For recruiters and hiring managers, the fear of being ‘displaced’ by AI is real. There is discomfort in change and the unknown, and at Sapia.ai we work daily with organisations to reframe how AI is perceived by hiring teams.
We know that technology like Sapia.ai doesn’t displace, it elevates. But how to manage that elevation with your team is critical. Bringing a team of recruiters and potentially an entire organisation of hiring managers on a journey of change requires you to enable them to envision what’s possible for them in the new world. (Check out how we do that here).
Our customers know that leveraging tools like Sapia.ai to take care of the ‘grunt work’ of recruiting – the screening, the shortlisting, the scheduling – enables recruiters to become strategic business partners. They have much more time to focus on building and enabling strategic functions like advisory, sourcing, and performance analytics.
But what does it mean for a recruiter to elevate their role to this strategic level? What can they be doing day to day, with all this extra time?
Here are some practical examples of things that recruiters can finally start doing when they’re no longer shuffling through mountains of applications.
Enhancing Employer Branding and Employee Value Proposition (EVP)
EVP improvement: Focus on understanding and communicating what makes your workplace attractive. This means conducting employee surveys, focus groups, and market research to enhance the EVP and stand out in the highly competitive retail job market.
Strengthening employer branding: Spend time collaborating with marketing and HR to develop campaigns that showcase your company’s culture, benefits, and growth opportunities. This would attract top talent who align with your values, reducing turnover by attracting the right candidates from the start.
Developing a Talent Pipeline and Workforce Planning
Proactive talent pipeline: Create a more robust talent pipeline, focusing on identifying high-potential candidates for future needs, not just immediate hires. This would involve partnering with schools, training programs, and community organizations to ensure a steady flow of candidates. Leveraging technology like Sapia.ai to assess potential talent for soft skills will help you build these pipelines in a data-driven way.
Workforce planning: Collaborate with department heads to forecast staffing needs based on sales trends, seasonal spikes, and other factors, ensuring we have a ready supply of talent at the right times. This reduces the “emergency” hiring that contributes to high turnover and reduces your stress levels when it comes to seasonal hiring peaks.
Improving Candidate Experience
Streamlining the recruitment journey: With time freed up from manual tasks, you’re able to take a step back to review your end-to-end candidate experience—from application to onboarding. Do candidates feel informed, valued, and respected at every step? Building a seamless experience will lead to better retention and referrals. It’s important to note that this should always be done when bringing a new tool like Sapia.ai into your hiring process, considering how a new assessment or interview experience can enable enhancements to other parts of your process. Ongoing, having the capacity to review the process and understand what is working and what can be enhanced ensures you stay on top of your game.
Personalized interactions for high-value candidates: Because Sapia.ai provides you with a shortlist of the top candidates automatically, you can invest your time in ensuring the high-potential candidates receive personal outreach and interaction, offering a human touch to differentiate your company from competitors.
Data Analysis and Recruitment Metrics
Leveraging analytics: Invest time in analyzing recruitment data—such as time-to-hire, candidate quality, and turnover rates. This allows you to continuously refine your sourcing strategies and ensure you’re hiring candidates who are a better long-term fit. At Sapia.ai, data like this is provided to all customers, leveraging data from your ATS and augmenting it with data collected from our interviews to provide insight into key business metrics.
Turnover reduction strategies: By analyzing patterns in the data, you can develop initiatives to address the reasons behind high turnover. For example, this could involve identifying roles or locations with the highest turnover and implementing specific retention strategies for those areas.
Learning and Development Initiatives
Collaborating with L&D teams: Partner with the Learning and Development department to identify skills gaps in the frontline roles and suggest programs to help new hires succeed and stay with the company longer; and ensure that your new assessment capability is assessing for those skills.
Upskilling managers: Help your hiring managers to be better equipped to engage and retain frontline staff by implementing leadership development programs, focusing on coaching and managing retail workers effectively.
Diversity and Inclusion (D&I) Initiatives
D&I recruitment strategies: Focus on building a more diverse workforce by creating targeted outreach programs that attract underrepresented groups. You could partner with community organizations, diversity job boards, and internal employee resource groups to widen your prospective applicant pool and ensure inclusivity in your sourcing strategy.
Bias-free hiring process: By default, an AI you are using to screen or assess candidates should be inclusive and remove bias from the screening process. At Sapia.ai we provide diversity metrics to our customers that give visibility of potential hiring bias, all the way to the store level. Leverage this type of data to understand where improvements can be made and work with your business stakeholders to remove potential biases and ensure a diverse hiring practice.
Stakeholder and Relationship Management
Building stronger relationships with stakeholders: You can invest more time in fostering relationships with department managers, understanding their unique needs, and tailoring recruitment strategies to fit their teams. This ensures that hires align better with specific team dynamics and culture.
Partnering with HR and Operations: Collaborate more closely with the HR and Operations teams to improve the overall employee lifecycle experience—from onboarding to performance management to retention strategies.
Innovating Sourcing and Retention Strategies
Exploring new recruitment channels: Experiment with new ways to source candidates—such as social media campaigns, employee referral programs, and local community engagement efforts.
Retention-focused recruitment: Implement hiring practices that focus not just on filling roles quickly but also on finding people who are likely to stay long-term, aligning better with the company’s goals and values. When working with a tool like Sapia.ai, you are more likely to hire the people who belong with you, as they’re being assessed directly for potential role-fit at the first step of the hiring process.
Creating an Effective Onboarding Process
Onboarding program review: Take a deep dive into improving your onboarding process, making it more engaging, informative, and aligned with your company culture. This ensures that new hires feel supported and prepared, increasing their likelihood of staying.
By shifting focus from administrative tasks to strategic initiatives, you’ll create a more efficient recruitment process while significantly impacting retention rates, employee engagement, and long-term business success.
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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.