Utah employers are increasingly adopting artificial intelligence (AI) to enhance operations, often measuring metrics like usage, speed, cost, and output. However, a growing perspective suggests that these businesses should also measure how quickly their employees become independently capable when working with these new tools.
The challenge for Utah's workforce arises as AI can automate many entry-level tasks that traditionally served as foundational learning experiences for junior employees. Tasks such as summarizing records, building initial spreadsheets, drafting standard communications, or assembling research can now be quickly performed by AI. If the only metric for success is time saved, organizations might overlook whether new employees are genuinely learning less.
The Stanford Digital Economy Lab’s August 2026 update reported a widening employment shortfall for workers aged 22–25 in highly AI-exposed occupations. This shortfall increased from 15% in July 2025 to 19% by June 2026, primarily appearing through weaker hiring in roles where AI can automate tasks. This indicates a potential interruption in the progression of skills development for new workers.
To counter this, a "competency clock" is proposed to make employee development visible. This system would begin on an employee’s first day, defining a small set of milestones. These milestones include verifying an AI output against source material, identifying routine exceptions, explaining why an apparently plausible AI answer is wrong, making recommendations using incomplete information, handling recurring decisions without assistance, and recognizing when to escalate an issue.
The goal is to measure how long it takes an employee to reliably achieve each milestone. Artificial intelligence, ideally, should accelerate this clock. The technology can remove busywork, generate practice cases, and free experienced employees from some documentation tasks, allowing them more time to coach. This could enable new workers to spend more time on judgment-based tasks rather than mechanical preparation.
However, if AI tool usage increases while the time taken to reach these milestones also lengthens, it serves as a warning sign for management. This scenario could indicate that employees are producing more output without a deeper understanding of the work.
Utah’s existing apprenticeship system offers a valuable model for this approach. Apprenticeship Utah describes an earn-and-learn structure that combines on-the-job learning with related classroom instruction. The state’s youth pathway, the Talent Ready Apprenticeship Connection, allows participants to integrate high school education with work experience at a partner employer and an Associate of Applied Science degree.
The common thread in these systems is progression. Individuals become skilled through practice, receiving feedback, and taking on more demanding work, not merely through access to tools. Utah’s apprenticeship model also highlights the importance of supervision; young apprentices do not move directly from instruction to independent responsibility. AI-era office roles need a similar bridge.
Adopting a competency clock would shift the focus of managers’ questions about AI. Instead of asking, "How much did the tool save?" the question would become, "How much faster did this person become capable?" This reorientation provides a better measure of whether artificial intelligence is truly strengthening Utah’s workforce for the long term, rather than just speeding up immediate tasks.
