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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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 👇