A candidate attended an interview for an entry-level role at a video game company. The chief of staff didn’t ask any questions about skills or experience – just small talk, a vibe check, and two generic prompts about the company.
When the candidate asked for feedback, the interviewer revealed she had no say in who moved forward: An AI system had recorded the entire conversation and would score it based on personality impressions.
Stories like this are now ubiquitous, exposing a growing gap between how hiring teams think AI improves the candidate journey and how candidates actually experience it. When interviews stop feeling human – or answers are treated like keyword slop – candidates lose confidence before you’re anywhere near an offer.
This disconnect is why we’re flipping the lens: exploring why AI-heavy hiring feels confusing or dehumanizing, where trust breaks, and what candidates say would make the process feel fairer and clearer.
Then we’ll look at how AI, used the right way, can speed communication, add structure, evaluate actual skills, and finally deliver the clarity candidates have been asking for.
How the AI-driven candidate journey feels today
If you know anyone actively seeking a job, they’ll tell you that they assume AI is screening them. They may not know how, when, or even why, but any trust that they’re dealing squarely with a 100% human screening process is gone.
In fact, a recent Gartner survey shows that more than 50% of candidates believe AI screens them, and just 26% trust it's fair.
Quiet rejection
One job candidate made it to the final round with a company she was excited to work for. She felt they had a great rapport and that her interviews were solid, but then they ghosted her. Weeks passed, and she still didn’t know whether she was actually rejected or simply forgotten.
In such situations, as Andrey Nemolyakin, Founder of Lunara AI, says, “Candidates are less upset about rejection than about not knowing why.” They’ve researched your company, reflected on their skills and experience, and rearranged their schedules so they can prove they can help your business succeed. When you invite them to invest that time and then fail to acknowledge their efforts, it’s not only discourteous – it leaves candidates feeling used rather than evaluated.
At least with a clear rejection, candidates can choose to grow from the experience based on your feedback. But without giving candidates closure, you’re leaving them with no understanding of what skills mattered or how to improve next time.
Unfortunately, AI can make ghosting more likely. When hiring teams use multiple disconnected tools – an ATS here, an interview bot there, with an automated email tool on top – no one owns communication during the candidate journey. And when you let AI absolve you from the responsibility to be transparent, you risk undermining trust in your organization.
Speed without respect
But more AI means a speedier, more efficient hiring process… right?
Candidates read it differently.
Joaquin Rodriguez, Sales and Marketing Director of Stay In Costa Rica, argues that quick decisions only feel good when candidates understand what’s happening.
He says, “The prevailing opinion that hiring speed will improve hiring processes overlooks how candidates perceive the time between [applying and] receiving feedback as a lack of respect as opposed to simply moving quickly.”
Speedy rejections signal to candidates that maybe nobody read the CV they spent hours preparing, or the system filtered them out automatically based on keywords they didn’t guess correctly.
That means candidates are just as unsettled by rapid rejections as they are by long delays or even ghosting. Without visibility into how hiring decisions are being made, speed looks like indifference – especially if an application is rejected within hours or even minutes.
Sorting candidates instead of understanding them
And it’s not just a matter of respect versus indifference. When hiring teams hand over the candidate journey to AI, candidates fear they will likely be misunderstood.
Research by Cameron Piercy, Associate Professor of Communication Studies at the University of Kansas, shows that when applicants are told an algorithm is involved, but they aren’t given any context, trust actually drops further.
“I think it really comes to the forefront when a machine's making a decision about you,” he says. “All of a sudden, you're like, ‘Machines don't get humans. They don't understand what my internship was worth, what I’ve written. Only a human could truly get me.’ I think what the data shows is that people are OK with algorithms making decisions – up to a certain point.”
When candidates feel that point has been crossed, they shift from “Ghosting is unfair” to deeper concerns. Did anyone actually look at their application? Has a human seen and understood what their experience actually means, or is the whole process automated?
If your candidate journey doesn’t answer these questions, candidates won’t have confidence in your organization. They’ll be suspicious of it. This also goes beyond rejection. For example, candidates might be able to accept that they’re not a good fit for the role. But if they feel unseen, it leaves them believing your process wasn’t designed for people like them, their effort doesn’t matter, and there’s no point engaging further.
Whether that’s literally true matters less than the emotional takeaway. If candidates can’t feel humans behind the scenes in their candidate journey, they assume there aren’t any.
How to use AI to improve the candidate journey
Yes, AI can turn hiring into a frustrating black box that approaches candidates as keyword generators. But should you opt out of AI altogether?
Actually, many candidates would still pick the right kind of automation over the slow, messy hiring processes they’re used to.
Here’s how AI can improve the candidate journey and hiring outcomes when used properly.
Giving screening a reliable structure
AI is making screening processes that were once unpredictable more reliable. For example, in contrast to the black-box experiences some candidates complain about, TestGorilla’s AI-powered resume scoring includes human oversight, ensuring fairness with clear criteria that hiring teams set ahead of time. Teams can see, review, adjust, and override those scoring criteria at any point. Personal details such as names, universities, and career gaps are also stripped before scoring, so the AI focuses only on signals relevant to the role. No single model decides the outcome or introduces quirks only it can see; instead, multiple large language models evaluate each resume. Teams can also create their own questions in skills tests or pull from AI-recommended ones.
But while it reduces some of the randomness of early screening, is AI really ready to interview job applicants?
Unlike traditional interviews, where one candidate might get a thoughtful, prepared interviewer and the next faces someone glancing at notes between meetings, AI interviews can keep the candidate experience consistent.
And research shows that candidates actually respond positively to AI interviews when they offer clear instructions and consistent scoring. Many candidates even prefer them to unstructured human interviews, as these often unfold like casual conversations without questions directly tied to the responsibilities of a role.
Helping candidates choose where to spend their effort
Some AI tools already help applicants understand whether they’re actually a fit before they pour hours into tailoring a resume.
Haider Alleg, Founder of Kainjo, has seen this in action: His HR platform uses match scoring and automated rankings to assess how well a candidate’s skills and experience match a specific job description.
This is where skills testing can also be a big help. Teams using TestGorilla’s talent assessments, for example, start with short, job-relevant tasks candidates can actively complete, like our role-specific skills tests.
AI then pitches in by:
Checking answers against objective rubrics
Flagging patterns instead of filtering people out invisibly
Supporting human judgment instead of replacing it
Using AI sensibly with human oversight to evaluate candidates’ actual skills helps job seekers avoid wasting time on “positions that are too junior or too senior,” says Alleg.
Candidates can also get information about salaries, benefits, and “colleagues they could reach out to chat with if interested or the type of project they will deal with beyond the job description,” he says.
Starting with clear expectations
Candidates want to know what actually matters in a job application. For instance, are you interested in how they approach interpersonal conflict at work or what their favorite programming languages are?
As Andrey Nemolyakin points out, a little honesty goes a long way. “Even highly qualified candidates report anxiety and frustration when they suspect they were filtered out by criteria they never saw or understood,” he says.
Setting clear expectations can be as simple as adding a couple of sentences to the job posting:
“Applications are reviewed based on skills and relevant experience, not keywords or resume formatting.”
“All applicants complete a short task related to the job as part of the first screen.”
These signals help candidates prepare for an actual task instead of rewriting their resume for the sixth time to sneak in the “right” jargon.
If your role uses assessments, naming them (even at a high level) also helps:
“You’ll complete a 15–20 minute skills test within 48 hours of applying.”
“We use a short writing sample/video response/logical reasoning exercise depending on the role.”
“Our AI video interviews score responses to structured questions to learn your actual abilities.”
When companies tell candidates exactly what to expect, where AI is used, and what’s being evaluated, it gives candidates the confidence they’ll get a fair shot in the hiring process.
Freeing up time for feedback
Just because a company is using AI doesn’t mean the feedback has to be vague or nonexistent. In fact, a fully manual screening process often can’t give feedback because there simply isn’t time.
Milos Eric, Co-Founder of OysterLink, explains. “Previously, if an organization turned someone away for a role, they may have communicated their decision simply with ‘No,’ he says. “By leveraging candidate assessment tools, organizations can provide additional information to help candidates develop further.”
According to Eric, it’s about dignity. Even a sentence of explanation from a real person helps remove a candidate’s fear that they’re being misjudged by AI. And AI explainability – the ability to understand why AI made a particular decision – gives hiring teams the freedom to send faster answers with context: a message about which skills mattered, or what career growth looks like.
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A faster and fairer candidate journey with TestGorilla
AI can speed up your hiring process without sending candidates on a less-than-ideal hiring journey.
By being transparent about how you use AI and ensuring human oversight, you can score resumes, evaluate written and video responses, and compare applicants consistently, without eliminating human control over the process.
With TestGorilla, you see every score, understand exactly how it was generated, and can override anything that doesn’t feel right.
The result is a faster, fairer candidate journey. Ready to see what a candidate-approved AI hiring process looks like in practice?
Book a free demo or create your TestGorilla account today.
Contributors
Andrey Nemolyakin, Lunara AI, Founder
Joaquin Rodriguez, Stay In Costa Rica, Sales & Marketing Director
Haider Alleg, Kainjoo, Founder
Milos Eric, OysterLink, Co-Founder
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