A new survey published this week puts a specific number on a trend that many businesses have been feeling but not fully quantifying.
The headline finding: 48% of hiring managers at U.S. companies with 101 or more employees would rather invest in AI tools than hire and train a recent college graduate.
Nearly half the people making entry-level hiring decisions — saying they would bypass human headcount for AI capability. The survey, conducted by ResumeTemplates.com via Pollfish with 1,000 hiring managers, was published June 3, 2026.
The Numbers Behind the Shift
The survey does not just capture a preference. It documents a structural change already underway.
55% of companies have already shifted entry-level hiring budget toward AI tools. This is not a future intent — it is an allocation that has happened. 45% have restructured so that one senior employee, working with AI, does the work that used to require multiple entry-level hires.
And 35% of hiring managers say they will not hire 2026 graduates at the same volume as the class of 2025. That breaks down to 18% planning to hire fewer, 5% planning to hire none at all, and 12% who have not committed either way.
The industry breakdown is striking. In technology, 65% of hiring managers prefer AI over a new graduate. In finance, the figure is 56%. Government sits at the other extreme, with only 20% of government hiring managers making the same call.
Why Hiring Managers Are Making This Trade
The survey asked hiring managers to rank the advantages of AI tools over new graduate hires. Faster onboarding came out on top at 61%. Reliable output was second at 55%. Around-the-clock availability followed at 52%, and lower cost was cited by 48%.
None of these are abstract concerns. New graduates take time to get up to speed, require management attention, and produce variable quality work during the learning curve. AI tools have predictable behaviour, can be deployed immediately, and are available at any hour.
The calculation changes when the human brings demonstrable skills that AI cannot replicate. The businesses shifting entry-level headcount to AI are making this trade specifically in role categories where the work is routine, well-defined, and describable in a prompt.
What This Actually Means
This is not a story about AI replacing humans across the board. It is a story about AI replacing undifferentiated labour.
Entry-level workers feeling the squeeze are those in roles where the work can be described in a prompt — where the inputs are known, the process is defined, and the output is predictable. AI handles that very well.
Entry-level workers finding opportunities are those who bring skills that immediately raise their contribution above what AI alone can deliver: data analysis, the ability to interrogate an AI output and know whether it is right, critical thinking about ambiguous problems. These are not soft skills. They are measurable, teachable capabilities.
The 55% of hiring managers who shifted budget to AI still have the same business problems to solve. They just changed how they solve them. The senior employees who remain are under pressure to do more with less — and that creates demand for capable people who can direct AI effectively, fill the judgment gap, and review what the tools produce.
What This Means for Business Leaders
If you are a business leader, this data confirms what many teams are already doing informally. The question is whether that shift is being managed strategically.
Redirecting entry-level hiring budget to AI tools is a reasonable short-term efficiency move. But it creates a skills gap downstream. Who improves the workflows when results are not good enough? Who trains the models on new data? Who catches the confident errors that AI produces without flagging? Those responsibilities fall to existing team members who also need to be developed.
The most effective answer is not AI or humans. It is humans who know how to work with AI — and organisations that invest in building that capability rather than assuming it will arrive with the next hire.
If you are an early career professional reading this survey data, the window for differentiation is skills-based. The gap between a candidate who can use AI tools effectively and one who cannot is widening fast, and it is becoming the primary filter in competitive roles.
The skills that make the difference — data literacy, analytical thinking, AI fluency — are learnable. They do not require a computer science degree. They require focused, structured practice with the right curriculum.
EDNA Learn’s data and AI courses are built for exactly this moment: practical, applied training in the skills that move professionals from replaceable to genuinely valuable in an AI-augmented workplace.
If you are a business leader investing in your team’s capability, we can help you build that across your organisation — not with theory, but with courses built around real tools and real business problems.
The market is shifting. The businesses and professionals who move first on skills will be the ones best positioned when the dust settles.