Not all interview questions are created equal. If you’re a veteran recruiter or talent acquisition manager, this may sound obvious, but the reality is that many companies (and their hiring managers) run interviews like an informal conversation, in which so-so questions are selected at random, and candidate responses are recorded haphazardly.
This has led to a general lack of confidence in interviewing as a practice: An Aptitude Research and Sapia.ai report from 2022 found that 33% of companies aren’t confident in the way they interview, and 50% believe they’ve lost talent due to their processes. Statistically speaking, it’s likely that your company or clients fit into these cohorts.
There are two proven fixes to shoddy interviewing: The right structure, and the right questions. Before we share our list of interview questions as recommended by our personality scientists, let’s quickly cover the structured interview and its benefits.
Why you should only be using structured interviews
Many (if not most) of us believe that resumes and past experience are the best indicators of future employee performance. Surprisingly, that’s not true: The best way to find top candidates (with 26% of predictive success) is the structured interview; conversely, past job experience accurately predicts just 3% of an employee’s on-the-job performance.
When you run AI-enhanced structured interviews, you devise a consistent set of questions that are asked to all candidates without any variation. Responses to questions are generally entered into a rubric, and are then scored against consistent criteria. The benefits of structured interviews are numerous, but the crucial reason for using them, especially in the context of AI interviews, is that you’re minimising variables; all candidates are compared fairly to one another.
Check out this quick video to learn more about the benefits of structured interviewing.
Our steadfast belief that past experience dictates the future, while understandable, has led to the proliferation of academic questions that trap and stymie candidates, mainly because they’re based on difficult concepts that are near-on impossible to articulate without preparation.
For example, it’s not uncommon for marketing candidates to be asked something like, “Tell me how you’d adjust ROAS (Return on Ad Spend) numbers to compensate for unexpected reduction in impression share”. Should the candidate know what ROAS and impression share are? Sure. Can you reasonably expect them to solve an unexpected puzzle while you stare them down? No.
Instead, you want to ask questions that speak to behaviours. And, ideally, you want a reliable framework for assessing responses so you can adequately analyse behaviours (more on this later).
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Don’t ask: Tell me how you’d respond if [specific situation] occurred.
Instead, ask: Tell me about when something went wrong at work, and you had to fix it. How did you go about it?
This is similar to the marketing example above. Although it’s tempting, you want to avoid questions that attempt to gauge technical proficiency. Such questions are best saved for pre- or post-interview assessments.
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Don’t ask: Why do you want this job?
Instead, ask: If you could do anything in the world, what might it be?
No one has ever had an original response to the question of ‘why’. Responses are laden with bias, and most hiring managers expect some grandiose (and frankly, sycophantic) response. Conversely, the question of ‘if’ is far more telling – it speaks to a candidate’s openness, which is a valuable (and measurable) trait for problem-solving.
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Don’t ask: What would your former manager say about you?
Instead, ask: Describe a time when you struggled to build a relationship with someone important. How did you eventually overcome that?
Former managers are not reliable character witnesses when their feedback is relaid second-hand by the candidate. You’ll never get a true response here. Instead, try to understand how a candidate overcomes difficult relationships. Answers will clue you in to a candidate’s levels of emotionality, extraversion, and empathy.
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Don’t ask: What is your biggest weakness?
Instead, ask: Tell me about a situation in which you have had to adjust to changes over which you had no control. How did you handle it?
Candidates will not accurately describe their weaknesses to you. As with the questions above, you’re asking a question that will result in a biased sample. Instead, ask about a common point of pressure that we all experience in working life. If someone is adaptable and flexible to change, their responses will show you – and you’ll have learned something much more valuable.
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Don’t ask: What is your biggest strength?
Instead, ask: How would you change an institutional “this is always how we do it” attitude, if you felt there was a better approach?
These questions don’t seem interchangeable on the face of it, but it’s far more valuable to know how a candidate can use their skills and behaviours to propel your company forward, than to know how they’d big-up themselves. Legacy thinking is a company-killer, and you want people who can think laterally to solve problems with new solutions. So find out if they can do that.
Go beyond great questions
These questions will serve you well in your interviews, but at a certain point, they’re only as good as your interview structure and analysis capabilities. Sapia’s Ai Smart Interviewer is designed to ask these questions – plus hundreds of others – and assess candidate responses to build accurate behavioural profiles. This means you get high quality candidates without having to bother with a long and inconsistent interview process.
Want even more questions designed by our people scientists and proven in the field? Download our HEXACO job interview rubric for free, here.
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New Research Proves the Value of AI Hiring
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.
The problem with traditional psychometric tests
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.
The Rise of Chat-Based Interviews
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.
Key Findings from the Latest Research
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.
Why Trust and Candidate Agency Win
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.
Blog
AI Maturity in the Enterprise
Barb Hyman, CEO & Founder, Sapia.ai
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.
You don’t need more pilots. You need a maturity model.
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:
The 5 Stages of AI Maturity (for real enterprises)
- Curious
- Awareness is growing across leadership
- Experimentation led by innovation teams
- Risk is unclear, appetite is cautious
- AI is seen as “tech”
- Reactive
- Gen AI introduced via vendors or tools (e.g., copilots, agents)
- Some pilots show promise, but with limited scale or guardrails
- Data privacy and sovereignty questions begin to surface
- Risk is siloed in legal/IT
- Capable
- Clear policies on privacy, bias, and governance
- Dedicated AI leads or councils exist
- Internal use cases scale (e.g., summarisation, scoring, chat)
- LLMs integrated with guardrails, safety reviewed
- Strategic
- AI embedded in workflows, not layered on
- LLM/data infrastructure is regionally compliant
- AI outcomes measured (accuracy, equity, productivity)
- Teams restructured around AI capability — not just tech enablement
- AI-Native
- AI informs and transforms core decisions (hiring, pricing, customer service)
- Enterprise builds proprietary intelligence
- FAIR™/RAI principles deeply operationalised
- Talent, systems, and leadership are aligned around an intelligent operating model
Why this matters for enterprise leaders
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.
Blog
Beyond the Black Box: Why Transparency in AI Hiring Matters More Than Ever
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.”
Metrics That Make Transparency Real (and Actionable)
🕒 Time to Hire
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.
💬 Candidate Sentiment, Advocacy & Verbatim Feedback
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.
🔍 Drop-Off Rates, Funnel Visibility & Automation That Works
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.
📈 Hiring Yield (Hired / Applied)
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.
🧠 AI Effectiveness: Score Distribution & Answer Originality
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.
From Metrics to Momentum
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.