Barb Hyman, Founder and CEO of Sapia.ai – Interview Series

PUBLISHER ARTICLE PREVIEW

From the original article

Barb Hyman, Founder and CEO of Sapia.ai is an experienced business leader whose career has spanned law, management consulting, marketing, learning and development, human resources, and technology. She began her career as a solicitor at Herbert Smith Freehills before moving into consulting with Boston Consulting Group, where she progressed to Project Leader and later returned in senior roles overseeing learning and development, HR, and marketing across Australasia.

Hyman also served as Head of Marketing and Sponsorship at the Museum of Contemporary Art in Sydney and as Executive General Manager of People & Culture at REA Group. She founded Sapia.ai in 2018, drawing on her experience in organizational leadership and talent management to rethink how companies assess and hire people using artificial intelligence.

Sapia.ai is an AI-powered hiring technology company focused on structured, conversational candidate assessment. Its platform uses chat-based interviews to evaluate candidates consistently at scale, helping employers assess skills and suitability while reducing reliance on traditional CV screening and other signals that can introduce bias. Sapia.ai says its technology has assessed more than 10 million candidates and combines structured assessment science with AI-powered talent intelligence, personalized candidate feedback, and integrations with existing applicant tracking and HR systems.

The company also emphasizes explainability, fairness, and enterprise compliance, with its broader platform spanning AI interviews, job analysis, interview scheduling, talent intelligence, and AI-powered career coaching.

Before founding Sapia.ai, you built a career spanning consulting at Boston Consulting Group, law, marketing, learning and development, and eventually People & Culture leadership at REA Group. What experiences from that journey convinced you that hiring needed to be fundamentally rethought, and what problem did you originally set out to solve with Sapia.ai

We rely on CVs. We don’t see the whole person. We make our most consequential decisions — who to hire, who to promote — on gut feel and proxies. Mirror hiring is rampant: since when did the school or university someone attended become a proxy for intelligence? It’s not. It’s a proxy for advantage.

Some of this relates to my personal story. I’m an immigrant and I know I landed in a fortunate category of immigrant: I’m white and I have a name people find easy to say. I come from a family where nobody went to university; my dad was a shopkeeper. And yet I’ve had extraordinary opportunities and five careers later, here I am.

When I think about the two billion workers in the world, I think about how many of them have exactly the talent, the drive, the critical thinking I’ve been credited with, but are never seen because we’re blinded by the prism of our own experience. Bias is a wonderful shortcut for making a fast decision, and it’s exactly why hiring keeps rewarding the already-advantaged.

It never felt fair or rational.

The other half of the story came from consulting. After my MBA, I moved into a world where you never walked into a boardroom and made a recommendation without real research behind it. And yet the highest-stakes decision any company makes (who joins, who leads, who shapes the culture) is still made on gut, or on a proxy as thin as a university name.

People aren’t on the balance sheet, but in professional services especially, they are the asset. That disconnect seemed absurd to me.

The CV itself hasn’t fundamentally changed since Leonardo da Vinci invented the format. Wikipedia lists more than 180 documented human biases. A third of North American candidates who finish a job application are left without an answer two months later. None of that is a talent problem: talent is equally distributed but opportunity is not.

What I set out to fix with Sapia wasn’t just a decision — it was from the experience of looking for a job. My team laughs at me for this, but I’ve always thought of hiring like dating. It’s a relationship. You’ll spend more time with the people at your job than with your partner.

So why would you make that decision on anything less than the same rigor — the data — we insist on for every other decision that matters?

You’ve argued that most organizations approach AI adoption as an efficiency project when it should begin as a trust project. What does “trust” actually look like in practice, and how should companies measure it alongside more traditional metrics such as productivity, cost savings, and adoption?

I learned this as a CHRO: if you’re not transparent about why you’re making a change, trust is the first casualty. And I think most companies get AI adoption backwards. They measure the productivity gain, e.g. the hours saved, the cost per hire, but they never stop to measure the thing that actually determines whether any of it sticks: do your people trust how you’re using AI, and why?

Trust is the substrate of your culture. It’s not a soft metric sitting next to the real ones — it’s the thing the real ones are built on. Think about it the way you’d think about any relationship. High trust gives you degrees of freedom. Low trust makes everything smaller, less buoyant, less relaxed.

And nobody does their best work when they don’t feel safe. Organizations are no different.

Yet so much of what’s been done with AI in the workplace has actually weaponized people’s own data against them, for instance scraping someone’s social media to infer whether they’re a good hire, a good risk, a bad risk. I genuinely cannot imagine the meeting where that decision got made, or how anyone thought it wouldn’t corrode culture the moment people found out.

So if you’re serious about AI adoption, trust has to be a metric from day one, sitting right alongside productivity and cost savings. Ask people directly: do you understand why we’re using this? Do you believe it’s being used fairly? If you can’t answer yes to both, the efficiency gains you’re celebrating are borrowed against a culture you’re quietly spending down.

One of the biggest concerns around AI in hiring is that models can learn from historical employment data and potentially reinforce the same human biases organizations are trying to eliminate.