To find out how to use Recruitment Automation to ‘hire with heart’, we also have a great eBook on recruitment automation with humanity.
Start with the end in mind – always hire for performance first
Most people are very familiar with a performance review. It’s the annual anxiety fest when every employee has their performance assessed and rated, perhaps against benchmarks agreed at last year’s review or defined by their job description.
So is a talent review basically the same thing? Well yes and no. While a talent review will still see employees rated and ranked, the focus extends beyond current and recent performance to consider their potential as future leaders in senior or key roles within the business. It’s all about mapping an organisation’s business needs against the capabilities and potential of its people.
Talent review plays an essential role in business planning, pinpointing skill gaps and helping organisations to develop and retain their best talent.
Forward-thinking organisations believe that talent review is bigger than an annual event. Rather, it’s an essential part of an always-on process of talent management that fosters a high-performance culture from the very first engagement with employees.
Sapia’s Ai-enabled chat interview platform helps businesses to plan for future success by ensuring candidates with the very best potential are identified and engaged upfront. This approach provides talent momentum from the outset, ensuring every hire is building ‘bench strength’ and providing leaders with confidence that the next generation is ready to step-up and step-into key roles as needed.
How do you hire for the values and behaviours that result in high-performance?
It’s no secret that high performers and team leaders share certain personality traits and behaviours. In fact, it’s a science that organisations have long embraced in their pursuit of excellence and competitive advantage.
Since it was first published in 1962, The Myers-Briggs Type Indicator that classified 16 personality types has been at the heart of most personality assessments and recruitment science. Much of the appeal of Myers-Briggs was its simplicity in reducing complexity to concise descriptors. These descriptors may have sufficed when only human intelligence was doing the processing and decision-making.
But in an age of data, it’s a big compromise – a compromise in accuracy, nuance, and the real diversity of personality types that exist in our population. It’s also a compromise we no longer need to make.
Read: Hire for Values
Moving beyond the limitations of Myers-Briggs
Sapia is a leading innovator and advocate of leveraging data and technology to enhance the recruitment process. In developing our award-winning automated chat interview platform, our data science team looked at how we could move beyond the limits of Myers-Briggs personality testing.
Our data team fed text responses to interview questions from 85,000 job applicants into our personality classifier. Spread across two regions, the UK and Australia, 47% of applicants were identified as male, 53% as female.
Instead of the standard 16 personality types, we directed the machine to group the data into 400 unique personality groupings.
Identifying 400 unique personality groupings and how they could be usefully applied to decision-making is beyond the ability of the human brain… but not beyond technology. Using Natural Language Processing (NLP) and machine learning, our artificial-intelligence enabled platform got to work with findings that were both surprising and not surprising at all.
What did we find?
The ‘not surprising’ part of our research is that even at 400 groupings, there are distinct differences in personality profiles. It’s not surprising when you consider that humans are not linear beings and that our personalities are highly complex and nuanced.
The most surprising thing we discovered was that personality types by role were distinct. The personality profiles attracted to sales roles, for example, were noticeably different from the profiles attached to a carer role. Even more surprising were the imperceptible differences in the personality distribution across the 400 types between men and women – a sign of how conscious or unconscious biases can play into our decision processes.
What does the talent review look like?
Differentiated by size, sector, structure and history, every organisation is unique. So every talent review will be unique too. Talent reviews need to be designed around the specific needs of the business but generally will bring performance management, learning and development and succession planning together.
When senior leaders meet for a talent review, their principle objective is to talk about the performance of individual employees in their teams and how those employees might take on more responsible roles in the future. Through this process, the critical positions in an organisation will be identified. Critical positions mean any role that business operations would stop or be seriously compromised if no one was able to step into the role immediately.
Keep in mind that these critical roles may not necessarily be management roles and will also depend on the nature of the business. In a manufacturing business, for example, the chief engineer might be solely responsible for keeping a production line in working order. Talent reviews need to consider every employee across an organisation.
Talent review improves business focus
An ongoing talent review process not only matches an organisation’s talent to existing roles, but it also helps identify new roles that will need to be created to achieve plans for future growth or expansion. It’s also possible that as a company moves forward, key roles may change or even become redundant. The most successful businesses are dynamic and flexible.
A structured review process reviews employees in terms of key strengths, career ambitions and readiness for promotion. Talent reviews provide a forum for a range of important conversations that every organisation interested in best practice needs to have:
- What matters most to our organisation?
- What are our business objectives?
- Does our existing talent pool and policy align with business strategy?
- Are our managers reviewing performance and potential in the best way possible, without favour or bias?
- Are we doing enough to support our people’s learning, development and growth?
- Do we have the right continuous performance management process in place?
- Are we identifying and recruiting the talent at the early career stage with identified potential to be the leaders of the future?
- Is our business (or part of the business) at risk without appropriate successors?
- What needs to be done to mitigate any risk?
- Do we need to embed new values and improve culture?
Planning a talent review
There is a range of methods that organisations use to assess their employees for talent reviews. While some will arrive at a ranking or score, others may use a more nuanced approach to assessing their talent.
Talent reviews can often reveal glaring disparity and bias in team leaders’ expectations of employees and how they rate them. An agreed and standardised approach across the organisation is essential. By ensuring employee expectations are aligned among leaders and cultural values are socialised across the organisation, potential friction around accountability can be diffused.
Rank and yank – what not to do
Though their ranking process has long been dropped, Jack Welch, the celebrated or controversial (pick your own path!) CEO of General Electric once insisted on an evaluation that reduced every employee’s performance to a number. Following evaluations each year, the lowest ranking 10% were fired across the business. In contemporary business, this ‘rank and yank’ approach would not be considered best-practice HR.
The 9-box performance and potential matrix
A less controversial ranking for employees is the 9-box matrix. This commonly-used assessment tool assigns employees to one of nine boxes on a grid that on one axis rates their performance (underperformance, effective performance, outstanding performance) and on the other rates their potential (low, medium, high). Employees ranked in the box where outstanding performance and high potential meet are those assessed most likely to be future leaders.
What matters most – agreeing your assessment criteria for hiring
Taking a step back from the talent review process, Sapia has worked to solve and improve the frontier problem of every recruiter and every employer – how to get the right talent on board sooner.
With policies and process to put the best candidates in place every time, ongoing talent management and talent reviews can be more streamlined and rewarding for employers and employees alike.
The first step to creating a step-change in the process is ensuring that everyone is assessing talent on the same criteria. These need to align with your organisation’s specific needs and values, which are ideally defined and documented as part of your business, brand and employer brand plans.
How Sapia helps you get to the best talent (much faster)
While Sapia’s early data breakthroughs were based on 85,000 interview responses, machine learning and artificial intelligence means that our platform never stops learning. Today, our Ai-powered platform has analysed more than 165 million words in text-based interviews from more than 700,000 candidates.
Continuous learning means that Sapia can help recruiters and employers make smarter, evidence-based employment decisions at the early career stage.
Within our science-based approach, behavioural interview questions are tailored around the agreed assessment criteria for the role. These questions are related to past behaviour to reliably assess personality traits. They can be customised to the specific role family – sales, retail, customer service etc– and aligned to the organisation’s agreed values and characteristics that will define their leaders of tomorrow.
Sapia’s bespoke Ai-platform analyses candidates’ responses across a range of criteria including readability, text structure, semantic alignment, sentiment and personality to identify candidates with the best future potential.
Nurturing your talent culture
Making the wrong choices for future leaders can put your business at risk. At times of talent review, careers can be derailed and employees demotivated. A properly executed talent management process that begins with smarter recruitment choices is one of the best investments in the future of your business.
The insights delivered through a disciplined, standardised and ongoing process of talent assessment can be used at both organisational and managerial levels to drive your business forward. Creating a culture of high performance begins with best practice in early career candidate assessment. With Sapia’s platform as a key element, a robust talent review and management process will work to:
- support continuous performance management
- deliver robust succession planning
- optimise talent performance
- support skills assessments and gaps analysis
- lift employee retention
- support talent development and career pathways
- drive employee engagement through career conversations
- inform talent planning and decisions with better data
- embed culture and values throughout your organisation
This article is presented by Sapia as part of our mission to promote best practice in contemporary recruiting and HR. Our Ai-enabled text chat interview platform can help any organisation identify future leaders while providing candidates with an efficient, empowering and enjoyable experience. The user satisfaction rate for our award-winning platform is 99%.
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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: