We see that AI replacing jobs is becoming a mainstream concern. A Harvard study tracking résumé and job posting data from 62 million U.S. workers across 285,000 firms found that junior employment began declining at AI-adopting firms in early 2023 and has continued falling.
The drop isn't being driven by layoffs, but rather by companies quietly hiring fewer entry-level people in the first place.
As teams see more AI workforce transformation, the productivity gains are visible. The workforce gaps forming underneath them are not—at least not yet. We’re also seeing adjustments and creations of new roles oriented towards AI specialization.
The Hidden Cost of the "Missing Middle"
While we see the conversations about AI impact focus on what gets automated, they rarely account for what gets lost in the process. Junior employees learn how businesses actually operate by handling foundational work repeatedly.
Let’s take financial reporting as an example: a first-year analyst putting together a monthly report is doing more than formatting numbers. They're noticing when a line item looks wrong, which variances leadership cares about, and which ones get waved off. Over time, that exposure builds pattern recognition and judgment that shows years later. They learn through the accumulation of context.
The negative impact of AI on employment becomes more apparent as teams become structurally top-heavy over time. Senior employees remain responsible for strategic decisions, while newer employees are expected to oversee AI-generated outputs without the operational experience needed to properly evaluate them, leading to a growing workforce skills gap.
Reducing entry-level hiring does not necessarily reduce oversight responsibilities but rather changes where the work happens. Senior employees spend more time reviewing AI-generated work, making it harder to transfer institutional knowledge.
Redesigning the Junior Role
The organizations adapting most effectively aren't treating this change as a choice between automation and workforce development. They're redesigning early-career roles around AI-supported learning, and that shift starts at hiring.
Staffing and screening processes need to evolve to assess AI literacy alongside technical competency. The strongest candidates are the ones who can:
- Recognize missing context
- Identify flawed outputs
- Understand when human judgment is required
- Critically evaluate AI-generated work
- Auditing which entry-level tasks have been automated and what learning exposure disappeared with them
- Redesigning junior roles around judgment work rather than execution work
- Building hiring filters that assess AI literacy and critical thinking rather than just technical familiarity
That means moving beyond static résumés and keyword matching toward scenario-based screening.
Balancing Efficiency with Long-Term Development
To be fair, some of the work that's disappearing probably should. Few employees benefit from spending hours manually reformatting spreadsheets or moving data between disconnected systems. The challenge is that many organizations are treating task automation and AI skills development as the same conversation when they're not.
Entry-level jobs and AI will keep evolving, and organizations that thrive over the next decade aren't necessarily the ones that automate fastest. They're the ones that figured out how to preserve the exposure that entry-level work used to carry growth. For organizations looking to get that balance right, the starting point is usually three things:
CTG works with organizations across all three areas, aligning workforce planning, staffing strategy, and technology to support operations and long-term growth. As AI adoption reshapes how organizations build teams, opening paths for skill development will remain important for everyone involved.
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