Rebuilding the First Rung of the Ladder
October 2026 | Written by Emma and Merilee
This is the second post of our AI series highlighting AI’s impact to our services and our clients across our primary sectors. Click “When AI Meets Your Values” to catch up on our first post where we discuss how Kinetic West is thinking about our own use of AI using a values-centered approach.
AI Impacts on Workforce Development
Last year, we wrote about entry-level jobs quietly deteriorating, and that issue has since exploded. In August, Forbes published an article, What the Changes in Entry-level Jobs Mean for New College Graduates, noting that employers have become increasingly reluctant to hire individuals with no work experience. The share of salaried jobs open to candidates with no experience has fallen 73% in four years, and this shift is happening in sectors not yet impacted by AI. Layer in the commercial uptick in AI tools, and the workforce system is seeing tectonic shifts constantly. Economists still haven't settled on whether AI will completely displace workers or simply shift the type of work performed at every level. Probably both.
Workforce and higher education policy experts and program specialists don’t have to wait for a consensus on what the future holds before acting. They can draw lessons from other major leaps in technology and recent market volatility. We can prepare for the future of work right now.
What makes AI feel categorically different is its reach. Past technology shifts arrived sector by sector, like the loom in textiles, the ATM in banking, and CAD in architecture. AI is landing in nearly every occupation at once, which is why it feels less like an acute disruption and more like a reckoning. While unprecedented in scale, speed, and impact, there are disruption patterns that still apply:
We’re used to change: With the broad adoption of the internet, the internet gave every worker access to a global library, and enterprise software replaced paper-based business processes. The market created the demand to teach workers new skills, and higher education caught up.
Vulnerable workers are the same: In response to the 2008 recession, the U.S. saw the passage of additional Title I-B grants for specific groups, namely: Adult Employment and Training Activities, Youth Activities, and Dislocated Worker Employment and Training Activities. This work focused on vulnerable populations, which are the same at-risk populations Congress would likely still focus on today.
Funding streams for possible new funding are similar: In 2008, Congress also created grant pools for specific occupations that needed retooling and upskilling for individuals who needed to change industries. These federal funding streams are the same today and likely the vehicles for new funding when it becomes a priority.
The workforce and higher education systems have opportunities now to get ahead of AI impacts on workforce education and training:
Prepare AI tool training as part of existing programs. Almost all forms of work will be impacted by AI, so don’t waste time creating separate trainings; instead, include AI tools in existing programs. Learn from drone adoption. When drones became a commercial tool, colleges stood up drone programs independently before realizing that drones were a tool for marketing, law enforcement, and agriculture programs. Market disruptors don’t have to be the sole focus of a program; the solutions are often embedded in what a program already does well.
Design and reward programs that can change. Employers spent years criticizing computer programming tracks for not keeping up with industry. Speed of program improvement, adaptability, and relevancy are issues today, even before AI was widely adopted. Programs that are able to stay current are built to be revised: their foundational outcomes are locked in, but they also include flexible sections for guest speakers, live case work, and simulations that can be swapped as tools shift. For policymakers, that means rewarding programs that have mechanisms and systems to adapt. Program directors and leaders can look for ways to shorten program approval timelines or have customizable sections that are easier to change.
Make AI a part of workforce and higher education leadership development. Half the world is saying slow down on AI, and half the world is saying move at full speed. Our institutions of learning can be the tie-breaker and set standards for ethical AI adoption, but only if all staff have leadership development opportunities..
While we expect that AI will impact employees at every point in their career path, there are occupations that will see rapid loss of entry-level jobs and experiences, e.g., graphic design, project management roles, office administration, to name a few. Iris Palmer from New America recently reflected on entry-level hiring and shared, “the tasks that used to be the first rung on the ladder are exactly the tasks AI absorbs first.” New graduates can differentiate themselves by gaining work experience during college. A ZipRecruiter study found that graduates were hired at roughly twice the rate of those without. We know that experiential learning, career exploration, and employment experiences connected to an education pathway can be rocket fuel to a career path. As Iris Palmer also wrote, " ’Go to college and figure the rest out later’ no longer works for most people.” Kinetic West and our partners are committed to career exploration and preparation, which have never been more important.
Employer interviews from Kinetic West's research consistently name the same priorities, and they're the ones AI isn't likely to solve for. Employers are looking for individuals with durable skills like problem-solving, human-to-human conflict resolution, and discernment. If entry-level work isn’t going to give people the repetition they need to build those kinds of skills, we must find creative ways for entry-level professionals to get their reps in.
AI tools are reshaping every occupation and thinning the bottom rung of the ladder. Workforce and higher education systems can’t just wait and see while a generation of entry-level workers are missing the opportunities to kick-start their careers towards economic self- and family-sustainability. These systems must adapt by building experiential learning at scale, committing to bringing AI tools into existing programs, fortifying leadership development training, and shortening the time it takes to improve and seek approval on programs. None of these requires knowing how this ends. It requires deciding that the first rung is worth rebuilding.
Up Next: AI + Education – A student centered approach in Washington State.
