Author: Buddhi Jayatilleke, Chief Data Scientist, Sapia.ai
We all know the value of company culture. Culture forms the shared values, beliefs, and practices that shape the behaviors and interactions of employees within an organization. It is like the collective personality of a company that shapes everything from employee satisfaction to customer experiences.
While culture is a collective outcome, it isn’t something that just happens automatically. Leaders are responsible for defining the underlying values and must remain intentional about sustaining the desired organizational culture. A key part of culture is who you hire and how you hire them. We hear phrases like “Culture Fit” and “Culture Add” in the hiring process. These are part of “who” you hire and are used to both accept and reject candidates. But “how” you hire reflects your culture and creates the virtuous (or vicious) cycle that amplifies (or derails) an organizational culture.
“If you hire people just because they can do a job, they’ll work for your money. But if you hire people who believe what you believe, they’ll work for you with blood and sweat and tears.” – Simon Sinek
The above quote, attributed to Simon Sinek, makes a great point, but how do you find people “who believe what you believe”? In other words, how do you attract and hire individuals who will thrive in and uplift your culture? The experience through the candidate’s journey plays a key role.
And today we have a new enabler. Artificial Intelligence (AI).
AI certainly can not create culture. Culture is innately a human construct. However, AI as a tool can help sustain, project, and amplify culture through effective engagement with employees and candidates. From job description writing to employee coaching, new generative AI tools, built on ethical principles, can help organizations instill their culture through sourcing to onboarding.
Here I highlight four key steps that leaders should pay attention to for building the right “hiring culture” and how AI can help. Due to my own experience in the selection process, more emphasis is placed there, but all 4 steps are equally important.
1. First Impressions Matter: The Job Description and Career Site
The job description is often the first interaction potential employees have with your organization. The language used, the values highlighted, and even the requirements listed can say a lot about your culture. For instance, emphasizing teamwork and collaboration suggests a culture valuing collective success over individual achievements. Using gender-neutral language can help attract a candidate pool that is gender diverse. These indicators give candidates an upfront understanding of what you prioritize and allow them to self-select based on fit.
Companies can enhance this first impression by providing more interactive means to get to know the organization rather than using static career websites filled with a lot of content. While some organizations do a great job in structuring the content and including more engaging content such as videos from existing employees, FAQ’s etc, these approaches fail to address questions a potential applicant might have in a timely manner. In a high-volume recruitment scenario, it is impossible to have human recruiters answer thousands of questions via phone or text chat.
This is where smart chatbots built on top of generative AI like Sapia.ai‘s Phai, a careers site assistant, can help. Phai can ingest all the relevant content on a website (or other sources) and then provide fast personalized responses to candidate queries, 24/. Phai not only enhances the experience but also increases the chances of a candidate completing the application process. Chat with Phai yourself by clicking the blue icon in the bottom right of your browser.
2. The Selection Process: What You Value in Candidates
The selection criteria and the selection process are reflections of what the organization values. Prioritizing skills over experience may indicate a culture that values continuous learning and potential. An interview is a common step in the selection process and most of the time it is unstructured and fraught with bias. We can all fall victim to various unconscious biases at this stage (and sometimes practice conscious ones too, unfortunately). As an example, here are 4 common ones that I have noticed in fast-paced growth environments like startups:
- Urgency bias: Rushed decisions to prioritize immediate needs over long-term goals when making hiring decisions. (We need to fill this role this week!)
- Confirmation bias: The tendency to interpret or favor information that confirms one’s preconceptions and ignore other relevant details. (This candidate comes from ABC Inc. They must be good!)
- Halo effect: Tendency to base an overall impression of a candidate on one positive trait or experience. (Wow! Their presentation slides looked amazing!)
- Dunning-Kruger effect: Individuals with limited skills or experience might overestimate their abilities in an interview and this overconfidence can sometimes be persuasive. Leaders who are inexperienced in a specific subject matter, for example, a non-technical founder who is recruiting an engineering manager, can be susceptible to this bias.
One way you can interrupt these human biases is to include an AI assistant in the process. This is where tools like Sapia.ai’s Chat Interview™ can help. Chat Interview™ conducts a chat-based structured interview that is scored by AI. Structured interviews are found to be high in validity and low in bias among the many options available to assess candidates. Hiring managers get access to a detailed report called Talent Insights (Ti) that can challenge some of their biased views and help them make better hiring decisions. For instance, independent research conducted using the Sapia.ai Chat Interview™ found a 36% reduction in the gender gap relative to recruitment without AI. One of the practices the Sapia.ai Chat Interview™ encourages is asking value-based interview questions to gauge alignment with company values. For example “Could you tell me about a time when you went above and beyond to help a team member at work?”.
3. Onboarding: The Introduction to Culture
The onboarding process is a critical stage for instilling organizational culture in new hires. Effective onboarding programs that align new employees with organizational values and expected behaviors can have a lasting impact on their integration and success within the company. As more companies become distributed and rely on remote work, part of company culture can be collaborating effectively over tools like wikis, and messaging apps like Slack and email. This requires making sure a new hire knows how to use these tools well and content norms specific to the company. This also brings to light the importance of “connection” as part of building culture, as in a remote work environment you have to be more intentional in building connections than when working together in an office. You can read more on this in “HR for the world of tomorrow“ where we discuss the changing landscape of work and how smart chat is the new medium for building connections.
4. Feedback and Continuous Improvement: Sustaining the Culture
How feedback is provided during the hiring process and the onboarding period can also be a cultural indicator. A culture that values growth and development is likely to provide constructive feedback to candidates (whether they are hired or not) and to new employees in an effective manner. This is the philosophy that Sapia.ai Chat Interview™ follows with My Insights, a feedback email every candidate gets after completing the chat interview that includes personality insights and coaching tips. The Sapia.ai Talent Insights report provides similar insights to the hiring managers that help them prepare for onboarding a new hire.
In essence, every aspect of the hiring process – from the job description to the final decision – is a reflection of your organizational culture. By being mindful of this, organizations can ensure they not only attract the right talent but also reinforce the culture they aspire to maintain and develop. AI can be used as a tool to mitigate biases, form a consistent process, and enhance the candidate experience to better reflect the company culture.
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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 👇