Organisational diversity is still an issue, and the cost is real.
You would think in this day and age organisational diversity would be a moot point. With global social reforms across gender, sexuality, disability and race equality, one could believe the challenge of diversity has been overcome.
Sadly, this is not the case. Some fu(cked) facts:
- In 2018, unemployment of ethnic minority groups in the United States made up 44.9% of total unemployment.The worst-off being Black or African American, despite the fact they represent less than 14% of the total population.[1]
- In Australia, the average full-time weekly wage for a woman is 15.3% less than a man; and women will retire with less than half the superannuation of a man.[2]
- In the UK, men with physical impairments generally experience pay gaps in the range of 15% to 28%, depending on the nature of the disability. The difference between non-disabled women’s pay and that of women with physical impairments ranges from 8% to 18%.[3]
- In the United States, 42% of Transgender American’s are unemployed and 31% are living at the poverty level.[4]
So why do we continue to see inequality in employment?
Despite all the awareness and work to improve employment equality and inclusion, there are four commons slip-ups in recruitment which allow for underlying bias to be introduced into the hiring process.
1. Job descriptions
Despite the best of intentions, hiring managers or recruiters can discourage groups of potential applicants. They do so by using restrictive terms which are gendered or ageist. This can extend to unnecessary education standards which are not required to do the role.
2. CV filtering
More often than not, recruiters and hiring managers are overwhelmed with application volumes. To save time CV screening is done for job titles, big brand company names, and favouring certain universities or education providers.
3. Unconscious name filtering
In some instances, unintentionally or intentionally, applicants will be filtered out of the screening process based on their name. Researchers of Harvard and Princeton found that blind auditions increased the likelihood that female musicians would be hired by an orchestra by 25 to 46%. Whilst one seminal study found that African American sounding names had a 50% lower call back rate for an interview when compared with typical White named individuals.
4. All of the unconscious biases
Would you believe there are over 100 different forms of cognitive biases? Confirmation bias, affinity bias, similarity bias, halo effect, horn effect, status quo bias, conformity bias… the list goes on. These biases make diverse hiring an even more difficult process as you don’t even know that you are missing out on the best candidates!
The Bottom Line? Lack of diversity costs.
Time and time again research has shown that diverse organisations are more effective, perform better financially and have higher levels of employee engagement.
A recent McKinsey report, “Delivering through Diversity” showed that organisations with gender-diverse management were 21% more likely to experience above-average profits. Whilst companies with a more culturally and ethnically diverse executive team were 33% more likely to see better-than-average profits. This figure grows to 43% when the board of director level is also diverse in gender, ethnicity, sexual orientation.[5]
More compelling is that for every 1% rise in workforce gender and cultural diversity, there is a corresponding increase of between 3 to 9 per cent in sales revenue![6]
Not only is diversity a social and ethical problem for organisations, but it is also a commercial one.
Assessing all applicants blindly and equally leads to improved diversity, recruitment efficiency, and organisational performance.
Blind screening: Removing information that reveals the candidate’s race, gender, age, names of schools, etc to reduce unconscious bias that creeps into hiring decisions.
For our customer, a global airline, cabin crew are at the heart of delivering great customer experience. With 9000+ cabin crew creating iconic experiences for passengers every day, they want to maintain their strong brand. They intend to do this through hiring the best in customer service to give their applicants an iconic experience.
An iconic brand also attracts an enormous number of applications some of which don’t fit the criteria. Sifting through so many CVs to uncover the right candidates is extremely time-consuming for the recruiters.
Some of the challenges the team faced in their existing processes included:
- Using videos to screen applicants that only added to the cognitive and time load for recruiters and introduced the opportunity for bias.
- Low yield from assessment centres for those applicants shortlisted from screening.
- Maintaining a gender mix in the applicant pool was important. Yet their screening tools were inadvertently omitting men from the process.
The results were amazing.
- No bias in the process with the ratio of men and women unaffected leading to a more diverse set of hires in comparison to prior campaigns.
- Shortlisting completed in 2 hours instead of 2 weeks.
- Over 2.5 times more offers were made compared with the existing process. 100% completion of the process by applicants.
- Average candidate enjoyability score of 9/10.
A post-campaign survey showed a perfect score from the recruitment team rating the technology as faster, fairer and delivering better candidates.
How conversational AI can truly remove bias in screening
No matter the good intentions, humans will always lean on their biases when making decisions. Interrupting bias in recruitment needs a systemic solution. Something that can operate independently, in the absence of a human trusted to do the right thing.
While Sapia does not claim to completely solve for bias within an organisation, using a chat-based assessment at the top of the recruitment funnel will help you to interrupt, manage and therefore change, biases that reduce diversity in hiring.
A fair and inclusive Candidate Experience
Chat is inclusive for all candidates
Candidates chat through text every day. It’s natural, normal and intuitive. Chat interviews provide an opportunity for them to express themselves, in their way, with no pressure.
Playing games to get a job is not relevant. Talking to a camera is not fair. What if you are unattractive, introverted, not the right colour or gender, or don’t have the right clothes? When you use chat over other assessment tools, you’re solving for adoption, candidate satisfaction, inclusivity and fairness. Our platform has a 99% candidate satisfaction score, and a 90% completion rate. Here’s the 2020 Candidate Experience Playbook.
We use an intrinsically blind assessment design
Blind screening means an interview that is truly blind to the irrelevant markers of age, gender and ethnicity. That just can’t see you. And therefore, cannot judge you. Sapia does not use any information other than the candidate responses to the interview questions to infer suitability for the job your candidates are applying for. As a company we call this ‘fairness through unawareness’. The algorithm knows nothing about sensitive attributes and therefore cannot use them to assess a candidate. Sapia only cares if the candidate is suitable for the job, and nothing else.
Why is organizational diversity important?
Are there some examples of organizational dimensions of diversity?
What does diversity mean?
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References
[1] https://builtin.com/diversity-inclusion/diversity-in-the-workplace-statistics
[2] https://humanrights.gov.au/
[3] https://www.equalityhumanrights.com/en/publication-download/research-report-107-disability-pay-gap
[4] https://theconversation.com/transgender-americans-are-more-likely-to-be-unemployed-and-poor-127585
[5] https://www.forbes.com/sites/pragyaagarwaleurope/2018/10/19/how-can-bias-during-interviews-affect-recruitment-in-your-organisation/#79b1c0b81951
[6] https://www.forbes.com/sites/pragyaagarwaleurope/2018/10/19/how-can-bias-during-interviews-affect-recruitment-in-your-organisation/#79b1c0b81951
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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.
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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.
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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 👇