To find out how to use Recruitment Automation to hire faster, reduce bias and save, we also have a great retail industry eBook on Ai in HR.
AI RECRUITMENT TOOLS
Artificial Intelligence. AI recruitment tools. Machine learning. Conversational AI recruiting. Chatbots.
Artificial Intelligence (AI) and AI recruitment tools are no longer just buzzwords in the realm of HR. Machine learning and chatbots, especially in the realm of conversational AI recruiting, are reshaping many industries, including HR.
While the possibilities of technology always felt like some distant future, there’s no denying that AI’s impact on recruiting within HR is evident now. Every day, technology touches and enhances our lives in ways we rarely even pause to think about. Machine learning recruitment tools, algorithms, apps, and digital automation continue to redefine how we shop, connect, bank, and more, including how HR departments operate.
It’s changed the ways we access customer services and the ways we can connect with our tribes across social platforms. In the age of COVID-19, AI-powered recruitment tools have even enabled ways of efficient remote working that few thought could be possible, proving to be a game-changer for HR professionals. AI in HR is not just about automating tasks; it’s about enhancing the human experience, making recruitment processes more efficient, and enabling a more connected and adaptive workforce.
Recruitment and Ai – We’ve only just begun
With the rise of automation and artificial intelligence (AI) across every industry sector, how AI is changing the game for recruiting is evident. AI recruitment tools are now rapidly reshaping the essential functions of hiring. These tools serve 3 key functions in the hiring process: sourcing, screening, and interviewing of candidates. Employing the latest advances in AI-powered recruitment, machine learning, and big data practices is delivering new efficiencies and better outcomes for businesses, recruiters, and candidates alike.
Text and chat interview automation with conversational AI recruiting
Conversational AI recruiting is a type of Ai that lets businesses have dynamic and meaningful conversations at scale with customers, staff, business partners and candidates.
Conversational Ai uses Natural Language Processing (NLP), a sub-field of Ai that’s focused on enabling computers to understand and process human languages. Through machine learning recruitment, it aims to get computers closer to a human level of understanding of language.
Conversational Ai uses NLP to discern meaning from both written and spoken word:
- Voice-activated systems – NLP is used in digital assistants you’re probably familiar with: Siri on iPhone, Google Home or Amazon Alexa, for example. These follow instructions to play music, to control connected devices throughout the home, find web-based information and resources and more. On an enterprise-level, you’ll be familiar with voice-driven customer service over the phone.
- Text driven systems – online or on mobiles, chat text discerns meaning in the written word.
How conversational AI recruiting tools are changing the recruitment conversation
Sometimes referred to as chatbots or textbots, Ai-based conversational tools, part of the suite of AI recruitment tools, continue to evolve and be applied in new and extraordinary ways. Our own peer-reviewed research shows how personality traits can be accurately inferred from answers to standard interview questions captured via a text chat.
AI recruitment works best in high volume recruitment such as customer-facing retail or service team roles. Conversational AI recruiting can be helpful for profiling personalities in candidates or existing employees without the time and costs of conducting lengthy psychometric profiling.
Conversational Ai can be helpful for profiling personalities in candidates or existing employees without the time and costs of conducting lengthy psychometric profiling. Add video into the mix and machine learning can add additional layers of meaning through analysis of facial expressions and profiles, body language and more.
Video interviewing continues to divide opinion as many believe it allows for unconscious (or not so unconscious) bias to remain front and centre of the hiring process. In text-based Ai interviews, many of the usual bias cues or triggers an be effectively eliminated at the candidate screening stage.
Automated interviews support remote working and remote hiring
In a post-COVID or COVID-normal economy, AI-powered recruitment tools will be pivotal. As more people compete for potentially fewer jobs, finding and engaging the best candidates will be even more challenging.
Ai-powered interviews can help recruiters cast their net wider to reach a bigger pool of candidates and find better-qualified candidates.
Mobile-first puts the power in candidates’ hands
People know text and are comfortable with text. So by providing a text chat-based mobile-first experience for candidates, improves the user experience and addresses communication challenges.
Chat-text provides an easier and less confronting interview process for many candidates.
Everyone has a story that’s bigger than their CV and Ai recruitment interviews give every candidate an opportunity to tell theirs. Candidates can choose when and where they complete their interview and standardised interview questions ensure a level playing field for all candidates.
Sapia’s text chat interview automation is blind screening at its best. We’ve removed possible factors that can influence human bias – no CVs, no socials, no videos, no facial recognition and no time limit. It’s just the candidate and their text answers, providing a fairer and richer experience where candidates feel comfortable just being themselves.
Recommender systems – How AI recruitment tools support people making people decisions
One of the most well-known applications of Ai, data science and machine learning recruitment is Recommender systems or Recommender engines.
In hiring, Recommender Systems use predictive modelling to recommend the most-likely best matches of applicants for a role.
Recommender systems guide decision-making by using machine learning to analyse all the data available through the HR lifecycle. From job advertising and clicks, through interviewing and hiring, to employees’ job satisfaction and tenure, data can be analysed to reveal predictive patterns and insights.
Data can find connections that humans don’t, providing valuable insight into what an ideal candidate looks like or where you’re likely to find them.
Predictive intelligence draws a picture of your ideal candidate
Recommender systems can cut through the ‘noise’ by providing a shortlist of top-ranked candidates. This is without burning time, sorting and reviewing potentially hundreds or even thousands of applications. Predictive intelligence shares additional insights on candidates’ values, traits, personality and communication skills. It helps to simplify the selection and guide faster talent decisions.
Machine learning is not infallible. One important consideration is questioning whether the data being used in machine learning recruitment is not inherently biased. It’s always important to have real people making decisions about people.
Interviews – it’s not where you finish, it’s where you start
Reviewing CVs of all candidates can be the most time-consuming part of a recruiter’s job. Especially for large-scale briefs such as retail or customer service teams. In defining a shortlist of potential candidates to proceed to the interview stage it can be hard to differentiate between CVs. It’s also easy to make decisions that may be based on personal biases.
But what if you could start the hiring process with all the benefits of an interview process, without investing your time in them? And what if in the time it would take to properly review just a handful of CVs, thousands of candidates could be screened by interview?
With Ai recruitment tools you can.
Five top ways conversational Ai tools are changing the recruitment game
When it comes to recruiting and hiring, the ability to read the mood as well as the words is a game-changer in candidate assessment. Here are our top five benefits for your business:
1.Reducing time to hire, improving the quality of candidates with AI-powered recruitment.
Without even having to consider CVs upfront, an upfront screening interview reduces time to hire by providing a shortlist of candidates with the best fit to move forward.
Ai interview automation looks beyond the CV to assess the skills, traits and temperament of candidates. Based on past hires, Ai learns what a successful candidate for your business looks like and joins the dots to find others that match that profile.
Recruiters and hirers can save time reviewing and assessing CVs. With the ability to complete briefs faster, build teams sooner and achieve business metrics, you can be on to the next job sooner. Or free yourself to concentrate on what you do best: building relationships, delivering a better hiring experience or enhancing the onboarding process.
2. Reduce bias & build diversity
Ai-enabled interviewing helps reduce the effects of unconscious bias – the inherently human prejudices, personal preferences, beliefs and world-views that shape our assessment of others. Our biases can easily have a negative impact on candidates and mean you’re potentially missing out on the best candidates for the job. It can also mean employers are missing the opportunity to cultivate workplace diversity and all the benefits it delivers.
Diversity improves employee productivity, retention and happiness. Time and again, research shows that diversity – of background, gender, experience and more – improves employee productivity, tenure and job satisfaction. In 2020, global management consulting company McKinsey confirmed that “The most diverse companies are now more likely than ever to outperform non-diverse companies on profitability”.
3. Cut costs of every hire
Companies that have automated part of their candidate screening and interviewing are not only reaping the benefits of a more streamlined and stress-free process but report an immediate pay-off in time and efficiency savings.
Get your time back quickly and reallocate budgets towards higher-value investments and automation in other areas of recruiting.
Calculate your RoI on interview automation
Use Sapia’s free calculator to:
- Calculate your costs of hiring
- Calculate the costs of your annual turnover
- See the financial benefits of using automation for hiring
- Avoid unnecessary revenue losses
4. Increase the productivity of every recruiter and hirer
Everyone has one part of their job that they could do better if they had more time. Like managing stakeholders. Improving business partnership skills. Or networking to improve talent pools with a focus on those high-end and hard-to-fill roles. Whatever yours might be, interview automation can give you back time to focus on high-value tasks.
Reviewing CVs and managing interviews might not be the biggest challenge in your role, but they are likely to be the most time-consuming. Automate those upfront interviews using the tools and process of Ai recruitment and you can focus on the bigger picture of finding the best fit for every role and meeting every brief with confidence.
5. Give candidates a better experience
While this one’s last on our list of the benefits of Ai interview automation, it could equally be the most important.
Ever since job boards hit the market, recruiters have been inundated with candidate applications. While that’s been good news for potential employers as well as recruiters, it’s not so good for candidates. Too often candidates make the effort to apply for a position, but then due to the sheer volume of applications they never hear a thing from the recruiter or hirer. It’s called “ghosting” for obvious reasons.
Ghosting is not just a bad look, it can be bad for business. Candidates can easily share a negative experience on social media. They may also be less inclined to apply again or accept a job offer now or in the future.
With interview automation, you can turn every candidate engagement into an efficient, empowering and enjoyable experience.
About Sapia
Sapia’s award-winning interview automation offers a mobile-first, text chat interview. At scale, it delivers an engaging and relatable, in-depth interview, followed up with personalised feedback for every candidate. Here’s how Ai automation provides a superior experience to a traditional interview process:
- A familiar and accessible, mobile-first text experience
- No confronting questions or videos
- Candidates can be themselves, completing questions related to role attributes where and when it suits
- Blind-screening at its best with no gender, age or ethnicity revealed
- Candidates are motivated by personalised feedback, insights and coaching tips… and the opportunity to provide their feedback on the process
Find out more about Sapia’s Ai-powered recruitment tool and how we can support your recruitment needs today.
You can try out Sapia’s Chat Interview right now, or leave us your details to get a personalised demo
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.