• Tom Strong, Program Director, The National Fund for Workforce Solutions
• Comments (0)

The Future of Workforce Equity: What A.I. Skills Will and Won’t Address

"As we move forward with defining the next frontier of work, the real challenge isn’t learning how to prompt a machine. It’s ensuring that the benefits of progress compound for everyone."

Tom Strong
Program Director
The National Fund for Workforce Solutions

Download PDF

The last few years have led many futurists to throw their predictions out the window. Nowhere is this truer than when it comes to generative artificial intelligence. Depending on your perspective, generative A.I. either promises or threatens one of the most significant labor market upheavals in over a century.

For those of us in the workforce development field, this moment demands a renewed focus on equity, adaptability, and inclusive skill-building. The question isn’t how workers will keep up, but how systems of work can evolve to ensure no one is left behind. One population we are particularly attentive to in this regard is workers over age 55, who often hear that to remain competitive in the job market, they must attain “A.I. skills.” Most of these workers, especially those in lower-wage roles, face growing uncertainty around their place in the workforce as AI adoption accelerates.

Our country has an aging population with many nearing retirement. Due to social support programs like Social Security and Medicare, low-income work is often thought of as an issue mostly affecting young to middle-aged individuals. However, in a country where millions are experiencing longer working lives than anticipated before retiring, poverty, income, and wealth inequality remain huge problems at any age. According to United For ALICE, nearly 40 percent of families headed by people 65 or older were income-constrained, asset-limited, and still working as of 2023. This does not include an additional 13 percent of the senior population that is under the federal poverty line.

But here's the problem: Even if “A.I. skills” are part of the solution, those skills have not actually been defined yet. A New York Times article earlier this year about how corporate A.I. use is changing labor market dynamics in the tech industry highlighted this confusion. According to a survey of more than 9,000 executives, 66 percent stated that they wouldn’t hire someone without A.I. skills. But few could define what they were actually looking for.

In the workforce development field, we champion “skills-first” or “skills-based” approaches to talent development and retention. The idea behind this is that traditional markers of achievement—such as grades, degrees, and references—aren’t always the best indicators for who has what it takes to both create value and truly thrive in a job. But without clear definitions and shared standards, we need to go deeper to solve the problem.

So, what would a practical, skills-based approach to A.I. look like? For starters, we need to realize that the biggest challenges are not about technical competencies. Part of the whole value proposition of large language models and other forms of A.I. is that they are easy to use. They are based on natural language rather than code or the ornate operating systems of the past. Moreover, the technology is changing so quickly that a focus on technical training will be a constantly moving target. People need to get comfortable with these new tools, they need to form new habits around them, but tech skills themselves are not the real impediment here.

Rather, we need to think more creatively about what helps people to succeed at work. Skills, technical or otherwise, are only one aspect of this.  We recommend an approach rooted in local experimentation, worker voice, and employer partnerships that reflect real-world conditions.  Over time, the National Fund and its local partners have learned that these elements help produce effective solutions for emerging and long-standing workforce challenges. Why these?

• Labor markets are local. Job opportunities, employer talent needs, education and training systems, and access to resources vary significantly by community.
• Frontline workers have unique insights into the problems facing workplaces. They tend to be closest to the products being made or the services being delivered, so when something goes wrong they are often the first to see it. By engaging them in co-design, businesses and workforce intermediaries can actually solve these problems rather than perpetuating them. We help businesses learn to treat employees as partners, which often results in increased innovation, profits, and employee retention and satisfaction.
• Employer partnerships are where the magic happens. There are no jobs without companies to employ people, and like workers, employers see different parts of the big picture than other stakeholders. Our approach emphasizes strong industry partnerships as well as a core focus on job quality as a result. Our network brings employers and training providers together to shape and scale skills training and adopt equitable practices that ensure all workers have access to opportunity.

Across our network, we’ve seen how practical workforce strategies can build the kind of skills that bolster corporate performance and help workers of all ages thrive. This approach  show how businesses and people can adapt to new skill needs in an ever-changing world, including one powered by A.I.

The future of work is uncertain, and that uncertainty is unlikely to fade anytime soon. But in workforce development, we don’t need perfect clarity to act. We can assist in building systems that help people adapt, grow, and thrive in the face of change. As we move forward with defining the next frontier of work, the real challenge isn’t learning how to prompt a machine. It’s ensuring that the benefits of progress compound for everyone, not just for those who already own the tools.