AI Requires a New Kind of Talent Infrastructure

Gallup's recent report on Gen Z and artificial intelligence highlights what it describes as an "AI paradox." Although AI use among Gen Z has remained relatively stable over the past year, confidence in the technology has declined. Excitement and hopefulness have decreased, while concerns about AI's impact on learning, critical thinking, and future employment have grown. At the same time, a majority of students recognize that AI skills will be important for their education and future careers, suggesting that many young people view AI as increasingly necessary even as they become more uncertain about its broader implications.

Gallup also notes that this trend is unfolding while AI adoption across the broader economy continues to accelerate. Organizations are investing heavily in AI, expanding employee access to AI tools, and integrating AI into day-to-day work. Against that backdrop, Gen Z's declining confidence presents an interesting paradox.

At first glance, these findings appear contradictory. They may instead reflect the fact that business leaders and young people are evaluating AI from two very different perspectives.

Business leaders and employers tend to view AI through the lens of organizational performance. They see opportunities to improve productivity, accelerate innovation, and create new products and services. Students and young adults, on the other hand, are trying to understand how AI will affect their own career prospects and economic future. Those are different perspectives, and they naturally lead to different conclusions.

A second observation reinforced this point for me.

Shortly after reading the Gallup report, I attended the launch of the Technician Economy initiative in Houston. The event was organized by UnMudl and brought together employers, community colleges, workforce organizations, and K–12 leaders to discuss a challenge that is becoming increasingly common across the country: the growing demand for technicians who can support advanced manufacturing, robotics, semiconductor production, energy systems, logistics, and other industries that are becoming more dependent on AI and automation.

What stood out was not simply the discussion about technician jobs. It was the degree of coordination taking place among employers and education partners. Employers were not waiting for schools to determine what students should learn. They were describing the occupations they anticipate needing to fill, the skills required for those occupations, and the importance of building stronger connections directly into those careers.

That type of regional coordination deserves more attention.

Much of the public discussion surrounding AI focuses on which jobs may be displaced. The conversation in Houston was focused on a different question: How do we prepare enough people for the jobs that AI and related technologies are creating?

The Technician Economy framework offers a useful perspective because it reframes AI as a workforce opportunity rather than simply a technological disruption. It argues that the nation's challenge is becoming less about innovation itself and more about deployment. The United States continues to invest heavily in advanced technologies, but those investments depend on a trained workforce capable of installing, operating, maintaining, troubleshooting, and improving increasingly sophisticated systems. Those responsibilities increasingly fall to technicians.

Many of these occupations provide strong wages for young people, offer opportunities for advancement, and do not necessarily require a traditional four-year degree. They do, however, require a combination of durable skills and technical knowledge that employers consistently identify as difficult to find.

The Houston meeting also highlighted another question that receives much less attention.

If employers can estimate how many technicians they will need, do we have comparable information about the supply of future talent?

Historically, the answer has been no.

We have developed increasingly sophisticated labor market information that helps estimate employer demand. We know much less about the relevant skills students are developing before they enter the workforce and how those skills align with regional employment opportunities.

The question of how we can more accurately measure and validate the foundational skills necessary for employment in these emerging roles has shaped much of our work at Educational Results Partnership.

Over the past several years, we have been developing Skills Currency and SkillSight to help make student skills more visible. Skills Currency translates evidence from students' academic performance into skills profiles that help students and employers understand where those skills create value in the labor market.

SkillSight allows students to explore those skills, identify careers where those skills are valued, and better understand how their current academic performance connects to future opportunities.

The objective is not to direct students toward a particular career. Rather, it is to provide better information so students can make more informed decisions about their own futures and understand the workforce relevance of their K–12 education.

Our recent pilot with high school students reinforced the value of this approach. After viewing their skills profiles alongside their academic transcripts, more than eight in ten students reported a better understanding of their top job skills and a better understanding of which careers aligned with those skills. Three out of four reported feeling more confident about their value in the workplace.

Perhaps even more interesting was what students asked for next.

Once students understood their skills, they wanted more information about career pathways, education and training options, internships, local employers, and employment opportunities. In other words, increased skills visibility created demand for better career information about specific occupations they had previously been unaware of.

Taken together, the Gallup report, the Technician Economy initiative in Houston, and our own pilot results suggest that these are not separate conversations. They describe different parts of the same system.

Gallup highlights that many young people remain uncertain and skeptical about how AI will affect their futures.

The Technician Economy demonstrates that employers are already organizing around new workforce needs created by AI and other advanced technologies.

Our work at ERP suggests that many students already possess foundational skills that are relevant to these emerging occupations but often lack visibility into those strengths and where they create value.

Viewed together, these observations point toward a broader opportunity.

Skills provide a common language between education and employment. If regions can better understand both employer demand and student talent supply using that common language, they can begin coordinating education, workforce development, and economic development in ways that have not previously been possible.

Consider what that could look like.

A regional employer collaborative identifies the technician occupations expected to experience the greatest hiring demand over the next five years. At the same time, student data are used to estimate how many graduating seniors have already demonstrated the foundational skills associated with success in those occupations.

Rather than discussing workforce shortages in the abstract, regional leaders can begin asking much more practical questions.

·        Where does talent supply already meet employer demand?

·        Where are the largest gaps?

·        Which skills are most common among graduating students?

·        Where should additional work-based learning, career pathways, dual enrollment, or short-term training programs be expanded?

Those conversations become possible when both sides of the equation are visible using a common language.

In my view, this represents a more productive way to think about AI's impact on the next generation.

The question is not simply whether young people are optimistic or skeptical about AI.

The more important question is whether we are building the information infrastructure that helps students understand where they fit within an economy increasingly shaped by AI.

Helping students recognize the skills they are developing, understand where those skills create value, and connecting them to emerging opportunities may ultimately do more than improve career navigation. It may also help bridge the gap between the way employers view AI—as a driver of new economic opportunity—and the way many young people currently experience it, as a source of uncertainty about their future.

That is a challenge that education, employers, and workforce organizations are well positioned to address together.