This Recruiter Says How You Write and What You Write Can Tell an Employer a Great Deal About Your Fit.
Barbara Hyman believes the most important skill for people looking for a job in the post-COVID world will be the ability to write.
“People who think clearly, write clearly,’’ says the chief executive of the artificial intelligence-powered recruiting firm Sapia, which judges its candidates on the most basic of skills.
The firm, which has big-name backers including Myer family member Rupert Myer, former Aconex founder turned venture capitalist Leigh Jasper, fund manager Dion Hershan and former JB Were partner Sam Brougham, gives every job candidate a first interview by asking them five text-based behavioural questions on their phone that take around 20 minutes to answer.
Then the company’s predictive models assign a “suitability” score to each candidate using over 80 features extracted from their responses and the system specifically precludes the use of names, gender and age to determine the recommended shortlist, removing unconscious bias from the recruitment process.
The company’s technology has already been used by 400,000 candidates looking for roles in retail, healthcare, customer service, hospitality, contact centres and graduate and trainee roles across 34 countries.
Its clients include Qantas, Bunnings, the Iceland Group grocery chain in the UK and Spanish telecoms giant Telefonica.
But Hyman says her biggest target client in the post-COVID world is government.
She believes the economy can only be sustainably reactivated through large-scale job security and that requires redeploying existing skillsets to meet in-demand industries.
“This requires a sophisticated and scaleable solution to find jobs for those whose industries have been decimated by the pandemic and have no jobs to return to. Our solution can immediately activate these job seekers into the new economy, steering them to the jobs they will be good at, she says.
She claims if the government activated this sort of technology for a range of growth industries the economic and social impact would be unprecedented.
“In a healthy economy, the cost benefit in Australia alone is $1bn net benefit (cost) for every 100,000 workers that get back to work one month earlier through reduced welfare payments and increased consumer spending. That is significantly higher when accounting for government subsidies as a result of COVID,” she says.
“A big part of getting back to work is the confidence and the mindset. We are exploring different avenues to allow people to use our chat bot to find their true role in the new economy. This is the vision we are trying to sell to government – you have your own personalised career coach that helps you find the ideal role.”
Hyman said one of the company’s big-name backers Rupert Myer, the chair of the Australia Council for the Arts and an emeritus trustee of The National Gallery of Victoria, had given her “amazing introductions” into the government and university sectors.
“When I came into the business in February 2018 it was running out of money. I had to get a bunch of the existing investors to support me,’’ says Hyman, a former chief human resources officer at REA Group and a human resources and marketing director at Boston Consulting.
Her data science leader at Sapia is Sri Lankan-born Buddhi Jayatilleke, who has a diverse background in machine learning, software engineering and academic research.
The firm has raised $4m in the past 2 years, including bringing in Australian global recruitment and talent management firm Hudson as a strategic investor last year.
“That gave us credibility because the number two recruitment firm in the market believes in what we are doing,’’ Hyman says.
“Whether you like it or not, there is enormous amount we can learn about you in 200 words. Just the very fact we don’t use any secret or behavioural data, you have to build trust from the beginning with your candidate. The completion rates are 95 per cent, the engagement rates are 99 per cent. But the key point is when we give you back your feedback. It is effectively a public service we are performing with this feedback.”
One of the firm’s initial backers was Rampersand, the venture capital firm which has a focus on early growth stage tech businesses.
Rampersand co-founder Paul Naphtali says the firm invested in Sapia for its ability to put data at the centre of a company’s people strategy.
“It’s a massive challenge for a start-up to aggregate the data and build the algorithms that can identify an individual’s suitability to a role quickly and accurately. It was a bold and ambitious plan from the beginning, and Sapia is now well on its way to becoming that data-centric engine,’’ he says.
“The company started with working to turbocharge the recruitment process by quickly identifying the right talent for the right roles.
“It’s taken time to build the tech and the data sets, but it’s paying off as a number of Australia’s leading companies now have Sapia as a default part of the process.”
He says the firm is now entering a new phase “where it also powers internal people management as well as for job seekers, which is obviously very relevant in the current environment”.
Recently in London Sapia was awarded the TIARA Talent Tech Star which honours the businesses globally in the talent acquisition industry.
Source: DAMON KITNEY, The Australian, October 30, 2020
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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.
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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.
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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.