How AI Is Reshaping Career Ladders and What It Means for Your Future

(Illustration by iStock/Yutthana Gaetgeaw)

The growing use of generative AI in the workplace creates a paradox for entry-level workers. The very tasks that once trained newcomers—such as summarizing meetings, cleaning data, and drafting memos—are increasingly automated. This means entry-level jobs today require experience that entry-level roles no longer supply.

AI has cannibalized the routine, low-risk work tasks that used to teach people how to operate in complex organizations. Without these foundational rungs, climbing the opportunity ladder into better employment becomes steeper—and for many, impossible. This isn't a temporary issue; AI is fundamentally reorganizing work, reshaping what knowledge and skills matter, and redefining how people acquire them.

The consequences ripple from individual career starts to the broader American promise of economic and social mobility. Yet the same technology that complicates first jobs can help reinvent how experience is earned, validated, and scaled. If we use AI to widen—not narrow—access to education, training, and proof of knowledge and skill, we can build a stronger career ladder to the middle class and beyond.

The Changing Entry-Level Job

Entry-level jobs traditionally serve two purposes: They enable work to be done, and they provide training. As routine tasks—such as scheduling, basic analysis, and internal communications—get handled by AI, human roles begin at a higher baseline. However, when that baseline assumes fluency with workflows, norms, and tools that were once learned on the job, many newcomers are left out.

This creates an experience gap: You need experience to get a job, and you need a job to gain the experience you need. Education and training providers, such as high schools or colleges, are rarely designed to keep pace with evolving workplace technology. Moreover, employers have trimmed in-house training, and screening algorithms cold-sort resumes for "proven contributors." The ladder's bottom rungs, once plentiful, are now scarce.

AI is also reconfiguring the idea and value of expertise. Scarcity is shifting from static knowledge—what you know—to dynamic capability—how you solve problems with tools, verify outputs, and apply context. Employers increasingly value individuals who can collaborate with AI by defining problems, orchestrating data and models, verifying reliability, and communicating results to others.

That favors a mix of analytical, social, and adaptive skills—rarely mastered in lecture halls alone. The question is less what credential do you hold? and more what can you reliably do, and can you show it? In this world, proof beats pedigree. Portfolios, projects, simulations, and performance tasks become stronger signals than course lists and traditional diplomas and degrees.

Used well, AI can democratize expertise. It can help a college student analyze data, a retail worker generate working code snippets, or a high school graduate produce professional-grade marketing assets. It reveals what analysts call latent expertise—capabilities current credentials fail to see. If employers look for that proof, more talent gets found.

The Credentials Dilemma

Employers struggle to move beyond diplomas and degrees without credible alternatives. Credentials proliferate, but what do they mean? And how do we create a labor market that can actually see knowledge and skills?

Credentials Engine has identified nearly 1.1 million unique credentials in the United States across four types of credential providers:

• Post-secondary educational institutions, with nearly 350,500 degrees and certificates.
• Massive open online course (MOOC) providers, with over 13,000 course completion certificates, micro-credentials, and online degrees from foreign universities.
• Non-academic providers, with nearly 656,500 badges, course completion certificates, licenses, certifications, and apprenticeships.
• Secondary schools, with over 56,100 diplomas from public and private secondary schools, alternative certificates from secondary schools, and high school equivalency diplomas.

Their report emphasizes the need to significantly enhance transparency in the credential marketplace to foster individual mobility and national economic growth. For example, how do credentialing practices overlap, including how certificates offered by institutions of higher education build on—or stack up to—other certificates and degrees? How are badges utilized to represent these and other credentials? How do we categorize different credentialing programs to reflect the time required to complete them and their value in the marketplace?

Another Credential Engine report estimated the total yearly expenditure by educational institutions, employers, federal grant programs, states, and the military on these credential programs is over $2.1 trillion. This substantial expenditure underscores the seriousness of the credentialing marketplace and the need for more effective processes for accountability and decision-making.

The yearly expenditure for these credentials also raises questions about their individual value and outcomes, especially which ones truly move a career forward and produce significant wage gains. An analysis by the Burning Glass Institute and American Enterprise Institute reports that "about 12 percent of credentials deliver significant wage gains that earners wouldn't have otherwise gotten, and just 18 percent of credential earners are likely to see wage increases their peers don't enjoy." The Burning Glass Institute has created a Career Value Index Navigator, an online tool that provides information on outcomes like wage gains and career advancement of virtually every certification in America, as well as more than 20,000 other non-degree credentials.

An Education and Training Infrastructure for the AI Era

AI won't be met with bigger budgets alone. One analysis summarizes the situation as follows: "...historically, workforce training has been a bust. Less than half of those trained under grants landed a job, with no evidence that the jobs people obtained were well-paying. Similarly, studies of training find minimal evidence of success."

What's needed is a redesigned model that treats work as a primary venue for learning, validates capability with evidence, and helps people keep climbing after their first job. Here are ten design principles for a reinvented education and training infrastructure for the AI era:

1. Create hybrid institutions that erase boundaries. Organizations that operate across K-12, community college, other providers, and employers would allow learners to move through one integrated system rather than multiple disconnected ones. For example, high school students currently account for 21 percent of total community college enrollments. This type of hybrid approach has been popularized by Jobs for the Future and other organizations under the name of "the big blur" which calls for erasing the education and training dividing line between high school, college, other training providers, and employers.

2. Make work-based learning the default, not the exception. Paid, structured work experience should be the center of early career preparation. This would include youth and adult apprenticeships, co-ops, clinicals, and employer-embedded bootcamps tied to hiring pathways. Where possible, programs such as apprenticeships should be organized with progressive responsibility and pay. For advanced roles, expand apprenticeship degree models that blend employment, mentorship, and academic credit so learners earn a recognized credential while accumulating on-the-job mastery.

3. Create skill adjacencies to speed transitions. Most workers aren't starting from zero. Map what knowledge they have and skills they can demonstrate to new job requirements and build education and training bridges to them. Use targeted modules to convert prior experience into marketable capability in weeks or months, not years. Help entry-level workers find launchpad jobs—73 roles like emergency medical technicians and power plant operators that propel individuals into careers that offer solid wages, job security, and upward mobility. This compresses time-to-competence and lowers cost for learners and employers.

4. Place performance-based hiring at the core. Replace resume proxies with job-relevant tasks such as structured work samples, simulations, supervised trial projects, and standardized rubrics that can be combined with AI-assisted scoring where appropriate. For example, data-analytics candidates should show they can clean messy data, build a basic model, check reliability, and explain tradeoffs to a non-technical stakeholder. Performance builds confidence and widens the opening to nontraditional talent.

5. Ongoing supports and post-placement mobility. Job placement is a milestone, not the finish line. Many displaced workers first need a "lifeboat" job, a stabilizing step that can become a waystation. Budget and plan for after placement with assistance like coaching, peer cohorts, on-the-job projects, tuition or upskilling stipends, and wage-triggered milestones that keep people moving from lifeboat to ladder. Measure and fund programs on mobility, not just initial placement.

6. Portable, machine-readable credentials with proof attached. Every credential should be modular and easy for employers to integrate into hiring systems. Wherever possible, attach verified artifacts such as code repositories, dashboards, clinical evaluations, supervisor ratings with clear proficiency levels. The T3 Innovation Network supported by the US Chamber of Commerce is an example of this, as is Western Governors University Achievement Wallet. Portability lets workers carry a "skills transcript" across platforms and employers.

7. Quality signals and accountability up front. Use evidence-based tools like the credential value indices mentioned earlier to separate the genuine signal from the noise. Public and employer funding should follow providers that can show labor market value, such as placement into launchpad roles, wage gains, advancement rates, and retention. Require transparent reporting on outcomes for providers and employers that includes creating a skills-first hiring approach. Then follow this up by scaling what works and fixing what doesn't.

8. Modernized safety net aligned to learning. Update unemployment insurance and related benefits to support rapid upskilling while job seeking. Pilot portable learning accounts and invest in regional learning and work hubs that include the hybrid institutions described earlier. These places should allow individuals to access broadband, AI tools, mentors, and employer-backed programs under one roof.

9. Employer partnerships that redesign entry-level roles. Ask employers to restructure entry-level jobs so they teach as well as produce. These restructured jobs should include projects that build in time for learning and mentorship and should have clear promotion ladders tied to demonstrated competencies. Apprenticeship and apprenticeship-degree models provide the template, with human resource departments, business units, and educators working to co-own role design and outcomes.

10. Data infrastructure that sees skill, not just seat time. Build interoperable maps between job tasks, competencies, and learning outcomes so curricula, including apprenticeship rotations, align with genuine work requirements. Share outcome data with providers to continuously improve pathways.

In short, a durable AI-era education and training model links learning to real work, validates what people can do and stays with the climb after the first job. As Ryan Craig argues in Apprenticeship Nation, apprenticeships are the clearest, most scalable way to make that model real.

Why This Matters Now

AI is here and changing how work gets done. This raises a simple question: Will it erase the first rungs of the career ladder or help us build new ones? We can choose the latter by pairing credible quality signals such as a credential value index and similar tools with performance-based hiring; by expanding community-anchored programs that stack and leverage skill adjacencies; and by funding post-placement supports that keep people climbing after the first job. Done together, a skills-first approach moves from rhetoric to reality. Workers gain verifiable transcripts of capability and employers hire with confidence from a broader pool.

The stakes are real. If we deploy AI only to peel off tasks and trim headcount, advantage will concentrate. But if we design roles so AI raises human capacity, more people will do consequential work sooner. That yields a labor market that can see skill, a training system that honors what people already know, and a culture that makes the "why" of hard work visible. That's how we replace the vanishing entry level with a better starting point and turn disruption into a fairer, faster ladder to opportunity.