from low-value preparation. The productivity gain becomes more valuable when some of it is converted into human capability.

The goal for Washington employers should be straightforward: automate routine preparation, then accelerate the apprenticeship that turns new hires into trusted professionals. That is how DC can gain from AI today without consuming the talent pipeline it will need tomorrow.

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).

The views expressed in this op-ed are the author's own and do not necessarily reflect those of Washington DC Magazine. WDCM welcomes opinion submissions from the community.