It is widely thought that Thomas Edison invented the concept of the job interview back in the early 1900s. To screen candidates, he would ask them to join him at a restaurant and eat a bowl of soup while he watched. He could pick out the losing candidates by their tendency to season their soup before eating it. According to Edison, premature salt-and-peppering speaks to a person’s over-reliance on assumptions. If you’re a true visionary, he posited, you leap into your soup face-first.
The soup test is definitely out there. And, given what we now know about psychology and candidate experience, it is not, strictly speaking, scientifically valid. But this exercise was first tested more than 100 years ago, so maybe we can forgive Edison for filling the holes in his data with social experiments.
Funnily enough, though, things haven’t changed much since Edison souped up his hiring game. Initial face-to-face job interviews remain the predominant tool of hiring managers. There are benefits to in-person interviews, but the deficits certainly outweigh the benefits. Simply put, the practice is infused with all manner of biases, unfairnesses, inefficiencies, and oddities. In the early 1900s, we had soup – now we have inscrutable corporate-isms, and bizarre group tasks with arbitrary scoring criteria.
We’re naming names: The ‘pen-drop’ test
Let’s say you’re looking to fill a position where quick thinking and adaptability are the two most important skills. You want your candidates to think fast, and think smart, especially when faced with sudden adversity. How do you find these people?
There is no perfect answer. People are people, after all. But there are far better ways to find out than dropping a pen in the middle of an interview to see whether or not a candidate picks it up for you. The ‘pen-drop’ test assumes that the quickest candidates are the most adaptable, and are the highest in empathy. But we have more reliable predictors for these, predictors subject to far fewer variables. The quickest pen picker-upper on a given day may not be the best lateral thinker, or the most open – they may have merely been the shortest candidate, or the most flexible candidate, or the candidate closest to the pen. Because you don’t have a control, or any way to account for variables such as these, can you really trust the findings?
Yes, the pen-drop test is an extreme example of a screening exercise that is only tenuously related to its desired outcome. But we have all, at some point in our working lives, participated in strange tasks and odd jobs during interviews. The greater point is this: Even the best-planned exercises are not a viable substitute for sound scientific measurement.
The fairest way to conduct job interviews AND get candidate data you can trust
The HEXACO personality inventory has at least three major dimensions relating to the test of a quick-thinking, empathic person: Extraversion, agreeableness, and conscientiousness. If you can assess a candidate using the HEXACO inventory, you might learn that the candidate is:
- High in trait extraversion, which makes them more likely to take the lead in social situations, and interpret social cues
- Low in trait conscientiousness, which makes them more likely to go with the flow, and adapt to unexpected situations
- High in trait agreeableness, which suggests they are more ‘willing to compromise and cooperate with others’.
And that’s only the start of what you might learn. By using an Ai-based recruitment or hiring tool, with a HEXACO personality modelling function, you have a simple, trustworthy, accurate, and fair way to sort your quick thinkers from your leaders, your leaders from your long-term planners, and so on.
That’s the essence of what a smart interviewer can do, and why we developed the world’s first smart interviewer. You no longer need to think up some strange post-interview exercise where you pull unsuspecting candidates into an impromptu indoor hockey game. You can simply:
- Entrust your first interviews to Smart Interviewer, our quick-learning Ai smart interviewer
- Receive a full report on each candidate, including their responses and where they sit on the HEXACO personality scale
- Instantly match candidates against the values you desire most (for example, ‘quick-thinking’ or ‘adaptable’.)
(We’re not the fun police, of course. If your approach to offering first-rate candidate experience involves a blind-folded three-legged race, count us in. Just make sure you have a smart interview waiting at the finish line. Fun, then statistical validity. Best of both worlds.)
We all want a world filled with better, fairer, simpler interviews. How will you go about it? Data, or gut-feel? Soup, or science?
Related
Blog
Reinventing the Competency Framework: A Data-Driven Approach for the AI Era
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.
Why Rethink Competency Frameworks?
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.)
Our Approach: Where AI Meets I/O Psychology
Sapia.ai’s methodology is rooted in the science of human behaviour but powered by cutting-edge AI. We asked two core questions:
- Can we make competency discovery agile, scalable, and evidence-based?
- Can we use AI to automate the process without losing the rigour of traditional psychology?
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:
- Behavioural Descriptor Extraction
- Clustering and Labeling
- Cluster Analysis by I/O Psychologists
- Thematic Categorisation and Definition of Competencies
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.
Built to Scale. Built to Adapt.
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:
- Job Analyser – Starting with a job description, it creates a unique competency profile for each role to build tailored structured interviews in seconds.
- Structured Chat-based Interviews that assess candidates’ responses according to the competency profile for consistent candidate assessment.
- Talent Insights Reports from every interview with deep reasoning and explainability for fair and objective hiring decisions.
- Phai Career Coach for internal mobility and employee growth that considers their competency strengths and career aspirations.
The Future of Talent Acquisition & Development is Competency-First
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.
Want to see how it works? Download the full framework.
Blog
It’s Time to Stop Hiring for Skills, and Start Hiring for Competencies
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.
Skills vs Competencies: The Crucial Distinction
- Skills are task-specific capabilities. Think Python programming, Excel, or even negotiation.
- Soft skills refer to interpersonal or behavioural qualities like adaptability, communication, and resilience.
But skills on their own — even soft ones — are generic, disjointed, and often disconnected from real-world performance. In contrast:
- Competencies are clusters of skills, knowledge, behaviours and abilities that are observable, measurable, and context-specific.
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?
Why Competencies Matter More Than Ever
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:
- Roles are changing faster than static skill frameworks can keep up
- Job candidates may have non-linear, cross-functional backgrounds
- The shelf-life of technical skills is shrinking rapidly
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.
Adaptive Talent: The New Competitive Advantage
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:
- Learning agility
- Change resilience
- Cross-functional collaboration
- Problem-solving in ambiguous contexts
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.
Building a Competency-Based Talent Framework
To hire effectively at scale, particularly in a technology-driven world of work, talent leaders must shift their lens:
- Define Role-Specific Competencies: Move beyond job descriptions based on qualifications or vague skill sets. Break roles down into measurable competencies that reflect current and emerging performance expectations. This step is crucial for organisations to be able to accurately assess role-fit in the next stages. Sapia.ai does this automatically, taking job descriptions and building role-specific competency models in seconds.
- Assess Competencies Fairly and Objectively: Use structured behavioural interviews, ideally at scale. These provide a much more accurate picture of a candidate’s readiness than self-reported skills or credentials. Sapia.ai’s AI powered interviews enable competency assessment, at scale.
- Build Pathways for Development and Internal Mobility: A competency framework makes it easier to identify transferable strengths, development gaps, and future-fit potential. It gives employees clarity on how to grow within the business. Using an AI-powered coach can help ensure that talent is being continuously developed against the organisation’s competency framework.
The Future of Work Requires Depth, Not Just Breadth
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.
Keen to Shift to Competencies, but Lacking a Framework?
Sapia.ai has developed a comprehensive Competency Framework using a data-driven approach. Download the full paper here.
Blog
The AGC Debate: Are AI-Written Interview Answers a Red Flag or Smart Strategy?
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 👇