It’s an understatement to say that recruiters and talent acquisition managers have had it tough over the last four-odd years. The pressures have compounded like a line of falling dominoes: First it was the COVID-19 pandemic; then came the mass talent migration; then the advent of new concepts like ‘quiet quitting’ and ‘acting your wage’, which, like them or not, seem to be the manifestations of a tired and existentially anxious workforce.
Now, in 2024, it’s likely that we’ll have to contend with a global recession.
Hiring is tougher. Candidates are wary and they expect more. Duh.
So do companies and their CEOs. However – and somewhat counter-productively – many companies have sought to cut recruitment budgets, lay off recruiters and talent acquisition managers en masse, and deprioritize long-term recruitment marketing strategies. We’re facing troubled times, and recruitment (and perhaps HR, more generally) is being treated as a cost center.
This misunderstanding of HR as a money sink is nothing new. It happens during every trough in the market. But, if we don’t make efforts to change this perception, 2024 will be a particularly painful valley to climb out of.
Why is recruitment treated as a cost center?
CEOs have been keen on talent strategy for years, but are struggling to quantify the effects of recruitment and talent acquisition activities. They cannot see the A to B journey, the action and its result. When the market is good, talent is in abundance, and you’re hiring effectively, nobody cares. But when times are hard, nebulous processes are put under harsh light.
Relatedly, recruitment and talent acquisition leaders are struggling to prove that the outcomes of their work are driving revenue. This is primarily an issue of data capture and analysis, in our experience: When companies come to us to help with hiring quality talent, the number one issue they have is to do with metrics and KPIs. Most do not know how to reliably measure quality of hire, nor time-to-hire, nor the effectiveness of their recruitment marketing channels. Many know that their processes are plagued with inefficiencies, but are not sure how to go about fixing them.
(To be clear, totally understandable. This stuff is hard.)
The big and unmanageable HR tech stack
Where recruitment is concerned, a HR tech stack tends to look like this: an unwieldy ATS, often coupled with a conversational AI or scheduling tool.
These technologies cost big money. As a result, the question CFOs and CEOs will be constantly asking of HR is this: Is it adding real value? Can you prove it? Or are we simply stuck to a system that tackles old problems with insufficient solutions?
The bottom line is this: Enterprise companies are overstacked, overworked, and need to adopt different solutions to old problems. It doesn’t mean less tech, necessarily, although it can; it means the right tech.
Focus first on areas of lost productivity.
Easier, perhaps, than it sounds. It’s always better to iterate than to completely restructure your hiring function. So get your team together and examine your processes. How much time is spent:
- Sourcing and reviewing candidates (including, importantly, reading through resumes and cover letters)?
- Screening?
- Scheduling, rescheduling, and conducting interviews?
- Organizing and corralling hiring managers?
- Gathering, collating, and providing feedback to candidates?
- Communicating with candidates, more generally?
- Onboarding?
Ideally, you have baseline data in your ATS to help you arrive at some indicative numbers. But let’s assume that you don’t: calculating rough person-hours is enough to see where time may be spent more effectively.
In our experience, sourcing and screening are the stages in which quick wins might be gotten. As time-honored research (and our Smart Interviewer product) shows, resumes and cover letters are not useful indicators of candidate quality or potential. They can be easily falsified. What’s more, Sapia and Aptitude research from 2022 discovered that 22% of candidates drop out at the application stage and 24% at the screening stage.
The biggest companies are starting to focus more on this. According to the Wall Street Journal, employers like Google, Delta and IBM are combatting the tight labor market by easing strict needs for college degrees.
Interviews are another huge cause of inefficiency. Structured interviews are the best explainer (at 26%) of an employee’s performance, but many companies allow recruiters and hiring managers to conduct interviews haphazardly, causing a misidentification and loss of talent that can be hard (if not impossible) to measure. If you’re interviewing badly, how can you know if you’re capable of finding good candidates? What’s the associated cost of such a problem?
It’s no surprise, then, that according to our research, 50% of companies say they’ve lost talent due to the way they interview. Big costs involved there.
Next, focus on measurable metrics
Don’t worry: We’re not going to lay out a massive and exhaustive list of metrics you should be tracking. Not feasible; you’re overworked as it is.
Instead, we’ll prescribe three good places to start, including links to helpful blog posts explaining how you measure them effectively:
- Candidate abandonment rate
- Candidate source attribution (or where candidates actually come from)
- Candidate experience baseline
Each of these metrics can help you improve efficiencies, and in turn, start to prove that your recruitment function is having a positive effect on business outcomes.
Layer on tech that can help you drastically improve hiring efficiency, while giving you time to focus on big picture stuff
At a certain point, we must realize that force-multiplying technology is the only way to win in the unfolding ‘now’ of work. We’re spending way too much time with processes that can be repeated and automated – often out of some sense of duty to uphold 1:1 human connection (as if technology completely removes that, which it doesn’t).
And, because we do this, we weaken our position at an executive level: CEOs care about what is scalable, and the average recruitment function, traditionally speaking, does not.
In a recent episode of our Pink Squirrels! podcast, Sapia CEO and founder Barb Hyman had a chat with expert HR change management leader, Kyle Lagunas, about this very topic.
The one foolproof way to elevate recruitment in your company
We exist to help you hire better, faster, and with fewer headaches. Our Smart Interviewer takes care of the scheduling, interviewing, and assessment stages of your process – saving upwards of 2,000 recruitment hours (av.) per month, and enabling you to offer jobs to candidates within 24 hours of application.
It’s delivered in a chat-based format (hello, Gen Z!), and candidate responses are assessed according to science-backed personality models. This means you can be sure you’re getting top talent, and you can prove it with measurable, repeatable data.
That’s not all: Our tech is blind, which means it natively disrupts bias and maximizes the size of your talent pool. Everyone gets an interview, and everyone gets personalized coaching tips whether or not they get the job. Our application completion rate, for all customers, sits at around 85% on average; our candidate satisfaction rate is well over 90%!
(And, if you need a second stage interview, you can use our Video Interview tool.)
Everything you do with our platform is pulled through to comprehensive data dashboards, allowing you to see hiring efficiency, quality, time, diversity, and other metrics. CEOs love this kind of transparency.
There you go: time saved NOT having to screen, review resumes and cover letters, compile candidate feedback, communicate with candidates, or improve hiring manager interview techniques.
When you’re saving that much time and money, your recruitment (or HR) function has more bandwidth to focus on long-term talent acquisition and people initiatives.
Don’t struggle in 2024 – speak to our team today about how we can solve your hiring challenges.
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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.