Decentralized recruitment, while enabling larger companies to hire efficiently, suffers in a labor-short market.
Under ordinary circumstances – like, say, the world before COVID and the Great Resignation – it’s ideal to let local hiring managers build their own workforces. Generally speaking, the decentralized approach is better for productivity, candidate experience, and the overall satisfaction of hiring managers, who look favourably on the trust and autonomy they get from head office.
However, when good candidates are hard to come by, the dearth of talent puts stress on the joints of such a sprawling network. We hear this frequently from companies who come to us to help improve efficiency, diversity, and quality of hire.
Here are the common problems companies are having with decentralized hiring in 2022:
- Hiring managers are frustrated, because they have a trickle of applicants and little control over employer branding and recruitment marketing.
- Consistency is hampered by inconsistent processes and rogue hiring managers, who frequently abandon workflows and ATS protocols in order to acquire warm bodies by any means necessary.
- Job advertising budgets are distributed unevenly, resulting in consternation for already-strained teams.
- Diversity is put on the backburner, both because hiring managers have the final say, and because they have little-to-no accountability over decisions.
- The company’s recruitment centre (i.e. head office) is unable to collect and analyze sufficient data to diagnose and fix recruitment problems across its decentralized network.
- The company is using an ATS with which either some (or all) of hiring managers are unhappy. Head office may know this, but in any case, it decides that the process of researching, purchasing, and implementing a new ATS is not worth the pain.
- A staunch desire to stick to the status quo, or ‘the way we’ve always done things’, because the company assumes that this period of hiring difficulty will soon pass.
These challenges (and others) have effected a drop in confidence in the way companies interview and process candidates. An Aptitude Research and Sapia.ai report from earlier this year found that 33% of companies aren’t confident in the way they interview, and 50% have lost talent due to poor processes. Meanwhile, 22% of the average talent pool is drained at the application stage.
Statistically speaking, roughly one in five people, at minimum, are bailing out of your application process at the very beginning.
How to improve efficiencies across a strained decentralized hiring network
As with many things in business, the answer to alleviating organizational pain lies in small, iterative improvements. Our recommendations do not include haphazard technological upgrades, nor do we advocate for widespread process changes. These will more than likely cause your decentralized hiring network to fall apart.
Here are some good places to start.
Look at removing time-wasting entry barriers, like resumes and cover letters
This is particularly important for the retail and hospitality industries, but certainly applies to any companies that hire entry-level team members at volume. Given the average level of job experience at this level of employment, most resumes and cover letters aren’t useful in gauging candidate quality. On the contrary – they take up precious hiring manager hours, are cumbersome for candidates to write, and are the main cause of the 22-24% candidate drop out rate we mentioned above. That’s not even accounting for the fact that anywhere between 60-80% of resumes contain falsifications.
Implement a simple, standardized process for capturing a candidate experience NPS baseline
Decentralization, almost by definition, makes capturing useful information difficult. But if you use an ATS as a tool for centralization, consider adding a candidate NPS measurement step to your application process. It can be as simple as a Net Promoter Score scale (1 to 10). If you hire at volume across multiple localities or regions, asking this one simple question can help you produce meaningful insights about how candidates find your process. What gets measured, gets managed, and though there are many other data points you might want to collect, this is a good (and relatively easy) place to start. If you’re keen to learn more about this, check out our podcast episode on candidate experience with Lars van Wieren, CEO at Starred.
Speak to your hiring managers regularly
Quantitative data is gold, but qualitative data is platinum. Make a habit of interviewing (not surveying, interviewing) your hiring managers on the ground. You’ll uncover invaluable insights that may enable you to make fast changes at scale. We help our clients collect qualitative feedback from hiring managers as a matter of course, leading to increases in productivity and hiring manager satisfaction.
Here are some useful questions to ask your hiring managers:
- Take me through how you run your local (e.g. instore) hiring process, from start to finish.
- Explain your process for interviewing candidates.
- Where do you think you waste the most time?
- What doesn’t work as well as it should?
- What kinds of candidates are you seeing, and how would you rate the overall quality?
- How might we support you in hiring more effectively?
This kind of bottom-up research aims to understand how hiring managers are actually behaving and interacting with systems. Some may be breaking from established protocols, but if you ask them why and how, you might uncover tactics and efficiencies that can be brought back to the rest of the organization, thereby improving the way all hiring managers operate. Two adages apply here: ‘Necessity is the mother of invention’, and ‘People will always find the path of least resistance’.
This fact-finding method is better than surveys because surveys impose a limited scope in which potential problem areas are preset. “We’re asking you about these things,” you’re saying, “and therefore, we’re suggesting they’re most important.” As a result, other problems and possible solutions are likely to be excluded from discovery. You’ll always learn more by having real conversations, because they can go in any conceivable direction.
Look for novel ways to encourage applications from otherwise passive candidates
Again, incredibly useful for retail, but applicable in a wide range of industries and contexts. Think about the universal touchpoints you have with customers (a.k.a candidates) across your decentralized network. In retail, some good examples might be your receipts and carry bags. These provide you invaluable real estate to advertise your jobs and employer brand. Consider putting a URL or QR code on these assets, and you might drastically increase the amount of people who know about and apply for the jobs you advertise. This tactic has the added benefit of capitalizing on active and loyal customers; after all, if they’re buying from you, they’re a prime target for recruitment marketing.
The best part about this manner of advertising? You already own the space, and the design can be centralized and rolled out at scale.
We’d be remiss if we didn’t point out that Sapia’s Ai Smart Interviewer is a dynamite solution for the inevitable pain points of decentralised recruitment. Our technology can be rolled out across your entire company, and takes care of the application, screening, interviewing, and assessment stages of your process.
Hiring managers save time – as much as 1,600 hours per month, for some of our customers – but they still get the option to approve and interact with short-listed candidates. Better still, our platform captures vital data on diversity and candidate experience, enabling you to see exactly how your network is performing, individually and collectively.
Best of all, Sapia tech integrates directly with the leading ATS platforms, and can be rolled out in as little as four weeks.
Woolworths Group, Australia’s largest private employer, uses Sapia to hire more than 50,000 candidates per year, nationwide. To see how they flourish in a labor-short market, check out our case study here.
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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.