“People are not your most important asset. The right people are.” – Jim Collins, author and lecturer on company sustainability and growth.
“Are the right people in the right roles?” [This is] the single most important factor for leadership success and for organisational success.” – Gail Kelly, former CEO of Westpac
Adam Bryant who pens the terrific weekly column, The Corner Office, for the NYT has interviewed a diverse pool of leaders, and a common theme from 99 % of his interviews with CEOs is that success correlates with hiring the best team.
My former boss Tracey Fellows, CEO of the REA Group, was also fond of saying that it is ‘people’ that keeps her up at night more than any other business challenge.
Most hiring in most organisations relies 100 % on people to make those most important decisions. Yet we do so with little objective data. Instead, we have layers upon layers of bias! And to give you an idea of how many there are, here is a whooping full Wikipedia list of cognitive biases for you to check out. This article lays out in great detail a plethora of cloudy, smeary and hazy biases I didn’t know could exist.
It concludes that they are mostly unalterable and fixed, regardless of how much unconscious bias training you attend in your lifetime.
Read: Why a Lack of Diversity is Costing Your Business
In some functions/ departments, your “Hiring for Diversity” may be going very well. However, diversity training and hiring isn’t repeatable, where humans are involved. And, if humans could be trained out of their biases, we may get more diversity in our new hires. But then, do we know that we are getting the ‘better’ hire from the applicant pool? How CAN you tell if you have no method of reliably testing for what matters for success?
You might say we rely on CVs to give us that ‘insight’ but did you know CVs are usually crafted, designed, worded and reworded to ‘best-light’ the applicant. Ever appointed an Excel whizz, who on hire doesn’t know a pivot from a concatenate? Or even worse, who cannot apply logic, reasoning and critical thought?
Because biases appear to be so hardwired and inalterable, it is more straightforward to remove bias from algorithms than from people.
This gives AI the potential to create a future where important insights underpinning decisions such as hiring, are made more fairly.
Read: Why Machines make better decisions than humans (oh and why I hate Simon Sinek)
Algorithmic bias is not the elephant in the room. Some argue that algorithms themselves have bias. The reality is that machine learning, by its very definition, is aiming to find patterns in large volumes of data, mostly latent, to support decisive actions. Removing bias is driven by what bits of training data you use to feed the machine.
If you build models from the profile of your talent and that talent is homogenous and monochromatic, then so will be the data model and you are back to self-reinforcing hiring.
If you are using data which looks at age, gender, ethnicity and all those visible markers of bias, then, sure enough, you will amplify that bias in your machine learning. Relying on internal performance data to make people decisions, that is like layering bias-upon-bias. Similar to building a sentencing algorithm with sentencing data from the US court system, which is already biased against black men.
So instead of lumping all AI and ML into one big bucket of ‘bias’, look beneath the surface to understand what’s going into the machine as that is where amplification risks loom large.
Unlike trying to solve human bias, machine learning is repeatable, stable, consistent and most importantly, testable. The value to the organisation is of course, immense.
Now that is ticking all the right boxes. It makes the possibility of objective and valid decisions available at scale, a probability.
The ability to test both training data and outcome data, continuously, allows you to detect and correct the slightest bias if it ever occurs.
Soon (maybe already) you will be putting yours, and your loved ones live in the hands of algorithms when you ride in that self-driven car. Algorithms are extensions to our cognitive ability helping us make better decisions, faster and consistently based on data, even in hiring.
You can try out Sapia’s Chat Interview right now – HERE. Else leave us your details to get a personalised demo
We can’t hide from reality anymore. Talent needs are shifting overnight, and AI is redefining what it means to work. Traditional talent frameworks are no longer fit for purpose. At Sapia.ai, we believe the future of talent strategy lies in a smarter, fairer, and more adaptive way of defining what great looks like.
Our AI hiring platform is built on the largest proprietary dataset of interview answers globally – we’re a data company at heart, and we’ve seen the power of data-driven people methodology in transforming how organisations hire and retain good talent.
So, when it came to building a new Competency Framework that could be leveraged globally for hiring for any role at any scale, of course, we used a ground-up, data-led methodology that bridges the gap between organisational psychology and AI.
Conventional frameworks are typically crafted through expert interviews and focus groups. While valuable, they tend to be subjective, static, and too slow to keep pace with evolving job demands. As roles become more fluid and technology augments or replaces task-based skills, organisations need a new way to understand the human capabilities that genuinely matter for performance.
We wanted to identify enduring, job-agnostic competencies that reflect what drives success in a modern workplace – capabilities like adaptability, resilience, learning agility, and customer orientation.
(Why competencies and not just skills? Read why here.)
Sapia.ai’s methodology is rooted in the science of human behaviour but powered by cutting-edge AI. We asked two core questions:
The answer to both: yes.
We began with a rich dataset of over 37,000 job descriptions across industries and role types. Using large language models (LLMs) and advanced NLP techniques, we extracted over 200,000 behavioural descriptors. These were distilled down through a four-step process:
This resulted in a refined list of 25 human-centric competencies, each with clear behavioural indicators and practical relevance across a wide range of roles.
Our framework is intelligent, but importantly, it’s adaptive. Organisations can apply this methodology to their own job descriptions to discover custom competencies. This bottom-up, role-data-led approach ensures alignment to real work, not just theoretical models.
And because the framework integrates directly with our AI-powered hiring tools, you get a connected system that brings your talent strategy to life.
Our framework comes to life in the following tools:
Skills alone cannot predict success. Competencies do. As AI continues transforming how we work, Sapia.ai’s Competency Framework offers a scalable, scientific, and fair foundation for hiring and developing the talent of tomorrow.
If you’re a CHRO or Head of Recruitment at an enterprise today, chances are you’ve been inundated with messages about the importance of “skills-based hiring.” LinkedIn’s recent Work Change Report (2025) is full of compelling data: a 140% increase in the rate at which professionals are adding new skills to their profiles since 2022, and a projection that by 2030, 70% of the skills used in most jobs today will have changed.
This is essential reading. But there’s a missed opportunity: the singular focus on “skills” fails to acknowledge the real metric that talent leaders need to be using to future-proof their workforce — competencies.
But skills on their own — even soft ones — are generic, disjointed, and often disconnected from real-world performance. In contrast:
Put simply, competencies answer the all-important question: Can this person apply the right skills, in the right way, at the right time, to deliver results in our environment?
The Work Change Report outlines a future where job titles are fluid, roles evolve quickly, and AI is a constant disruptor. This creates three massive challenges for hiring at scale:
Skills alone don’t tell us whether someone can succeed in a role that will look different 12 months from now. But competencies can. Because they measure not just what a person knows, but how they apply it.
The LinkedIn report highlights a critical insight: organisations now prioritise agility in entry-level hiring. And there’s a good reason for that. With professionals expected to hold twice as many jobs over their careers compared to 15 years ago, adaptability is not just a nice-to-have. It’s core to success.
But you can’t measure agility with a keyword on a CV. You measure it by looking at competencies like:
When you shift the focus away from skills to behavioural competencies that can be defined, observed, and assessed in structured ways, you open yourself up to a much more dynamic and more useful way of managing talent.
To hire effectively at scale, particularly in a technology-driven world of work, talent leaders must shift their lens:
LinkedIn’s data shows that people are learning more skills more quickly than ever. But the real question for talent leaders like you is: Are those skills being applied in ways that drive value? Are we hiring for task proficiency or performance?
The truth is that the organisations that will thrive in an AI-driven, skills-fluid economy aren’t the ones chasing the next hot skill. They’re the ones designing systems to identify, develop and scale competence.
Sapia.ai has developed a comprehensive Competency Framework using a data-driven approach. Download the full paper here.
Every day, we read stories of increased fake or AI-assisted applications. Tools like LazyApply are just one of many flooding the market, driving up applicant volumes to never-before-seen levels.
As an overwhelmed hiring function, how do you find the needle in the haystack without using an army of recruiters to filter through the maze?
At Sapia.ai, we help global enterprises do just that. Many of the world’s most trusted brands, such as Qantas Group, have relied on our hiring platform as a co-pilot for better hiring since 2020.
Our Chat Interview has given millions of candidates a voice they wouldn’t have had – enabling them to share in their own words why they’re the best fit for the role. To find the people who belong with their brands, our customers must trust that their candidates represent themselves. Thus, they want to trust that our AI is analysing real human answers—not answers from a machine.
The Rise of GPT
When ChatGPT went viral in November 2022, we immediately adopted a defensive strategy. We had long been flagging plagiarised candidate responses, but then, we needed to act fast to flag responses using artificially generated content (‘AGC’).
Many companies were in the same position, but Sapia.ai was the only company with a large proprietary data set of interview answers that pre-dated GPT and similar tools: 2.5 billion words written by real humans.
That data enabled us to build a world-first:- an LLM-based AGC detector for text-based interviews, recently upgraded to v2.0 with 99% accuracy and a false positive rate of 1%. An NLP classification model built on Sapia.ai proprietary data that operates across all Sapia.ai chat interviews.
Full Transparency with Candidates
Because we value candidate trust as much as customer trust, we wanted to be transparent with candidates about our ability to detect artificially generated content (AGC). As an LLM, we could identify AGC in real time and warn candidates that we had detected it.
This has had a powerful impact on candidate behaviour. Since our AGC detector went live, we have seen that the real-time flagging acts as a real-time disincentive to use tools like ChatGPT to generate interview responses.
The detector generates a warning if 3 or more answers are flagged as having artificially generated content. The Sapia.ai Chat Interview uses 5 open-ended interview questions for volume hiring roles, such as retail, contact centre, and customer service, and 6 questions for professional roles, such as engineers, data scientists, graduates, etc.
Let’s Take a Closer Look at the Data…
We see that using our AGC detector LLM to communicate live with candidates in the interview flow when artificial content has been detected has a positive effect on deterring candidates from using AI tools to generate their answers.
The rate of AGC use declines from 1 question flagged to 5 questions – raising the flag on one question is generally enough to deter candidates from trying again.
The graph below shows the number of candidates, from a total of almost 2.7m, that used artificially generated content in their answers.
Differences in AGC Usage Rate by Groups
We see no meaningful differences in candidate behaviour based on the job they are applying for or based on geography.
However, we have found differences by gender and ethnicity – for example, men use artificially generated content more than women. The graph below shows the overall completion ratios by gender – for all interviews on the left and for interviews where the number of questions with AGC detected is 5 or more on the right.
Perception of Artificially Generated Content by Hirers.
We’re curious to understand how hirers perceive the use of these tools to assist candidates in a written interview. The creation of the detector was based on the majority of Sapia.ai customers wanting transparency & explainability around the use of these tools by candidates, often because they want to ensure that candidates are using their own words to complete their interviews and they want to avoid wasting time progressing candidates who are not as capable as their chat interview suggests.
However, some of our customers feel that it’s a positive reflection of the candidate, showing that they are using the tools available to them to put their best foot forward.
It’s a mix of perspectives.
Our detector labels it as the use of artificially generated content. It’s up to our customers how they use that information in their decision-making processes.
This concept of having a human in the loop is one of the key dimensions of ethical AI, and we ensure that it is used in every AI-related hiring product we build.
Interested in the science behind it all? Download our published research on developing the AGC detector 👇