It seems that using AI could consign fantastical or over-optimised resumes to the dustbin of history, along with the Rolodex and fax machines.
But how do we go about selecting the perfect (or as close to perfect as possible) candidates from AI-created shortlists?
It should be so easy to learn how to conduct an AI interview that adds the human element to the AI selection process. The web is awash with opportunities to earn recruitment qualifications from a variety of bodies, both respected and dubious, especially focusing on AI interview techniques. There are so many manuals, guides, and blog posts on the best ways of interviewing, including the integration of AI for interviews. People have been interviewing people for hundreds of years, but now AI is adding a new dimension to this age-old practice.
And yet…
We’ve all heard about bizarre interview questions (no explanation needed). We’ve felt the pain of people caught up in interview nightmares (from both sides of the desk). And we’ve scratched our heads and noses over the blogs on body language in face-to-face interviews(bias klaxon).
Even without the extremes, people have tales to tell. Did you ever come away from an interview for your ideal job, where something just felt wrong?
It’s clear that adding human interaction to the recruitment process is by no means straightforward. Highlighting these recurring problems doesn’t solve the underlying question, which is:
“We’ve used an algorithm to better identify suitable candidates. How do we ensure that adding the crucial human part of hiring doesn’t re-introduce the very biases that the algorithm filtered out?”
Searching for “Perfect interview Questions” gives 167,000,000 results. Many of them include the Perfect Answers to match. So it’s not simply about asking questions that, once upon a time, were reckoned to extract truthful and useful responses.
Instead we want questions that will make the best of that human interaction, building on and exploring the reasons the algorithm put these candidates on the list. Our questions need to help us achieve the ultimate goal of the interview: finding a candidate who can do the job, fit with the company culture AND stay for a meaningful period of time.
It’s generally agreed that we get better interview answers by asking open questions. I’d expand on that. They should ideally be questions that don’t relate specifically to the candidate’s resume, or only at the highest level, to get an in-depth understanding.
We should try to avoid using leading questions that will give an astute candidate any clues to the answers we’re looking for. And we should probably steer clear of most, if not all, of the questions that appear on those lists of ‘Perfect Interview Questions’, knowing that some candidates will reach for a well-practised ‘Perfect Answer’. We want them to display their understanding of the question and knowledge of the subject matter. Not their ability to recall a pre-rehearsed answer.
And so, we need to remember that we’re looking for the substance of the answers we get, not the candidate’s ability to weave the flimsiest material into an enchanting story.
So, here are some possible questions to get you thinking.
Of course, you’ll need to frame and adjust those questions to match the role and your company.
AI equips recruiters with impartial insights that resumes, questionnaires and even personality profiles can’t provide. Well-constructed, supervised algorithms overlook all the biases that every human has. And that can only be a good thing.
Statistically robust AI uses an algorithm, derived from business performance and behavioural science, to shortlist candidates. It can predict which ones will do well, fit well and stay. We can trust it to know what makes a successful employee, for our particular organisation and this specific role. It can tell us to invest effort with the applicants on that shortlist. However unlikely they seem at first glance.
So we can use all of our knowledge and skills to understand a candidate’s suitability and look beyond things that might have previously led us to a rejection.
AI is the recruiter’s friend, not a competitor. It can stop us wasting time chasing candidates who we think will make great hires but instead fail to live up to the expectation. And it can direct us to the hidden gems we might have otherwise overlooked.
Technology like AI for HR is only a threat if you ignore it.
Don’t be that company that still swears by dated processes because that’s the way it’s always been done. The opportunity here is putting technology to work, helping your organisation evolve for the better. The longer the delay, the harder it will be. So don’t be left at the back playing catch-up.
There are very few businesses these days that communicate by fax machines – and that’s for a reason. In a few years, you’ll look back and wonder “Why didn’t we all embrace Artificial Intelligence sooner?”
A new study has just confirmed what many in HR have long suspected: traditional psychometric tests are no longer the gold standard for hiring.
Published in Frontiers in Psychology, the research compared AI-powered, chat-based interviews to traditional assessments, finding that structured, conversational AI interviews significantly reduce social desirability bias, deliver a better candidate experience, and offer a fairer path to talent discovery.
We’ve always believed hiring should be about understanding people and their potential, rather than reducing them to static scores. This latest research validates that approach, signalling to employers what modern, fair and inclusive hiring should look like.
While used for many decades in the absence of a more candidate-first approach, psychometric testing has some fatal flaws.
For starters, these tests rely heavily on self-reporting. Candidates are expected to assess their own traits. Could you truly and honestly rate how conscientious you are, how well you manage stress, or how likely you are to follow rules? Human beings are nuanced, and in high-stakes situations like job applications, most people are answering to impress, which can lead to less-than-honest self-evaluations.
This is known as social desirability bias: a tendency to respond in ways that are perceived as more favourable or acceptable, even if they don’t reflect reality. In other words, traditional assessments often capture a version of the candidate that’s curated for the test, not the person who will show up to work.
Worse still, these assessments can feel cold, transactional, even intimidating. They do little to surface communication skills, adaptability, or real-world problem solving, the things that make someone great at a job. And for many candidates, especially those from underrepresented backgrounds, the format itself can feel exclusionary.
Enter conversational AI.
Organisations have been using chat-based interviews to assess talent since before 2018, and they offer a distinctly different approach.
Rather than asking candidates to rate themselves on abstract traits, they invite them into a structured, open-ended conversation. This creates space for candidates to share stories, explain their thinking, and demonstrate how they communicate and solve problems.
The format reduces stress and pressure because it feels more like messaging than testing. Candidates can be more authentic, and their responses have been proven to reveal personality traits, values, and competencies in a context that mirrors honest workplace communication.
Importantly, every candidate receives the same questions, evaluated against the same objective, explainable framework. These interviews are structured by design, evaluated by AI models like Sapia.ai’s InterviewBERT, and built on deep language analysis. That means better data, richer insights, and a process that works at scale without compromising fairness.
The new study, published in Frontiers in Psychology, put AI-powered, chat-based interviews head-to-head with traditional psychometric assessments, and the results were striking.
One of the most significant takeaways was that candidates are less likely to “fake good” in chat interviews. The study found that AI-led conversations reduce social desirability bias, giving a more honest, unfiltered view of how people think and express themselves. That’s because, unlike multiple-choice questionnaires, chat-based assessments don’t offer obvious “right” answers – it’s on the candidate to express themselves authentically and not guess teh answer they think they would be rewarded for.
The research also confirmed what our candidate feedback has shown for years: people actually enjoy this kind of assessment. Participants rated the chat interviews as more engaging, less stressful, and more respectful of their individuality. In a hiring landscape where candidate experience is make-or-break, this matters.
And while traditional psychometric tests still show higher predictive validity in isolated lab conditions, the researchers were clear: real-world hiring decisions can’t be reduced to prediction alone. Fairness, transparency, and experience matter just as much, often more, when building trust and attracting top talent.
Sapia.ai was spotlighted in the study as a leader in this space, with our InterviewBERT model recognised for its ability to interpret candidate responses in a way that’s explainable, responsible, and grounded in science.
Today, hiring has to be about earning trust and empowering candidates to show up as their full selves, and having a voice in the process.
Traditional assessments often strip candidates of agency. They’re asked to conform, perform, and second-guess what the “right” answer might be. Chat-based interviews flip that dynamic. By inviting candidates into an open conversation, they offer something rare in hiring: autonomy. Candidates can tell their story, explain their thinking, and share how they approach real-world challenges, all in their own words.
This signals respect from the employer. It says: We trust you to show us who you are.
Hiring should be a two-way street – a long-held belief we’ve had, now backed by peer-reviewed science. The new research confirms that AI-led interviews can reduce bias, enhance fairness, and give candidates control over how they’re seen and evaluated.
It’s time for a new way to map progress in AI adoption, and pilots are not it.
Over the past year, I’ve been lucky enough to see inside dozens of enterprise AI programs. As a CEO, founder, and recently, judge in the inaugural Australian Financial Review AI Awards.
And here’s what struck me:
Despite the hype, we still don’t have a shared language for AI maturity in business.
Some companies are racing ahead. Others are still building slide decks. But the real issue is that even the orgs that are “doing AI” often don’t know what good looks like.
The most successful AI adoption strategy does not have you buying the hottest Gen AI tool or spinning up a chatbot to solve one use case. What it should do is build organisational capability in AI ethics, AI governance, data, design, and most of all, leadership.
It’s time we introduced a real AI Maturity Model. Not a checklist. A considered progression model. Something that recognises where your organisation is today and what needs to evolve next, safely, responsibly, and strategically.
Here’s an early sketch based on what I’ve seen:
AI is a capability.And like any capability, it needs time, structure, investment, and a map.
If you’re an HR leader, CIO, or enterprise buyer, and you’re trying to separate the real from the theatre, maturity thinking is your edge.
Let’s stop asking, “Who’s using AI?”
And start asking: “How mature is our AI practice and what’s the next step?”
I’m working on a more complete model now, based on what I’ve seen in Australia, the UK, and across our customer base. If you’re thinking about this too, I’d love to hear from you.
For too long, AI in hiring has been a black box. It promises speed, fairness, and efficiency, but rarely shows its work.
That era is ending.
“AI hiring should never feel like a mystery. Transparency builds trust, and trust drives adoption.”
At Sapia.ai, we’ve always worked to provide transparency to our customers. Whether with explainable scores, understandable AI models, or by sharing ROI data regularly, it’s a founding principle on which we build all of our products.
Now, with Discover Insights, transparency is embedded into our user experience. And it’s giving TA leaders the clarity to lead with confidence.
Transparency Is the New Talent Advantage
Candidates expect fairness. Executives demand ROI. Boards want compliance. Transparency delivers all three.
Even visionary Talent Leaders can find it difficult to move beyond managing processes to driving strategy without the right data. Discover Insights changes that.
“When talent leaders can see what’s working (and why) they can stop defending their strategy and start owning it.”
What it is: The median time between application and hire.
Why it matters: This is your speedometer. A sharp view of how long hiring takes and how that varies by cohort, role, or team helps you identify delays and prove efficiency gains to leadership.
Faster time to hire = faster access to revenue-driving talent.
What it is: Satisfaction scores, brand advocacy measures, and unfiltered candidate comments.
Why it matters: Many platforms track satisfaction. Sapia.ai’s Discover Insights takes it further, measuring whether that satisfaction translates into employer and consumer brand advocacy.
And with verbatim feedback collected at scale, talent leaders don’t have to guess how candidates feel. They can read it, learn from it, and take action.
You don’t just measure experience. You understand it in the candidates’ own words.
What it is: The percentage of candidates who exit the hiring process at different stages, and how to spot why.
Why it matters: Understanding drop-off points lets teams fix friction quickly. Embedding automation early in the funnel reduces recruiter workload and elevates top candidates, getting them talking to your hiring teams faster.
Assessment completion benchmarks in volume hiring range between 60–80%, but with a mobile-first, chat-based format like Sapia.ai’s, clients often exceed that.
Optimising your funnel isn’t about doing more. It’s about doing smarter, with less effort and better outcomes.
What it is: The percentage of completed applications that result in a hire.
Why it matters: This is your funnel efficiency score. A high yield means your sourcing, screening, and selection are aligned. A low one? There’s leakage, misfit, or missed opportunity.
Hiring yield signals funnel health, recruiter performance, and candidate-process fit.
What it is: Insights into how candidate scores are distributed, and whether responses appear copied or AI-generated.
Why it matters: In high-volume hiring, a normal distribution of scores suggests your assessment is calibrated fairly. If it’s skewed too far left or right, it could be too hard or too easy, and that affects trust.
Add in answer originality, and you can track engagement integrity, protecting both your process and your brand.
To effectively lead, you need more than simply tracking; you need insights enabling action.
When you can see how AI impacts every part of your hiring, from recruiter productivity to candidate sentiment to untapped talent, you lead with insight, not assumption. And that’s how TA earns a seat at the strategy table.