In recent years, a flood of pre-employment talent assessment tools has come into the market.
From automating initial candidate interviews to conducting online skills or personality testing, these tools help recruiters look beyond the CV to find the best candidates for every job.
In today’s competitive world of work, recruiters and hiring managers want to be sure that every decision is the right decision. As competition between companies for the very best talent has increased and as more candidates apply for fewer roles, just filling a role is no longer an option. Reviewing CVs and assessing candidates is time-consuming and costly, and recruiters need to be confident that they are delivering value to their clients in both costs and the quality of candidates.
That’s why recruiters and employers alike are seeking ways to take the guesswork out of the process in identifying talent who will be the best fit for the team, work most productively and stay in the role longer.
In this guide, Sapia explores the types of tools available, the insights they can provide and how they can benefit your business. We’ll also provide some guidelines for helping you to assess which tools could deliver the best return on your investment.
Why use talent assessment tools?
Pre-employment assessment of candidates is, of course, the very reason that recruiters exist.
Talent assessment tools have been developed to help make that process easier, faster and more cost-effective. The tools leverage technology to more accurately identify the best talent for a role and predict their fit and performance in that organisation.
The benefits of candidate evaluation software can include:
- Go beyond the CV – Leverage technology to focus on skills as well as things such as cultural fit, aptitude, cognitive abilities and more to identify candidates “most likely” to be successful.
- Increase productivity – Spend less time on manual screening and more time on higher-value briefs and candidates.
- Decrease costs – Automated processes can reduce the costs of manual talent review and assessment, dramatically reducing overall hiring costs. Redirect savings to investments in people or technology.
- Remove bias from the process – Data-driven tools can take unconscious bias out of the assessment equation to focus on skills and fit. Sapia’s Ai-enabled text interview automation platform, for example, offers blind screening at its best to help build workplace diversity.
- Build deeper talent pools – Use technology to extend your reach to more candidates. The best tools integrate with your applicant tracking system to seamlessly run hiring processes and build ‘ready to go’ talent capabilities.
- Fill roles faster – With the ability to screen more candidates in less time you can confidently begin interviewing sooner.
- Be successful – With the right talent assessment tools working for your business, you are more likely to achieve the best talent outcomes every time.
- Improve the candidate experience – Provide an engaging and enjoyable experience for candidates. Some tools including Sapia’s platform automate personalised feedback that is always appreciated by candidates.
- Know what candidates want – Using data to understand candidates, their motivations and their expectations can help managers be better prepared for onboarding. Building profiles of successful candidates will also provide insight for the next similar brief.
- Make hiring decisions with confidence – Objective evidence and data-driven findings can help make every decision a better decision.
Types of talent assessment tools
The wide range of available talent assessment tools can be generally grouped into three areas of assessment: Work behaviours; Knowledge, skills and experience; Innate abilities and attributes.
Some tools may focus on a single attribute such as coding abilities or English competency while others can combine a range of tests and interview capabilities within one platform.
Once the requirements of a role are understood, the right tools can be chosen to assess those competencies.
1. Learnt knowledge, skills and experience assessments look at candidates’ specific job knowledge, qualifications and work experience. Assessed against the agreed capabilities required for the role, these assessments can be an extremely accurate and effective predictor of a candidate’s performance in the role. Some tools may focus on specific sectors and roles – eg sales, HR, health, hospitality, programming, engineering – while other platforms will cover a range of these with tests that can be customised to specific requirements
Some examples:
- Job knowledge assessments: This type of test measures specific areas of knowledge or skills – often technical – that are considered minimum requirements for a role.
- Skills assessments: Through mobile-driven text conversations, video interviews, multiple-choice quizzes or even online ‘games’, job-specific and general work skills or soft skills can be assessed.
- Coding assessments: There are many tools designed specifically to test and assess candidates’ coding abilities and technical skills. These assessments can be used at the screening stage to filter candidates or during later interview stages where full-scale coding challenges could reflect actual work or challenges the candidate would encounter as an employee. Tools can be a platform and industry-specific.
- General skills: Tools can also address general work skills such as literacy and numeracy, basic typing and data entry, ability to follow instructions, and more.
- Work behaviour assessments: observe actual behaviours and simulations that match and help predict real on-the-job requirements. Job simulation exercises and work sample tests give candidates an opportunity to demonstrate their abilities and skills. They allow employers to assess job-specific skills and analyse candidates’ capabilities in decision-making and prioritising, multi-tasking or their ability to work under pressure. Tasks can be highly customised to specific responsibilities of the role and of the organisation.
2. Innate abilities and attributes assessments focus on traits that are not job-specific such as personality, interests and cognitive abilities including problem-solving, logic skills, reading comprehension and learning ability. These universal human traits have proven to be effective indicators of job performance and cultural fit. Softskill testing: Tools can be used for talent evaluation across a range of qualities and personality traits such as teamwork, sales ability, good judgement, integrity, curiosity, impact, ownership and independence.
Some examples:
- Automated Interviews: AI-driven platforms can automate interview processes and provide a better experience for recruiters, hirers and candidates alike. Platforms like Sapia’s automated text interview can provide a true advantage, especially at the screening stage for large volume recruitment briefs such as customer-facing retail or service teams.
- Candidate ranking: Powered by artificial intelligence and machine learning, many assessment tools will analyse results to grade and rank candidates. Rankings around different criteria can save time and provide the confidence that you are focused on the right candidates.
- Cognitive screening: These tools provide insight into how candidates think, solve problems and learn. Insights can help hirers understand future management needs to prepare and support new employees to be successful in their role.
- Integrity assessments: Assessing attitudes and experiences relating to honesty, reliability and trust.
- Psychographic screening: Insights into a candidate’s personality, values and interests can help assess their fit within a team and within an organisation’s culture and values.
- Bias-free screening: Unconscious bias removed from the process so candidates are assessed on their skills and decisions are not influenced by a candidate’s gender, age, ethnicity and other personal credentials that do not affect their ability to do the job. Sapia’s mobile-first, text interview platform is an industry leader in blind interviewing.
10 questions to help you choose the best talent assessment tools
Saving time and money, filling roles with better quality candidates. That’s the key reason talent assessment tools are indispensable across the recruitment industry and in every employment sector. But with the plethora of tools available, how do you decide which ones are right for your organisation? Which talent assessment tools will best contribute to your success?
Before you invest, Sapia’s talent assessment tool checklist can help:
1) What do you need to know?
As an experienced recruiter, you can probably already recognise where your talent assessments sometimes fall short or you think they could be better. The data insight that can support your recruitment and hiring processes will be different for everyone and will vary according to:
- industry or sector specialisation
- experience level of candidates – entry-level to management and C-suite roles
- qualifications, skills or personality traits required for roles
- nature of brief– specialist technical roles or large volume team roles
When you know what you need to measure, you can start narrowing your search to identify the tools that can give you what you’re looking for.
2) How will the findings be presented?
Consider the format and depth of the feedback that different tools can provide. Is a numerical ranking of candidates sufficient or will in-depth analysis, comparisons and recommendations better serve your needs?
3) Do assessments support the hiring organisation’s brand values and strategy?
Consider whether the tools positively support an organisation’s employment policies and practices such as workplace diversity and inclusion, language or numeric competencies and minimum skills requirements.
4) Do tools remove bias from talent assessment?
Removing unconscious bias from the talent assessment process is a priority for organisations looking to improve workplace diversity and inclusion. While a text-based chat platform (such as Sapia) can effectively take bias out of the equation, video submissions bring the opportunity for bias front and centre of the process.
5) Do the tools support the interview process?
Few, if any, hiring decisions should ever be made solely on the basis of talent assessment tools rankings or findings. Make sure tools can provide meaningful data that will enhance the interview process. Many tools will help identify areas that should be explored further in the interview process and even suggest questions to help shape the interview.
6) How will the tool integrate with existing systems?
The best tools will integrate with your existing systems and processes and with other tools. You want to be sure that you can combine data from different tools to create meaningful reports and records. Tools that integrate with your existing ATS (Applicant Tracking System) are likely to deliver the best savings in time and effort.
7) What will candidates think?
Every candidate deserves a fair and positive experience, whether they are successful or not. Choose tools that are easy and engaging to use, appropriate for the role and tools that will enhance, not undermine, your employer brand.
The best tools also deliver value by allowing candidates to provide feedback on their engagement with tools after the assessment process.
https://sapia.ai/blog/predictivehire-is-named-candidate-experience-solution-of-the-year/
8) How do I find out what tools are best?
Ask your industry colleagues for recommendations and search the web for reviews and guides like this one that can help you navigate a very crowded market. When you think you’ve found the tools that will work best for you, your clients and your candidates, ask vendors to show you how their assessment tools can deliver with a personal demonstration or even a free trial.
9) Have you analysed the costs?
You want to be sure that your investment will pay its way. Take the time to consider the value of the candidate feedback or assessment of different tools will provide. Many vendors provide online calculators to help you estimate the return on your investment.
10) Do the tools support best practice?
Talent assessment tools can provide objective, measurable insights that other more traditional recruitment methods can’t provide. But technology has its limits too. Make sure that a positive candidate experience remains a priority – nobody wants to feel discriminated against or feel embarrassed or violated by intrusive personality testing.
Make sure also that in focusing on one key skill or trait, you’re not missing a candidate’s true strengths. In short, don’t use your talent assessment tools as the recruitment tool, use them in conjunction with all the other methods, tools and skills in your recruitment toolbox.
The Buyers Guide to Navigating Ai Hiring Solutions
Leveraging objective data to augment decisions like who to hire and who to promote is critical if you are looking to minimise unconscious preferences and biases, which can surface even when those responsible have the best of intentions.
The greatest algorithm on earth is the one inside of our skull, but it is heavily biased. Human decision making is the ultimate black box.
Only with data, the right data alongside human judgment can we get any change happening. And clearly, what your employees and candidates are now looking for, is change. We hope that the debate over the value of diverse teams is now over. There is plenty of evidence that diverse teams lead to better decisions and therefore, business outcomes for any organisation.
This means that CHROs today are being charged with interrupting the bias in their people decisions and expected to manage bias as closely as the CFO manages the financials. But the use of Ai tools in hiring and promotion requires careful consideration to ensure the technology does not inadvertently introduce bias or amplify any existing biases. To assist HR decision-makers to navigate these decisions confidently, we invite you to consider these 8 critical questions when selecting your Ai technology. You will find not only the key questions to ask when testing the tools but why these are critical questions to ask and how to differentiate between the answers you are given.
This guide is presented by Sapia whose AI-powered, text chat talent assessment tool has a user satisfaction rate of 99%.
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Neuroinclusion by design. Not by exception.
Why neuroinclusion can’t be a retrofit and how Sapia.ai is building a better experience for every candidate.
In the past, if you were neurodivergent and applying for a job, you were often asked to disclose your diagnosis to get a basic accommodation – extra time on a test, maybe the option to skip a task. That disclosure often came with risk: of judgment, of stigma, or just being seen as different.
This wasn’t inclusion. It was bureaucracy. And it made neurodiverse candidates carry the burden of fitting in.
We’ve come a long way, but we’re not there yet.
Shifting from retrofits to inclusive-by-design
Over the last two decades, hiring practices have slowly moved away from reactive accommodations toward proactive, human-centric design. Leading employers began experimenting with:
- Sharing interview questions in advance
- Replacing group exercises with structured simulations
- Offering a variety of assessment formats
- Co-designing assessments with neurodiverse candidates
But even these advances have often been limited in scope, applied to special hiring programs or specific roles. Neurodiverse talent still encounters systems built for neurotypical profiles, with limited flexibility and a heavy dose of social performance pressure.
Hiring needs to look different.
Insight 1: The next frontier of hiring equity is universal design
Truly inclusive hiring doesn’t rely on diagnosis or disclosure. It doesn’t just give a select few special treatment. It’s about removing friction for everyone, especially those who’ve historically been excluded.
That’s why Sapia.ai was built with universal design principles from day one.
Here’s what that looks like in practice:
- No time limits — Candidates answer at their own pace
- No pressure to perform — It’s a conversation, not a spotlight
- No video, no group tasks — Just structured, 1:1 chat-based interviews
- Built-in coaching — Everyone gets personalised feedback
It’s not a workaround. It’s a rework.
Insight 2: Not all “friendly” methods are inclusive
We tend to assume that social or “casual” interview formats make people comfortable. But for many neurodiverse individuals, icebreakers, group exercises, and informal chats are the problem, not the solution.
When we asked 6,000 neurodiverse candidates about their experience using Sapia.ai’s chat-based interview, they told us:
“It felt very 1:1 and trustworthy… I had time to fully think about my answers.”
“It was less anxiety-inducing than video interviews.”
“I like that all applicants get initial interviews which ensures an unbiased and fair way to weigh-up candidates.”
Insight 3: Prediction ≠ Inclusion
Some AI systems claim to infer skills or fit from resumes or behavioural data. But if the training data is biased or the experience itself is exclusionary, you’re just replicating the same inequity with more speed and scale.
Inclusion means seeing people for who they are, not who they resemble in your data set.
At Sapia.ai, every interaction is transparent, explainable, and scientifically validated. We use structured, fair assessments that work for all brains, not just neurotypical ones.
Where to from here?
Neurodiversity is rising in both awareness and representation. However, inclusion won’t scale unless the systems behind hiring change as well.
That’s why we built a platform that:
- Doesn’t rely on disclosure
- Removes ambiguity and pressure
- Creates space for everyone to shine
- Measures what matters, fairly
Sapia.ai is already powering inclusive, structured, and scalable hiring for global employers like BT Group, Costa Coffee and Concentrix. Want to see how your hiring process can be more inclusive for neurodivergent individuals? Let’s chat.
Blog
Skills Measurement vs Skills Inference – What’s the Difference and Why Does It Matter?
There’s growing interest in AI-driven tools that infer skills from CVs, LinkedIn profiles, and other passive data sources. These systems claim to map someone’s capability based on the words they use, the jobs they’ve held, and patterns derived from millions of similar profiles. In theory, it’s efficient. But when inference becomes the primary basis for hiring or promotion, we need to scrutinise what’s actually being measured and what’s not.
Let’s be clear: the technology isn’t the problem. Modern inference engines use advanced natural language processing, embeddings, and knowledge graphs. The science behind them is genuinely impressive. And when they’re used alongside richer sources of data, such as internal project contributions, validated assessments, or behavioural evidence, they can offer valuable insight for workforce planning and development.
But we need to separate the two ideas:
- Skills Measurement: Directly observing or quantifying a skill based on evidence of actual performance.
- Skills Inference: Estimating the likelihood that someone has a skill, based on indirect signals or patterns in their data.
The risk lies in conflating the two.
The Problem Isn’t Inference of Skills. It’s the Data Feeding It
CVs and LinkedIn profiles are riddled with bias, inconsistency, and omission. They’re self-authored, unverified, and often written strategically – for example, to enhance certain experiences or downplay others in response to a job ad.
And different groups represent themselves in different ways. Ahuja (2024) showed, for example, that male MBA graduates in India tend to self-promote more than their female peers. Something as simple as a longer LinkedIn ‘About’ section becomes a proxy for perceived competence.
Job titles are vague. Skill descriptions vary. Proficiency is rarely signposted. Even where systems draw on internal performance data, the quality is often questionable. Ratings tend to cluster (remember the year everyone got a ‘3’ at your org?) and can often reflect manager bias or company culture more than actual output.
Sophisticated ≠ Objective
The most advanced skill inference platforms use layered data: open web sources like job ads and bios, public databases like O*NET and ESCO, internal frameworks, even anonymised behavioural signals from platform users. This breadth gives a more complete picture, and the models powering it are undeniably sophisticated.
But sophistication doesn’t equal accuracy.
These systems rely heavily on proxies and correlations, rather than observed behaviour. They estimate presence, not proficiency. And when used in high-stakes decisions, that distinction matters.
Transparency (or Lack Thereof)
In many inference systems, it’s hard to trace where a skill came from. Was it picked up from a keyword? Assumed from a job title? Correlated with others in similar roles? The logic is rarely visible, and that’s a problem, especially when decisions based on these inferences affect access to jobs, development, or promotion.
Presence ≠ Proficiency
Inferred skills suggest someone might have a capability. But hiring isn’t about possibility. It’s about evidence of capability. Saying you’ve led a team isn’t the same as doing it well. Collecting or observing actual examples of behaviour allows you to evaluate someone’s true competence at a claimed skill.
Some platforms try to infer proficiency, too, but this is still inference, not measurement. No matter how smart the model, it’s still drawing conclusions from indirect data.
By contrast, validated assessments like structured interviews, simulations, and psychometric tools are designed to measure. They observe behaviour against defined criteria, use consistent scoring frameworks (like Behaviourally Anchored Rating Scales, or BARS), and provide a transparent, defensible basis for decision-making. In doing this, the level or proficiency of a skill can be placed on a properly calibrated scale.
But here’s the thing: we don’t have to choose one over the other.
A Smarter Way Forward: The Hybrid Model
The real opportunity lies in combining the rigour of measurement with the scalability of inference.
Start with measurement
Define the skills that matter. Use structured tools to capture behavioural evidence. Set a clear standard for what good looks like. For example, define Behaviourally Anchored Rating Scales (BARS) when assessing interviews for skills. Using a framework like Sapia.ai’s Competency Framework is critical for defining what you want to measure.
Layer in inference
Apply AI to scale scoring, add contextual nuance, and detect deeper patterns that human assessors might miss, especially when reviewing large volumes of data.
Anchor the whole system in transparency and validation
Ensure people understand how inferences are made by providing clear explanations. Continuously test for fairness. Keep human oversight in the loop, especially where the stakes are high. More information on ensuring AI systems are transparent can be found in this paper.
This hybrid model respects the strengths and limits of both approaches. It recognises that AI can’t replace human judgement, but it can enhance it. That inference can extend reach, but only measurement can give you higher confidence in the results.
The Bottom Line
Inference can support and guide, but only measurement can prove. And when people’s futures are on the line, proof should always win.
References
Ahuja, A. (2024). LinkedIn profile analysis reveals gender-based differences in self-presentation among Indian MBA graduates. Journal of Business and Psychology.
Blog
Making Healthcare Hiring Human with Ethical AI
Hiring for care is unlike any other sector. Recruiters are looking for people who can bring empathy, resilience, and energy to the most demanding human roles. Whether it’s dental care, mental health, or aged care, new hires are charged with looking after others when they’re most vulnerable. The stakes are high.
Hiring for care is exactly where leveraging ethical AI can make the biggest impact.
Hiring for the traits that matter
The best carers don’t always have the best CVs.
That’s why our chat-based AI interview doesn’t screen for qualifications. It screens for the the skills that matter when caring for others. The traits that define a brilliant care worker, things like:
Empathy, Self-awareness, Accountability, Teamwork, and Energy.
The best way to uncover these traits is through structured behavioural science, delivered through an experience that allows candidates to open up. Giving candidates space to give real-life, open-text answers. With no time pressure or video stress. Then, our AI picks up the signals that matter, free from any demographic data or bias-inducing signals.
Candidates’ answers to our structured interview questions aren’t simply ticking boxes. They’re a window into how someone shows up under pressure. And they’re helping leading care organisations hire people who belong in care and those who stay.
Inclusion, built in
Inclusivity should be a core foundation of any talent assessment, and it’s a fundamental requirement for hirers in the care industry.
When healthcare hirers use chat-based AI interviews, designed to be inclusive for all groups, candidates complete their interviews when and where they choose, without the bias traps of face-to-face or phone screening. There are no accents to judge, no assumptions, just their words and their story.
And it works:
- 91.8% of carer candidates complete their interviews
- Carer candidates report 9/10 Candidate Satisfaction with their interview experience
- 80% of candidates would recommend others to apply
- Every candidate receives personalised feedback, regardless of the outcome
Drop-offs are reduced, and engagement & employer brand advocacy go up. Building a brand that candidates want to work for includes providing a hiring experience that candidates want to complete.
Real outcomes in care hiring
Our smart chat already works for some of the most respected names in healthcare and community services. Here’s a sample of the outcomes that are possible by leveraging ethical AI, a validated scientific assessment, wrapped in an experience that candidates love:
Anglicare – a leading provider of aged care services
- Time-to-offer dropped from 40+ days to just 14
- Candidate pool grew by 30%
- Turnover dropped by 63%
Abano Healthcare – Australasia’s largest dental support organisation
- 1,213 recruiter hours saved in the first month (67 hours per individual hiring team member)
- $25,000 saved in screening and interviewing time
Berry Street – a not for profit family & child services organisation
- Time-to-hire down from 22 to 7 days
- 95.4% of candidates completed their chat interviews
A smarter way to hire
The case study tells the full story of how Sapia.ai helped Anglicare, Abano Healthcare, and Berry Street transform their hiring processes by scaling up, reducing burnout, and hiring with heart.
Download it here: