It’s a cliché, but nonetheless true, that as time passes all processes become dated.
Some might need to be thrown out completely. Many more need to be adjusted and refined to keep up as workplaces and ways of working change.
I’m not old enough to remember the recruitment days of Rolodex and faxed documents. But I’ve heard the stories. Paper mountains of resumes teetering on desks. Consultants queuing at the one office fax machine to send their applicants’ profiles to clients.
Who knew that today we’d be communicating almost instantly by email, on our own computers, or sifting through resumes using Applicant Tracking Systems? In the 1980s that would have sounded like something from Doctor Who.
Since then, it’s all slowed down a bit.
Sure, ATSs take a lot of the legwork out of choosing who to interview. But they’ve also led to Resume Optimisation tools to help applicants beat our filters.
How can we avoid picking only the people who are best at gaming the system? How do we know we’re not missing our perfect applicants?
Now AI is taking the hiring process another leap forward. It’s speeding up the more process-driven elements and helping us select interviewees who are more likely to fit into our businesses.
And that means we need to re-examine two elements of that hiring process – the resume and the interview.
First, let’s tackle the resume.
Why resumes aren’t worth the paper they’re written on
Here’s a challenge for you. Find five well-known businesses that don’t ask for a resume on their careers page. Difficult, isn’t it?
Now think about the resumes you’ve seen recently.
I’ve seen resumes that are well-constructed, professionally crafted prose. And others that are complete works of fiction.
You’re as likely to find glaring spelling mistakes, a messy layout, and a shameless plea to be considered as you are a concise summary, an attractive photo and carefully chosen keywords. If you’re really unlucky you get all of these in one “super-resume”.
A quick search on “How are resumes used?” reveals the astounding advice that applicants should “know the facts in detail, as they may be questioned” about them. That just confirms my suspicion that these documents are more like scripts than records of facts.
And, there’s one more thing that recruiters know about resumes, even if they don’t all admit it …
Not one CV is properly read when they’re selecting applicants for interview.
According to research by the Cambridge Network, some recruiters give CVs a six-second speed-read and many recruiters spend just under 20% of their time on a profile … looking at the picture!
Resumes are rarely used correctly or understood properly, by applicants or recruiters. They most certainly do not predict how successful an applicant is likely to be in a role. Instead, they’re a minefield of potential bias: year of graduation (age bias), name (racial / gender/identity bias), experience in a similar business (confirmation bias), and so on.
So isn’t it better to put some truly intelligent AI for HR to work instead?
How new AI for HR makes resumes redundant
I was astonished to see that 96 per cent of senior HR leaders understand the benefits of using artificial intelligence in their HR and talent functions. But there’s a big gap between recognising the benefits and reaping them.
The canny HR leaders who are already adopting AI techniques will have a head start on their slower rivals.
Some more traditional HR tech providers have evolved their recruitment tools, presenting them as predictive. However, they’re more likely to be creating profiles of your better staff and matching these profiles to the external candidate market, not predicting how they will perform.
Instead, the new wave of HR tech uses well-constructed algorithms, created using a business’s performance data, to provide an unbiased shortlist of candidates far more likely to succeed within the business once hired.
HR tech uses well-constructed algorithms
The algorithm can’t be misled by optimisation techniques, personal feelings or prejudice. Instead, it uses objective data, science and evidence to find the people who are most likely to be a good fit and perform. For this role, in this business. And it will help uncover applicants we might have otherwise overlooked when their resume didn’t match our expectations.
The better solutions work by identifying the defining characteristics of the whole performance group within a business (superstars through to under-performers) and then predicts where external applicants will sit on your performance scale once/if hired.
These advanced solutions then go further via validation reports to prove their better predictions are turning into better new hires. They then use Machine Learning to ensure each unique model continues to learn more about the performance of each business, further improving its predictive power over time.
These two additional steps mean that whilst us humans are still required to make the final hiring decision, we will get better results for our applicants and our businesses. Maybe that’s where the resume might still have a role – as the frame for some reasonable high-level questions to help us understand the person in front of us in more depth, once they’ve got through the first stage.
The most sophisticated algorithms are already outperforming humans in the selection and identification of suitable candidates – and by that I mean candidates who go on to become productive, valuable and loyal employees.
Decision time for CHROs
So, what would you rather have?
– A shortlist of candidates chosen because of what they’ve selected to include in (and omit from) their resume?
Or
– A shortlist of candidates you know are likely to do well in your workforce, because they’ve been chosen using statistically-proven, company-specific performance drivers validated by behavioural science?
Not that tricky a question, is it?
And very easy to see how, with the advent of AI for HR, resumes will soon be as much a part of recruitment as faxes and Rolodex.
Suggested Reading:
https://sapia.ai/blog/cv-tells-you-nothing/
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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: