More money is flowing into Environmental, Social and Governance (ESG) than ever. In 2021, investors poured $649 billion into ESG-focused funds worldwide, up 90% from the $542 billion invested in 2020. In the UK, over 21% of investors plan to back funds and companies with comprehensive ESG strategies by 2025. And in Australia, more than 55% of super funds are using responsible investment approaches to inform strategic asset allocation.
All this investment has prompted a sharper focus on social issues across major companies – the S in Environmental, Social, and Governance. The great news is that investment in the big S, in turn, means more money and attention toward progress in Diversity, Equity and Inclusion (DEI).
Executives who underscore the significance of diversity in hiring understand that an impactful DEI strategy must originate from the highest ranks – consider the Australian superannuation fund HESTA and its 40:40 vision as a prime example. However, for the strategy to be truly effective, diversity hiring ideas need to permeate all levels of the organization. It’s also critical to meticulously track and measure the extent to which we are achieving our diversity hiring goals to ensure real progress is made.
Both boards and shareholders want measurable change in DEI, and fast. According to a Harvard Business Review study of S&P 500 earnings calls, the frequency with which CEOs talk about issues of equity, fairness, and inclusion has increased by 658% since 2018. The momentum is clear, and expectations are that this will only increase further in the coming years.
Diversity goals need to be measurable, today
According to another HBR article, 40% of US companies discussed DEI in their Q2 2020 earnings calls, which is a huge step up from the 4% of companies that did the year before. And with 1,600 CEOs pledging to take action on DEI, setting goals and tracking progress remain top priorities.
DEI and ESG are big challenges, and we might take myriad possible approaches in trying to solve them. Some companies may start at the executive level (HESTA, as an example), while others may invest in partnerships and outreach programs. The spectrum of options can easily become overwhelming.
| “Interestingly, I’m just looking at our workforce profile and have been discussing the changes in diversity since we updated our recruitment approach last March. Not only have we hired three times more ethnic minorities and 1.5 times more women, but we now have twice as many LGBTQI+ colleagues in our business than we did three years ago! Other initiatives have played a part, but I’d imagine the game changer has been Sapia as we’ve had some direct feedback from a transgender colleague that they felt more confident with our recruitment process than they did in other applications! David Nally, HR Manager, Woodie’s UK |
So why not start with the people you bring into your company, at all levels? Why not begin with the way you attract, assess, and select talent?
With advanced conversational Ai, you can set realistic DEI targets and measure them comprehensively, ensuring access to the best talent from diverse backgrounds. A sophisticated Interviewer is not just another chatbot that operates on a fixed set of rules. For instance, our conversational Ai delves deep into interview responses to understand each candidate’s unique attributes in a fair and objective manner.
Our Smart Interviewer helps you track and meet these three key diversity goals.
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Gender bias
Our proprietary interview response database is made up of more than 500,000,000 words, enabling us to conduct the most sophisticated response analysis in the recruitment industry. We can do this on a macro scale (e.g. across countries, cultures, industries, and role types); or for individual companies.
Take these findings, combining data from a range of our customers, globally:
Figure 1: Gender stats across applicants, Ai recommendations and hired
Thanks to our machine-learning capabilities, and the vastness of our database, we can provide the hiring team with real-time analytics on the following diversity hiring goals:
- Number of observed female applicants vs number of expected female applicants (and the same for male applicants)
- Diversity smart goals examples such as the overall hiring rate of females vs males
- Overall rate of female vs male recommendations made by our Ai to meet diversity targets
By employing a smart interviewing Ai at the first stage of recruitment, we can prove progress with regards to inclusivity and bias reduction. These aggregate company data show that while the expected number of female applicants exceeded the number of those that actually applied, the number of recommendations made by our Smart Interviewer also beat expectations (effectively compensating for the top-of-funnel bias). We can also see that the rate of observed female hires far exceeded the expected number.
What does this indicate? With merely three metrics, you can discern the advancements made in your DEI recruiting goals – and if the performance doesn’t meet the mark, it’s evident at which stage the targets falter.
It is important to note that the recommendations of our Ai are based solely on its analysis of candidate responses in the chat-based interview. Its suitability criteria is based, among other factors, on HEXACO personality modeling and accurate assessments of various job related competencies such as team work, critical thinking and communication skills.
Our data also keep biases in check at each stage of the recruitment process, depending on the role type. As you can see, for all three roles, this company’s hiring outcomes were within regulatory limits (as stipulated by the US Equal Employment Opportunity Commission (EEOC)) across the three stages of their funnel: Applications received, recommendations made, and the hiring decisions ultimately made by the hiring team. The final step, it is important to note, happens independently of our Ai: It is a human decision. Despite this, the outcome data is recorded, so that the company can compare its outcomes against inputs and recommendations to see if late-funnel biases are occurring.
Figure 2: Role-type-based gender bias. Mid line represents zero bias. Shaded regions signify the tolerance range. Right of line favors females, while left favors males.
The feedback from candidates is extremely positive: Company A’s strivings for fairness and equality in its processes has resulted in a candidate satisfaction score of 98.7% for females, and 98.1% for males. Better still, the interview dropout rate across the board is less than 10%.
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Ethnicity bias
Parallel to gender, our ethnicity analytics equip hiring managers to efficiently set and accurately monitor diversity smart goals examples for ethnic representation in recruitment. Company A, as depicted in Figure 2, is pioneering in this respect: Its BAME (Black, Asian, and Ethnic Minorities) recommendation rate stands at 46.5%, outpacing expectations, while its non-BAME recommendation rate is at 37.1%.
Our data has also helped Company A to increase its hiring commitments for First Nations people: The rate currently sits at 4.5%, from 4,000 candidates, above the national average of 1.8% (2018-19). This number is expected to increase over the coming year.
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Personality biases
The data we collect helps us, as well as our customers, understand the extent to which personality determines role suitability and general workplace success. It also helps us to eliminate long-standing biases that negatively impact certain candidates, despite the fact that said candidates may be highly suitable to the roles for which they are applying.
For example, people high in trait agreeableness (compassionate, polite, not likely to dissent or proffer controversial viewpoints) tend to underperform in the traditional face-to-face interviews. Hiring managers may assume, based on this, that they are unable to lead, or are not a ‘culture fit’. However, a face-value assessment of agreeableness is not a reliable predictor of candidate potential. Only scientific analysis of HEXACO traits can make this call with accuracy.
Take these two visualizations, showing how different personality traits affect the recommendations made by our Ai. Females (red dot) and males (blue dot) are slightly different in agreeableness, but there is virtually no difference in their conscientiousness, a strong predictor of job performance. As a result of being able to measure conscientiousness accurately, our system can effectively allow for higher levels of agreeableness – or cancel out the negative face-value judgements typically made in face-to-face interviews. Despite these personality differences, as shown in Figure 1, Sapia Ai recommendations for both male and female groups remain similar (~40%). This results in a fairer chance for all, and a wider pool of candidates. In this case, this is to the benefit of females.
Figure 3: Male (blue) and Female (red) personality trait differences
Bringing it all together
The world is changing, and we can no longer continue to take a “We’ll see what happens” approach to the ‘S’ in ESG. Many investors are pushing companies for better diversity and inclusion outcomes. At Sapia, our data show that fair, scientifically valid, and explainable Ai can produce better outcomes for peoples of all genders and ethnicities. The companies that have adopted our Ai approach are seeing strong improvement in their own DEI practices and results.
Over and above assisting our clients, our commitment to DEI is embodied in a guiding vision of our own: Our FAIR Framework. This embeds an approach that ensures our systems and processes are ethical and transparent. Many similar Ai systems operate in a ‘black box’, providing little knowledge about how their algorithms help make important decisions or create issues like amplifying biases. We are committed to a fairer world, free of bias – and, with every candidate interviewed, our data is bringing us closer.
Related
Blog
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.