AI is Changing Resumes. Here’s What Employers Need to Know

It’s worth being precise about what’s actually changed, because the shift isn’t candidates inventing experience from nothing. It’s something subtler, and in some ways harder to spot. AI tools are extremely good at taking a thin or average bit of experience and dressing it up to sound senior, structured and impressive, without the substance underneath moving at all.

Polished Language, Inflated Claims

The tell isn’t usually one glaring lie sitting in the middle of the page. It’s a pattern across the whole document. Every bullet point reads like it was lifted from the “ideal candidate” section of a job ad rather than written by someone describing their own week. Responsibilities get framed in outcome language, “drove improvements,” “delivered results,” “managed end-to-end processes,” without any of the specifics that would let you judge whether that’s actually true. A resume that says someone “led a high-performing team through a period of significant operational change” might be describing a genuine achievement. It might also be describing two weeks of covering a coworker’s shifts. On paper, both sentences look identical.

There’s also a quieter pattern showing up at volume that’s worth knowing about, even if it never makes it into a conversation with a single candidate. A growing number of resumes coming through now sound oddly similar to each other in structure and word choice, because they were shaped by the same small handful of tools using similar prompts. One resume like that tells you nothing unusual. A stack of them starting to sound interchangeable is a sign the document in front of you might be telling you more about the software than the person who used it.

The Gap Between What’s Written and What’s True

The actual risk here isn’t dishonesty so much as distance. Most candidates using these tools aren’t trying to deceive anyone. They’re trying to present themselves well, the same as anyone going for a job has always tried to do. The problem is that AI-smoothed language can describe a skill or an achievement with total confidence regardless of how solid the experience behind it actually is, because the tool has no way of knowing the difference. It optimises for how something sounds, not whether it’s true.

That gap stays invisible right up until someone is asked a direct, specific question about what they wrote. Asked to walk through the “operational improvement” they led, or describe exactly what they did with the equipment they listed, the answer either holds up in detail or it doesn’t. A candidate with the real experience will usually go deeper than the resume did, because they’re drawing on something they actually lived through. A candidate whose resume did most of the talking tends to fall back on the same vague phrasing that’s already sitting on the page in front of you, because there’s nothing further behind it to draw on.
This is the part that matters most for anyone hiring off a resume and a short conversation.

The gap is real, it’s increasingly common, and it almost never shows up unless someone is having the kind of conversation built to find it.

None of what’s been covered so far is a real problem if every candidate gets a proper, in-depth conversation with someone who knows what to listen for. The cost only shows up once screening starts relying on the resume by itself, or on a short phone call to fill in the rest. That’s a fairly common setup for a lot of Brisbane businesses, particularly smaller ones without a dedicated HR function, and it’s exactly the setup AI-assisted resumes are best at slipping through.

Phone Screens and Keyword Filters Miss the Same Things

A standard phone screen tends to run through a fairly predictable list. Confirm availability, confirm the basics of the role, ask one or two questions about experience, wrap up in fifteen minutes. It’s an efficient way to filter out people who are clearly unsuitable, but it was never designed to test whether the experience on the resume is as solid as it sounds. There usually isn’t time, and the questions asked are often general enough that a well-prepared answer, real or not, gets through without much friction.

Keyword-based filtering runs into the same wall from a different direction. A system built to scan for the right terms, the right years of experience, the right phrase matching the job ad, rewards a resume that contains those words. It has no way of telling whether the experience behind those words is genuine or generously interpreted, and AI tools are, almost by definition, extremely good at making sure a resume contains the words a filter is looking for.

The two processes that were supposed to be doing the screening, the quick call and the keyword scan, are now both being tested against documents specifically optimised to pass them.

The cost of this rarely shows up at the point of hire. It shows up a week or two later, when the person who sounded confident on the phone and looked strong on paper turns out to need far more supervision, training or correction than expected, and the business is left absorbing that gap mid-project rather than catching it beforehand.

Why It Matters More for Licensed and Ticketed Roles

This problem gets sharper, not softer, once you move into trades, logistics and manufacturing hiring. For a lot of office-based roles, an inflated resume mostly risks a slow start and a bit of extra training. For licensed and ticketed work, the same gap carries a different kind of weight, because the resume isn’t just describing a skill level. It’s implicitly vouching for a qualification that either exists or doesn’t.

A confidently written line about forklift experience doesn’t tell you whether the licence behind it is current, expired, or was completed years ago with no real time on the machine since. A resume claiming welding experience doesn’t tell you which actual ticket someone holds, or whether they’ve worked unsupervised versus alongside someone more senior the whole time.

These are exactly the kind of details AI-polished language is bad at preserving, because the tool is optimising for how confident and capable the sentence sounds, not for the specific, checkable facts sitting underneath it. The more impressive the writing gets, the easier it becomes to lose the detail that actually matters.

Getting this wrong doesn’t just produce an awkward first week. In a licensed role, it can mean someone arriving on site without the qualification the work legally requires them to hold, and that’s a problem that surfaces the moment they’re asked to do the job, not during the interview that was meant to catch it.

Everything in this article so far points to the same conclusion. A resume can sound confident without being accurate, a phone screen can confirm the basics without testing anything underneath them, and the roles where this matters most are exactly the ones where getting it wrong has real consequences, not just an awkward first week. This is the part of the process that’s built to close that gap, and it’s the one thing that hasn’t changed while everything around it has.

No Automated Keyword Filtering

Every application that comes through Youngbrook gets read by a person, every time, regardless of how many come in. Nobody’s resume is filtered out by a keyword match or scored by an algorithm before a human being has actually looked at it. That’s not a small operational detail. It’s the difference between a process built to find the right person and a process built to process volume quickly.

A recruiter who’s spent years placing people in warehouses, workshops and industrial sites reads a resume differently to a system scanning for matching terms, because they know what the work actually sounds like described honestly. They can tell the difference between a resume that mentions reach truck experience because someone genuinely spent six months on one most days of the week, and a resume that mentions it because someone did a single induction shift two years ago and an AI tool turned that into a confident-sounding bullet point.

A keyword filter has no way to make that distinction. It just sees the right word in the right place and moves on. A person who knows the industry sees the same word and starts asking the question that actually matters: when, how often, and on what.

This works in the candidate’s favour just as often as it protects an employer. Plenty of genuinely capable people, particularly tradespeople and operators who are better with their hands than with a laptop, write a fairly plain, undersold resume. Not because their experience is thin, but because writing confidently about themselves has never been the part of the job they cared about. A keyword filter punishes that person for not phrasing things the way an algorithm wants to see them phrased. A recruiter who actually reads the application doesn’t.

Every Resume Reviewed by Someone Who Knows the Role

The real value in having a person do this work isn’t that they’re smarter than software. It’s that they know the industries they’re recruiting for well enough to tell a rehearsed answer from a real one, and well enough to ask the kind of question that actually exposes the difference.

In practice, that starts before a candidate is even on the phone. Our recruiters know what genuine experience in a given role actually sounds like, because they’ve placed dozens of people into that exact kind of work and spoken to the employers who took them on. That history matters. It means a conversation with a candidate isn’t a generic interview script run the same way for every applicant. It’s shaped by knowing what good answers in this specific trade or role tend to sound like, and what answers tend to be filling a gap.

Licences and tickets are verified properly, not taken on trust because a resume states them clearly and confidently. A document claiming a current qualification and an actual current qualification aren’t always the same thing, and that’s not a distinction either side can afford to discover after someone’s already turned up to start.

Where something written doesn’t quite match what a candidate says when they’re actually talking it through, that gets worked out and understood properly before they’re ever put forward to an employer. Sometimes it’s nothing, a simple gap in how something was phrased. Sometimes it’s the exact thing that would have caused a problem on site in week one. Either way, it gets resolved on our side, by someone equipped to recognise it, rather than landing on an employer’s desk as a surprise.

It’s a slower way of doing things than running a search across a database of resumes. It’s also exactly why the candidates who do reach you have already had that gap tested by someone who knew what to look for, instead of you finding out about it the hard way, on the job, after the hire’s already been made.