AI has moved into the center of hiring with remarkable speed. According to SHRM's 2025 Talent Trends research, 43% of organizations now use AI in HR tasks, up from 26% a year earlier, and recruiting is the leading use case — 44% of those teams use AI to screen resumes. AI interviews are the natural next step, and for overloaded recruiting teams the appeal is obvious: more applications handled, less time per screen.
The catch is that candidates don't share that confidence. In a 2025 Gartner survey, only about 26% of candidates said they trust AI to evaluate them fairly.
When the people you're trying to hire don't trust the tool you're assessing them with, that gap doesn't stay hidden. It surfaces as candidates who disengage, drop out, or quietly decline, and it tends to lose you the ones who have other options. The same body of Gartner research points to the flip side: candidates are markedly more likely to apply when a process keeps a human in the loop.
The encouraging part is that candidates aren't rejecting AI outright. They're reacting to how it's used — silently, without explanation, and with no human in sight. This piece is about closing that gap: how to use AI interviews in a way that protects efficiency and candidate experience at the same time.
Why candidate experience becomes the real test for AI interviews
AI interviews usually enter a hiring process to solve a recruiter problem: too many applications, not enough hours. From the inside, that logic is sound. The trouble is that candidates experience the same tool very differently.
For a candidate, an AI interview can feel efficient and flexible — answer on your own schedule, no calendar tetris. It can just as easily feel opaque and impersonal: a countdown timer, a camera, and no way to tell whether a human will ever see your answers. The deciding factor isn't the technology. It's whether the process was explained.
This is why the experience matters more than it might seem. When candidates don't understand how AI affects the outcome, the uncertainty itself becomes the problem. Their concern usually isn't "a machine is involved" so much as "I don't know how this machine is judging me, or whether anyone will double-check it." That instinct is widely shared: in SHRM's research, 80% of workers said a human should review AI outputs before they're acted on. People are open to AI in the process and they want a person accountable for the decision.
The cost of getting this wrong lands squarely on metrics TA leaders already track. A confusing or cold AI interview drives drop-off, and drop-off is rarely random — strong candidates with competing offers are the first to leave. From there it flows downstream into lower completion rates, weaker pipelines, and softer offer acceptance. Candidate experience, in other words, is where the real return on an AI interview is decided.
What candidates need from an AI interview process
The fix starts with information. Most candidate frustration traces back to one thing: people were left guessing. Before they start an AI interview, candidates want straightforward answers to a short list of questions:
- Why is an AI interview being used at this stage?
- How long will it take, and what format are the questions?
- What information does it collect — and will it record audio, video, or generate a transcript?
- Does the AI score or screen me out, or does it only assist?
- Will a human review my interview before any decision?
- What happens if the technology fails mid-interview?
- Can I request an accommodation or an alternative format?
None of this is a burden to provide. Treated well, transparency is how you build trust — and given that the overwhelming majority of people expect a human to review AI outputs before they count, telling candidates where a recruiter stays involved directly addresses their biggest worry.
A few practical moves cover most of it. Explain the purpose of the AI interview directly in the invitation, and set expectations up front: how long it runs, the kind of questions, and what to expect on screen. Avoid vague phrasing like "your responses will be analyzed," which tells candidates nothing and quietly raises suspicion. Say plainly whether a recruiter will review the conversation. And give people a way out when something breaks — a support contact or an alternative path — so a frozen screen doesn't cost a good candidate the role.
How TA teams can design AI interviews that feel fair
Transparency sets expectations; design determines whether the process actually delivers on them. A handful of principles keep an AI interview fair in practice rather than just on paper.
Start with the questions. Keep them job-relevant, and resist anything overly personal or unrelated to the role fairness erodes fast when candidates can't see why they're being asked something.
Keep the questions consistent across candidates, too, so the experience doesn't quietly vary from one person to the next. When it comes to evaluation, lean on structured scorecards tied to defined criteria rather than an opaque AI ranking; a score a recruiter can interpret is far more defensible than a number no one can explain.
Then connect the pieces. An AI interview shouldn't live as an isolated tool off to the side. Its summary belongs in the same candidate profile as recruiter notes and interview feedback, so the full picture sits in one place and inconsistencies are easy to spot. Build in human review for senior roles and for any borderline case, and make that review a real step, not a rubber stamp.
Finally, measure whether it's working. Track candidate drop-off, completion rates, feedback, and offer acceptance for roles that use AI interviews, and revisit periodically whether the tool is genuinely improving hiring quality or just speeding up a flawed process. The data will tell you where the experience is costing you candidates.
This is also where an ATS does quiet, important work. The goal is to fold the AI interview into a complete candidate journey rather than bolt it on as a one-off. A platform like Moka helps by unifying candidate data, supporting structured feedback, automating the workflow around the interview, and surfacing the analytics above — so a TA team can watch efficiency, quality, and candidate experience together instead of trading one for another.
The throughline is simple. Candidates aren't walking away from AI; they're walking away from processes that leave them in the dark.
Tell people when AI is in the room, explain what it's measuring, keep a human accountable for the outcome, and an AI interview can do exactly what it was meant to do — without quietly draining your pipeline.
