There’s a confidence crisis quietly building at the bottom of the job market — and AI is at the centre of it.

New data from ICIMS, released this week, paints a striking picture of where entry-level candidates stand in mid-2026. The headline figure: only 19% of entry-level job seekers say they feel “very confident” in their careers. Nearly three in ten — 29% — report low or no confidence at all.

This is not a soft sentiment story. It’s backed by hard hiring numbers that tell the same tale.

What the Data Actually Shows

ICIMS draws on proprietary data from more than 3 million global platform users and over 691 million candidate profiles. Their May 2026 Workforce Report reveals a hiring market that looks fine on the surface but is deeply mismatched underneath.

In April 2026, job openings hit a 12-month peak — 15% above baseline. Yet application volume dropped 10%. Hiring velocity flatlined at zero percent growth. Entry-level openings specifically jumped 18%, but entry-level applications fell 9%, and actual hires rose just 3%.

More jobs. Fewer applications. Near-zero growth in who actually gets hired.

That gap is not a coincidence.

The 54% Problem

The most telling finding from the ICIMS report: 54% of entry-level job seekers believe employers now expect mid-level experience from people applying for entry-level roles.

Think about that. More than half of young workers starting their careers believe the bar has already moved against them — before they’ve had the chance to gain any experience at all.

AI is the reason. As businesses adopt AI tools that compress timelines, automate routine tasks, and handle what used to be “junior” work, the baseline competency expected of new hires has shifted. Employers aren’t just hiring entry-level any more — they’re hiring entry-level workers who can already navigate AI tools, understand data, and add value from day one.

AI Is Changing What Entry-Level Even Means

78% of entry-level job seekers aged 18 to 24 say they believe AI and automation are changing the volume and nature of entry-level roles. They’re right. What they don’t yet know is how to respond.

The response is not to be scared of AI. The response is to understand it — and to build the skills that make you genuinely useful alongside it.

Data literacy used to be a nice-to-have for most roles. It’s becoming a baseline expectation — and organisations that invest in it systematically outperform those that don’t. The ability to work with AI tools, interpret outputs, ask the right questions of data, and bridge business needs with technical capabilities — that’s the new entry-level competency floor.

A candidate who comes in knowing Power BI, understanding how to prompt AI tools effectively, and able to interpret data to answer a real business question is not just “entry-level with AI skills.” They are, in practical terms, a mid-level contributor on day one.

That’s the gap the report is pointing to — and it’s exactly the gap that training can close.

What This Means for Business Leaders

If you’re hiring right now, the squeeze goes both ways. You want candidates who are already AI-capable. Candidates are frustrated because you’re expecting experience they haven’t had the chance to build yet. The result is open roles, falling applications, and stagnant hire rates.

A few things worth considering:

Stop waiting for AI-ready talent to appear. The market will not supply enough of it fast enough. Businesses that invest in training their existing teams — and that partner with hire-for-attitude, train-for-skill approaches — will outcompete businesses waiting for perfect CVs.

AI skills need to be developed deliberately. They don’t emerge on their own. A junior analyst who knows Excel and has some SQL experience will not automatically become AI-capable without structured development. The gap needs active investment.

Data literacy is the foundation. Before any AI tool can be useful, the person using it needs to understand what they’re trying to learn from data, how to read outputs critically, and how to distinguish good analysis from confident noise. This is teachable. It takes time and the right curriculum.

What This Means for Early Career Workers

If you’re early in your career, the ICIMS data describes a real frustration. But it also points at the clearest possible path forward.

The 78% of young workers who recognize AI is changing their landscape are not wrong to be concerned. They’re right about the problem. The question is what they do about it.

The people who will close the gap are those who go on the offensive — who build data and AI skills before being asked, who demonstrate capability before being hired, who show up already knowing how to think analytically and use modern tools.

That’s not a five-year plan. With focused, practical training, it’s a six-to-twelve month shift. The window is open. But it won’t stay open indefinitely.

What This Means for Enterprise DNA

At Enterprise DNA, we’ve spent years watching this pattern develop. The 220,000-plus professionals we’ve trained have consistently told us the same thing: the moment they understood data — really understood it, not just the tools but the thinking — their careers changed.

The ICIMS data is not surprising to us. It’s confirmation of what we’ve seen happening at the individual level, now showing up at scale across the hiring market.

If you’re a business leader trying to build AI-capable teams, we can help. If you’re an early career professional who recognizes the gap described in this report and wants to close it, we can help with that too.

The skills exist. The training is available. The market will reward those who move first.

Explore EDNA Learn’s data and AI courses — built specifically to develop the kind of practical, applicable capability the market is now demanding.