Before COVID, the conversations I was having with HR executives were about how Sapia might help them with the volume of candidates they were receiving for job openings. For every job posted there were often over a thousand candidates, and it doesn’t take much of a stretch of the imagination to understand how overwhelmed many big organisations were. Our Ai was seen as the solution to automate dealing with candidate volume in a way that found the best people, but also touched base with everyone who applied as part of their brand building. In a nutshell, before the pandemic, efficiency was the key driver in looking for automated hiring solutions like ours.
Now that we’re emerging from the disruption of COVID, no one is talking to me about needing help with the volume of candidates they receive. In fact, they are asking how we might help them get any candidates in the first place! All around the globe, and across multiple industries, there is a need for candidates. It’s certainly been an abrupt change that has left many scratching their heads, but there is almost no time to wrap your head around it if you want to stay in the game. This is a new war for talent unlike any we’ve seen before, and candidates have the upper hand. It’s created a need for a solution to solve two things: firstly, to identify skills in candidates that traditional ways of hiring failed to identify (I call this cohort “undiscovered talent”) and a strong candidate experience (you are the one being interviewed from the moment they hit “apply”).
I thought it was worth looking at how the “war of talent” has evolved since it was first coined by Steven Hankin at McKinsey & Company in 1997. At that time there was a shift in the way that companies valued their talent, and it became seen as important to attract the best in order to have a successful organisation. It’s hard to think about this now, but at that time the whole idea of cultivating company cultures that aimed to elevate and value employees was new. At this stage though the “war” was largely for executive talent with recruiters focusing on building their brand by poaching star C-Suite talent off competitors, wooing them with big sign-up bonuses and lavish overtures like unexpected gifts and trips.
As tech companies started to become the big players in the market, the focus turned from business acumen to the need for the best digital and technical talent. Recruiting became less about material perks (though many engineers still commanded high salaries) but also about giving talent things they wanted besides just money. Flexibility, free lunches, unlimited holidays and creating cultures that were about “working hard and having fun” were how the war for technical talent was won. This was really a time of culture wars between companies, but also meant that many companies hired only for culture-fit. This resulted in fairly homogenous teams that were largely white male techbros, and eventually many large tech companies were called out on it. Beyond tech, corporates were also waking up to the fact that they had some serious diversity issues that needed to be addressed. This led to a new war. The war for diverse talent.
Pre-COVID, hiring more diversely was a strong focus for companies to find the best talent. We all know that diverse teams result in better business outcomes and anyone who had a “pale, male and stale” executive team was seen as minted in the past. Coupled with Black Lives Matter, which became a global movement to address racial inequality from the C-suite down, finding more diverse talent through reducing bias in hiring, was where the war was being fought. This is not a won battle by the way, and remains a large focus for many companies that we work with and help. Importantly, finding diverse talent is still a key part of this new and emerging next phase of the “war on talent” … the one where workers have the upper hand. The one where candidates are in short supply, and people want jobs that suit them just as much as whether they are seen as just suited to the job.
Recruiters have been forced to look at people differently – and this is not a bad thing. Factors like age, ethnicity, education, gender and even past experience that obscured our understanding of someone’s ability to do a job have all been cancelled as qualifying factors. Soft skills, or human skills, have become the focus on what we need to understand in order to assess someone’s suitability to do a job. Are they a team player? Do they like to problem solve? How aligned are they to our company values? Are they self-aware and in touch with their emotions? Can they put stress aside to achieve outcomes?
“What we recruit for” has significantly shifted for many already, but there is still some catching up to do on the “how we recruit”. To be blunt, CV’s and cover letters begging recruiters to “pick me!” serve no purpose in this new battle. They ask too much of candidates from the outset, serve no valuable purpose in the information they provide, confirm our biases and just create work on the HR manager’s side.
We need to walk in a candidate’s shoes and make sure that our recruiting process puts them first, treats them fairly and without bias, meets them where they are at, and is both friendly and informative. And, HR teams need to do this all while working efficiently and fast. Speed is crucial when talent is in short supply.
Impossible? No, not at all. Recruiters need to understand that Ai platforms like ours exist to solve all these problems. We’re not a “technical” solution, but a human one, in that we can accurately identify soft skills immediately and engage with candidates in a one-on-one way, at scale.
You cannot win this war on talent without chat-driven Ai technology. Technology like ours is the only way you can quickly understand the real human skills that every candidate brings to the table, without dismissing anyone upfront.
I can’t help but think that these issues we’re facing as recruiters and HR managers right now, where workers have the upper hand, while unchartered territory, will only serve our industry for the better. It’s a chance to give everyone a fair go, truly understand them, treat them with the dignity they deserve … and still hire better teams.
Maybe it’s not a battle after all. Maybe it’s a win-win.
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For more on how to improve candidate experience using recruitment automation, we have a great eBook on candidate experience.
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Blog
Neuroinclusion by design. Not by exception.
Why neuroinclusion can’t be a retrofit and how Sapia.ai is building a better experience for every candidate.
In the past, if you were neurodivergent and applying for a job, you were often asked to disclose your diagnosis to get a basic accommodation – extra time on a test, maybe the option to skip a task. That disclosure often came with risk: of judgment, of stigma, or just being seen as different.
This wasn’t inclusion. It was bureaucracy. And it made neurodiverse candidates carry the burden of fitting in.
We’ve come a long way, but we’re not there yet.
Shifting from retrofits to inclusive-by-design
Over the last two decades, hiring practices have slowly moved away from reactive accommodations toward proactive, human-centric design. Leading employers began experimenting with:
- Sharing interview questions in advance
- Replacing group exercises with structured simulations
- Offering a variety of assessment formats
- Co-designing assessments with neurodiverse candidates
But even these advances have often been limited in scope, applied to special hiring programs or specific roles. Neurodiverse talent still encounters systems built for neurotypical profiles, with limited flexibility and a heavy dose of social performance pressure.
Hiring needs to look different.
Insight 1: The next frontier of hiring equity is universal design
Truly inclusive hiring doesn’t rely on diagnosis or disclosure. It doesn’t just give a select few special treatment. It’s about removing friction for everyone, especially those who’ve historically been excluded.
That’s why Sapia.ai was built with universal design principles from day one.
Here’s what that looks like in practice:
- No time limits — Candidates answer at their own pace
- No pressure to perform — It’s a conversation, not a spotlight
- No video, no group tasks — Just structured, 1:1 chat-based interviews
- Built-in coaching — Everyone gets personalised feedback
It’s not a workaround. It’s a rework.
Insight 2: Not all “friendly” methods are inclusive
We tend to assume that social or “casual” interview formats make people comfortable. But for many neurodiverse individuals, icebreakers, group exercises, and informal chats are the problem, not the solution.
When we asked 6,000 neurodiverse candidates about their experience using Sapia.ai’s chat-based interview, they told us:
“It felt very 1:1 and trustworthy… I had time to fully think about my answers.”
“It was less anxiety-inducing than video interviews.”
“I like that all applicants get initial interviews which ensures an unbiased and fair way to weigh-up candidates.”
Insight 3: Prediction ≠ Inclusion
Some AI systems claim to infer skills or fit from resumes or behavioural data. But if the training data is biased or the experience itself is exclusionary, you’re just replicating the same inequity with more speed and scale.
Inclusion means seeing people for who they are, not who they resemble in your data set.
At Sapia.ai, every interaction is transparent, explainable, and scientifically validated. We use structured, fair assessments that work for all brains, not just neurotypical ones.
Where to from here?
Neurodiversity is rising in both awareness and representation. However, inclusion won’t scale unless the systems behind hiring change as well.
That’s why we built a platform that:
- Doesn’t rely on disclosure
- Removes ambiguity and pressure
- Creates space for everyone to shine
- Measures what matters, fairly
Sapia.ai is already powering inclusive, structured, and scalable hiring for global employers like BT Group, Costa Coffee and Concentrix. Want to see how your hiring process can be more inclusive for neurodivergent individuals? Let’s chat.
Blog
Skills Measurement vs Skills Inference – What’s the Difference and Why Does It Matter?
There’s growing interest in AI-driven tools that infer skills from CVs, LinkedIn profiles, and other passive data sources. These systems claim to map someone’s capability based on the words they use, the jobs they’ve held, and patterns derived from millions of similar profiles. In theory, it’s efficient. But when inference becomes the primary basis for hiring or promotion, we need to scrutinise what’s actually being measured and what’s not.
Let’s be clear: the technology isn’t the problem. Modern inference engines use advanced natural language processing, embeddings, and knowledge graphs. The science behind them is genuinely impressive. And when they’re used alongside richer sources of data, such as internal project contributions, validated assessments, or behavioural evidence, they can offer valuable insight for workforce planning and development.
But we need to separate the two ideas:
- Skills Measurement: Directly observing or quantifying a skill based on evidence of actual performance.
- Skills Inference: Estimating the likelihood that someone has a skill, based on indirect signals or patterns in their data.
The risk lies in conflating the two.
The Problem Isn’t Inference of Skills. It’s the Data Feeding It
CVs and LinkedIn profiles are riddled with bias, inconsistency, and omission. They’re self-authored, unverified, and often written strategically – for example, to enhance certain experiences or downplay others in response to a job ad.
And different groups represent themselves in different ways. Ahuja (2024) showed, for example, that male MBA graduates in India tend to self-promote more than their female peers. Something as simple as a longer LinkedIn ‘About’ section becomes a proxy for perceived competence.
Job titles are vague. Skill descriptions vary. Proficiency is rarely signposted. Even where systems draw on internal performance data, the quality is often questionable. Ratings tend to cluster (remember the year everyone got a ‘3’ at your org?) and can often reflect manager bias or company culture more than actual output.
Sophisticated ≠ Objective
The most advanced skill inference platforms use layered data: open web sources like job ads and bios, public databases like O*NET and ESCO, internal frameworks, even anonymised behavioural signals from platform users. This breadth gives a more complete picture, and the models powering it are undeniably sophisticated.
But sophistication doesn’t equal accuracy.
These systems rely heavily on proxies and correlations, rather than observed behaviour. They estimate presence, not proficiency. And when used in high-stakes decisions, that distinction matters.
Transparency (or Lack Thereof)
In many inference systems, it’s hard to trace where a skill came from. Was it picked up from a keyword? Assumed from a job title? Correlated with others in similar roles? The logic is rarely visible, and that’s a problem, especially when decisions based on these inferences affect access to jobs, development, or promotion.
Presence ≠ Proficiency
Inferred skills suggest someone might have a capability. But hiring isn’t about possibility. It’s about evidence of capability. Saying you’ve led a team isn’t the same as doing it well. Collecting or observing actual examples of behaviour allows you to evaluate someone’s true competence at a claimed skill.
Some platforms try to infer proficiency, too, but this is still inference, not measurement. No matter how smart the model, it’s still drawing conclusions from indirect data.
By contrast, validated assessments like structured interviews, simulations, and psychometric tools are designed to measure. They observe behaviour against defined criteria, use consistent scoring frameworks (like Behaviourally Anchored Rating Scales, or BARS), and provide a transparent, defensible basis for decision-making. In doing this, the level or proficiency of a skill can be placed on a properly calibrated scale.
But here’s the thing: we don’t have to choose one over the other.
A Smarter Way Forward: The Hybrid Model
The real opportunity lies in combining the rigour of measurement with the scalability of inference.
Start with measurement
Define the skills that matter. Use structured tools to capture behavioural evidence. Set a clear standard for what good looks like. For example, define Behaviourally Anchored Rating Scales (BARS) when assessing interviews for skills. Using a framework like Sapia.ai’s Competency Framework is critical for defining what you want to measure.
Layer in inference
Apply AI to scale scoring, add contextual nuance, and detect deeper patterns that human assessors might miss, especially when reviewing large volumes of data.
Anchor the whole system in transparency and validation
Ensure people understand how inferences are made by providing clear explanations. Continuously test for fairness. Keep human oversight in the loop, especially where the stakes are high. More information on ensuring AI systems are transparent can be found in this paper.
This hybrid model respects the strengths and limits of both approaches. It recognises that AI can’t replace human judgement, but it can enhance it. That inference can extend reach, but only measurement can give you higher confidence in the results.
The Bottom Line
Inference can support and guide, but only measurement can prove. And when people’s futures are on the line, proof should always win.
References
Ahuja, A. (2024). LinkedIn profile analysis reveals gender-based differences in self-presentation among Indian MBA graduates. Journal of Business and Psychology.
Blog
Making Healthcare Hiring Human with Ethical AI
Hiring for care is unlike any other sector. Recruiters are looking for people who can bring empathy, resilience, and energy to the most demanding human roles. Whether it’s dental care, mental health, or aged care, new hires are charged with looking after others when they’re most vulnerable. The stakes are high.
Hiring for care is exactly where leveraging ethical AI can make the biggest impact.
Hiring for the traits that matter
The best carers don’t always have the best CVs.
That’s why our chat-based AI interview doesn’t screen for qualifications. It screens for the the skills that matter when caring for others. The traits that define a brilliant care worker, things like:
Empathy, Self-awareness, Accountability, Teamwork, and Energy.
The best way to uncover these traits is through structured behavioural science, delivered through an experience that allows candidates to open up. Giving candidates space to give real-life, open-text answers. With no time pressure or video stress. Then, our AI picks up the signals that matter, free from any demographic data or bias-inducing signals.
Candidates’ answers to our structured interview questions aren’t simply ticking boxes. They’re a window into how someone shows up under pressure. And they’re helping leading care organisations hire people who belong in care and those who stay.
Inclusion, built in
Inclusivity should be a core foundation of any talent assessment, and it’s a fundamental requirement for hirers in the care industry.
When healthcare hirers use chat-based AI interviews, designed to be inclusive for all groups, candidates complete their interviews when and where they choose, without the bias traps of face-to-face or phone screening. There are no accents to judge, no assumptions, just their words and their story.
And it works:
- 91.8% of carer candidates complete their interviews
- Carer candidates report 9/10 Candidate Satisfaction with their interview experience
- 80% of candidates would recommend others to apply
- Every candidate receives personalised feedback, regardless of the outcome
Drop-offs are reduced, and engagement & employer brand advocacy go up. Building a brand that candidates want to work for includes providing a hiring experience that candidates want to complete.
Real outcomes in care hiring
Our smart chat already works for some of the most respected names in healthcare and community services. Here’s a sample of the outcomes that are possible by leveraging ethical AI, a validated scientific assessment, wrapped in an experience that candidates love:
Anglicare – a leading provider of aged care services
- Time-to-offer dropped from 40+ days to just 14
- Candidate pool grew by 30%
- Turnover dropped by 63%
Abano Healthcare – Australasia’s largest dental support organisation
- 1,213 recruiter hours saved in the first month (67 hours per individual hiring team member)
- $25,000 saved in screening and interviewing time
Berry Street – a not for profit family & child services organisation
- Time-to-hire down from 22 to 7 days
- 95.4% of candidates completed their chat interviews
A smarter way to hire
The case study tells the full story of how Sapia.ai helped Anglicare, Abano Healthcare, and Berry Street transform their hiring processes by scaling up, reducing burnout, and hiring with heart.
Download it here: