Last week I made a promise to share a journey that brought me to be working in the business at the cutting edge of technology and science within the People/Talent sector.
In my previous post, I shared some of the thinking of people within my sector. This is what I learned about hard work during my 13 years working in tech recruitment.
I was 22 years old when I became a recruiter. I was competitive, driven and hungry to succeed. Not only in financial terms, like many other recruiters, but also my professional status and standing. I wanted to be one of the best at my job and to be respected for the work I did.
And I know there are thousands of recruiters out there whose hard work often goes unrecognised by clients, candidates, managers and colleagues alike. I no longer know exactly what it’s like to be a recruiter in 2018 but back in 2005-2010 if you joined one, my teams, we’d have had conversations that went something like this:
It requires a lot of hard work and skill with a splash of good luck.
The hard work is the time commitment needed to consistently deliver for your clients and candidates.
You need the skill to learn the difference between C# and C++ and how technologies stack together.
Eventually, your business development efforts will combine with good luck when that client answers your call and confirms they are indeed looking to hire someone within your vertical specialism. Happy days!!
You agree to terms for the customer’s key role, you pat yourself on the back and then you go again – back to the hard work because now you’ve got to find suitable candidates.
Good recruiters already have a network of great candidates – you go to them first, qualify/rule out and you’ve got a shortlist inside an hour or two. Then, more hard work.
When the other unknown recruiters working at unknown agencies also trying to fill the same role, clock off at 6 pm to enjoy their evening plans, you’re still in the office.
If you’re anything like I was you’ll still be in the office until 9 pm when the contractors start to get a little irate.
“Sorry for ringing so late in your evening but I’m trying to fill a key role for an important customer.”
Most of them appreciate your hard work and candour. Some even sound impressed with your commitment.
A few get grumpy but them’s the rubs – it’s water off a duck’s back for a driven, professional recruiter who wants to do their best for their customer and won’t mind, professionally, ruffling the feathers of a few early-to-beders to ensure they keep on top of their game, delivering great candidates to their clients.
Eventually, your hard work pays off and you place the successful candidate (probably after at least one candidate did an interview no-show following the death of a distant relative/hospital appointment/dog vs homework / insert obscure excuse)
Meet Tom & Sally to get a sense of what I was filling – I was definitely ‘Tom’!
That was my early recruitment career. Because I knew there were no shortcuts to success. I needed to graft, sacrifice my evening socialising (don’t worry, I made up for it at the weekends!) to ensure I found the best candidates for my clients.
I was a recruiter and I really, really loved my job. I genuinely hope today’s recruiters love their jobs as much as I did but the recruitment world I knew is no longer. And that’s because Talent AI has created a shortcut!
AI can now rapidly identify suitable talent and create a shortlist of candidates for a human recruiter to then engage with.
A shortcut that also helps remove bias from talent workflows.
In fact, it’s such a clever shortcut that it should have its own name. I have a suggestion. Let’s call it…Recruitment!
Because recruitment was still recruitment when ATS providers rolled out filters and keyword identification tools which were quickly gamed by candidates – writing retail on a CV pushed it up the results list but that didn’t make the candidate more knowledgeable in retail.
Recruitment was still recruitment when talent attraction projects were created. Recruitment is still recruitment throughout the modern-day careers day (which I hope has evolved from my experiences back in the early 2000s)!
It’s still recruitment if you bring in video interviews (disclaimer: I hate the idea of video interviews; I think they simply shift bias to a different stage in the recruitment process).
Recruitment will still be recruitment with AI, it’ll just be better for candidates, clients and recruiters alike.
Suggested reading:
https://sapia.ai/7-tips-to-making…stment-decisions/
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.