To find out how to improve candidate experience using Recruitment Automation, we also have a great eBook on candidate experience.
New insights from Aptitude Research suggest recruitment automation can play a much greater role in talent acquisition than just improving efficiency for hiring managers, it can also make the interview process more human for candidates.
The research shows that when you shift the focus from an employer-driven view to a candidate-first view, then it is possible to reduce bias in hiring and improve the overall human element of talent acquisition.
For most companies, the value of automation is perceived through the recruiter and hiring manager experience, with the benefits to the candidate often ignored. However, recruitment automation has to be about more than simply moving candidates through the process quickly to have any significant benefit to a company.
When you focus on the impact and experience of the candidate, the benefits to both recruiters and candidates can significantly improve through recruitment automation. This approach has given rise to a movement called humanistic automation technology.
But humanistic automation sounds like an oxymoron right? Is it even possible?
The Aptitude Research showed not only is this possible, but that when Ai is used this way, it creates personal connection at scale, and works to reduce bias, something no other technology or even human-centred solution can deliver.
So, how exactly does it do this?
There have been some slight improvements in building connections through the hiring process recently, but only 50% of companies have a single point of contact for communication, which results in candidates feeling engaged or valued through the process.
Recruitment automation with a candidate-focus means that communication is personalised for high-engagement with the ability for the conversation to adapt to what it learns about a candidate almost immediately.
As a candidate finding out that you are not successful is tough, and worse, most companies just ghost those they don’t wish to move ahead with. Automation can ensure that every candidate is engaged and cared for even when they are not moving forward in the process – and that doesn’t mean a standard rejection email. Ai can deliver highly personalised communication that builds connection even for those unsuccessful in their application.
Although some companies have made efforts to remove bias from resumes, companies still have a lot of work to do on inclusion. For starters, many are relying on training programs, which have shown to be largely ineffective in delivering long-term change.
It’s true that recruitment automation can amplify bias, but automation that works to reduce bias is continually testing against biases in the system and has been shown to be effective in reducing the impact of bias in hiring decisions. Somethings humans cannot do (we’re inherently biased, whether we like it or not).
When you have the right data input gathered through blind screening and blind interviews – that don’t rely on CV data – then you can help companies achieve an equal and fair experience to all candidates.
Inclusive hiring is not limited to gender and race. Companies need a broader view of diversity, equity, and inclusion that includes individuals with disabilities and neurodiversity. This requires the right digital tools and technology to ensure that candidates have a positive experience. In many cases, chat and text are more inclusive over video or even phone screening and interviews for these candidates.
Most companies see feedback as a risky area and something they have no ability to do in a fair and timely manner. Essentially this is a lost opportunity for learning and development.
When you see feedback as a value proposition of an employer brand, its power in transforming your TA strategy becomes clear. Recruitment automation allows companies to deliver personalized feedback building trust and strengthening your employer brand.
Personalized feedback with tangible action items, means that candidates feel empowered even if they are rejected. Technology can help to deliver these action items in a human way, that even humans are not able to do at scale or even very well.
These insights are only made possible through natural language processing and machine learning that work in the background to reveal important information about the candidate. When a candidate feels like they are ‘seen’ that can be a transformational moment in their career paths.
Only recruitment automation can deliver individual feedback to everyone who takes time to do a job interview.
In an era of growing awareness around the privacy of data, only 1 in 4 candidates trust the data being will be used to drive hiring decisions. As companies look at recruitment automation through a candidate-centric lens, they must consider both the quality of the data they use and how to build trust between employers and candidates.
The biggest mistake that most companies make is using the wrong data. Resume data is not necessarily an indicator of performance or quality of hire.
Ethical Ai is something that hiring managers need to understand and use to evaluate providers. Providers using ethical Ai operate transparently, are backed by explanations, describe their methodology, and frequently publish their data.
Aptitude Research found that when data is transparent, it increases the trust in talent acquisition leaders, hiring managers, and senior leaders. With data transparency, 84% of talent acquisition leaders stated that they trust the data, and 78% of senior leaders trust the data.
55% of companies are increasing their investment in recruitment automation this year. These companies recognise that automation can improve efficiency, lift the administrative burden, reduce costs, and enable data-driven decisions.
This report focuses on a new look at automation through the eyes of the candidate
After all, automation is more than moving candidates through a process quickly. It should also enable companies to communicate in a meaningful and inclusive way and build trust between candidates and employers.
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.
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.
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.
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.
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.
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.
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:
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.
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.”
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.
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.
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.
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.
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.
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.