The column below reflects the views of the author, and these opinions are neither endorsed nor supported by WisOpinion.com.

State workforce officials are gathering in Milwaukee this week for the 2026 NASWA Summit, where leaders of state workforce agencies will compare approaches to workforce development, employment services, labor-market information and technology. That makes this a useful moment for Wisconsin to ask a harder question about its own AI workforce investments: What should count as success?

Wisconsin is already moving beyond generic AI awareness. The state’s WisTRAIN program is built around employer-led training in advanced manufacturing and artificial intelligence, including data analytics, automation, robotics, cybersecurity and human-AI collaboration. That is the right direction. Employers need training connected to real jobs rather than abstract demonstrations.

But a training program can still look successful on paper while weakening the first rung of a career ladder.

Generative AI is especially good at many of the routine tasks that once helped junior workers learn how a job actually works. Drafting a first version, organizing information, preparing a basic analysis or documenting a process may be inefficient, but those tasks also build pattern recognition. When software does them instantly, employers can gain productivity while quietly removing the practice that helps a novice become dependable.

Wisconsin should therefore supplement completion rates and credentials with a first-rung scorecard.

First, measure whether trainees get paid opportunities to perform real work. A course matters more when it leads to supervised responsibility rather than another certificate.

Second, test baseline competence before measuring AI-assisted speed. Workers should understand the underlying task well enough to recognize when an AI system produces a polished but wrong result.

Third, measure verification and exception handling. Can the worker check a consequential claim independently? Can the worker recognize when a normal workflow no longer applies? Does the worker know when to correct the output, pause the process or escalate to someone with more expertise?

Fourth, track retention and advancement after training. The strongest evidence of workforce development is not that someone finished instruction. It is that the person can take on greater responsibility months later.

This is compatible with what Wisconsin is already trying to accomplish. WisTRAIN explicitly emphasizes employer-driven occupational training and human-AI collaboration. Its design also rewards employment retention after training. The next step is to make capability progression just as visible as participation.

Employers do not need to preserve obsolete busywork simply because junior employees once learned through it. They do need to replace the learning that disappears when AI absorbs routine tasks.

The practical goal should be a shorter path to judgment, not a shorter path to dependence. If Wisconsin measures whether AI training creates that path, its workforce investment will tell employers and workers something far more useful than how many people completed a course.

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