It’s a fact: People “lie on resumés”, whether the format is a LinkedIn profile, or an old-fashioned document.
Checkster reports that 78% of people who applied for a job in 2020 “lied on their resume about experience” or skills.
Another poll by LendEDU found that 34% of LinkedIn users “lie on their resume”, to some extent, on their profiles. Of that number, 55% said they padded out their ‘Skills’ section. To win roles, it seems, many of us are not above a little trickery.
In 2024, when talent acquisition specialists and hiring managers have hours (and sometimes less) to assess candidates, interview them, and woo them, the risk of resumé skill-creep is magnified.
For a rushed and overworked hiring manager, who is fed up with losing talent and looking bad because of it, the process of vetting becomes less about careful analysis and more about keyword-matching. This raises the question: “why do people lie on their CVs?” The answer might lie in the intense competition for jobs and the perceived need to stand out.
All of this culminates in a series of statistics that should not be surprising: According to a 2022 Aptitude Research report of more than 300 HR leaders at major companies, 50% of companies have lost quality talent due to the way they interview and hire.
At the same time, 50% of companies do not measure the ROI of their interview process. One third are not confident in their interviewing game as a whole.
The process is broken.
The resumé is at the heart of a greater efficiency problem. That same Aptitude Research report examined the average company recruitment funnel, laying out the points at which candidates typically drop out. It found that, on average:
22% of candidates drop out at the application stage
24% drop out at the screening stage
25% at the interview stage
So you might be losing anywhere from 20 to 40% of your talent pool while you spend time vetting resumés and sifting through cover letters.
Aside from the fact that this is a massive time-waster and a prime source of frustration for hiring managers, enforcing the use of resumés is not an effective way to ensure quality of hire.
That’s for two reasons:
78% of people “do people lie on their resume”, as we mentioned above.
Even if they don’t, average humans cannot look at resumés and determine exactly how good a candidate will be at the job for which they’re applying. According to research by Frank Schmidt and John Hunter, past experience accounts for just 3% of on-the-job performance. Put simply, what has happened cannot predict what will happen.
Therefore, our over-reliance on resumés creates problems when we go to interview candidates. It’s a classic problem: Overworked hiring managers formulate questions on-the-fly after making cursory glances at candidate submissions.
It’s little wonder that 25% of candidates bail at this point – often, they’re just reconfirming information they’ve already told you about who they are and what they’ve done.
There is an alternative: Structured interviews. Schmidt and Hunter found that structured interviews are the best predictor (26%) of on-the-job success.
The biggest companies are starting to focus more on this.
According to the Wall Street Journal, employers like Google, Delta, and IBM are combatting the tight labor market by easing strict needs for college degrees, focusing instead on interview and assessment processes that accurately measure soft skills and behavioral traits.
can you lie on linkedin ?
In the digital arena of professional profiles, LinkedIn emerges as the modern-day résumé, a curated collection of experiences and skills for the world to see. The veracity of these profiles, however, often comes into question. While Checkster’s 2020 survey unveiled that 78% of job applicants might lie on their résumés, the trend translates seamlessly to LinkedIn, where the stakes are just as high and the scrutiny potentially even more public.
LendEDU’s poll sheds light on this digital deception, indicating that a third of LinkedIn profiles are peppered with half-truths, especially within the ‘Skills’ section. In the rapid-fire realm of talent acquisition, where hiring managers and specialists are inundated with profiles, the lure to lie on LinkedIn about experience is magnified under the pressure of competition.
In the efficiency-driven process of recruitment, LinkedIn profiles serve as a quick filter, a snapshot of potential. Yet, this convenience comes at a cost. With half of the companies reporting a loss of quality talent due to flawed interview and hiring practices, as highlighted by Aptitude Research in 2022, the reliance on LinkedIn for pre-interview assessments is fraught with risk. The platform’s ease of access to a candidate’s professional narrative, while beneficial, also opens the door for inflated qualifications to slip through, unchecked.
The résumé, whether digital or document, sits at the crux of an efficacy dilemma. The same research posits that past experiences, as listed on LinkedIn, account for a mere fraction of actual job performance. Thus, the embellishments on LinkedIn, while aiming to secure an interview, may ultimately undermine a candidate’s prospects, as hiring managers seek substantive evidence of skills and capabilities.
In conclusion, as the job market evolves and companies like Google and IBM pivot towards skill and behavioral assessment over formal qualifications, the integrity of one’s LinkedIn profile becomes crucial. It’s a clarion call for honesty, as the professional world increasingly values authenticity and the accurate portrayal of one’s abilities and experiences.
Getting started with structured interviews
In its simplest form, the structured interview is based around a predefined set of questions.
These questions are typically behavioural and situational in nature: It’s about giving candidates the opportunity to explore how they think, solve problems, formulate plans, and deal with success and failure.
Therefore, questions like ‘Tell me how you’d respond if [specific situation] occurred’ don’t belong in a structured interview.
Instead, you might ask, ‘Tell me about when something went wrong with work, and you had to fix it. How did you go about it?’
Importantly, the questions you ask must be the same for all candidates. A critical component of the structured interview is fair and balanced comparison of candidates.
If you ask each candidate something different – as so often happens in a fast-paced hourly hiring setup – you can never accurately compare one candidate against another.
In that uncertainty, bias creeps in. It becomes a case of ‘I like this guy, he leans forward when he speaks.’
We’ve developed a handy tool to help you get started with structured interviews today: Our HEXACO job interview rubric. It comes with step-by-step instructions to help you figure out what skills and traits you need based on your open roles and company values.
From there, we’ve supplied you with more than 20 science-backed questions and a scorecard. It’s something simple enough for a busy hiring manager to use.
Remove the resumé entirely, and succeed
There is a possible world in which the resumé serves hiring managers as a kind of back-up validation document, used purely to verify the veracity of a candidate’s skills and experience.
In this world, the first stage of your recruitment funnel is the actual candidate interview.
That’s what our Ai Smart Interviewer can do. It’s a conversational Ai that takes candidates through a chat-based interview, using questions tailored to your open roles.
Candidates give their responses – with plenty of time to think – and Smart Interviewer analyses their word choices and sentence structures using its machine learning brainpower.
A candidate may be able to lie about their years of experience, or their knowledge of CSS, but our Smart Interviewer can accurately determine their cognitive ability, language proficiency, and personality traits.
Then it can make recommendations to you on the best candidates, according to the criteria you’ve set – and, at this point, you haven’t even looked at a single resumé.
But, as with traditional processes, you have the final say in who you hire.
In 2024
, the name of the game is efficiency. Success will be measured in time saved NOT having to screen, review resumes and cover letters, compile candidate feedback, communicate with candidates, or improve hiring manager interview techniques.
When you’re saving that much time and money, your recruitment (or HR) function has more bandwidth to focus on long-term talent acquisition and people initiatives.
Don’t struggle in 2024 – speak to our team today about how we can solve your hiring challenges.
Related
Blog
New Research Proves the Value of AI Hiring
A new study has just confirmed what many in HR have long suspected: traditional psychometric tests are no longer the gold standard for hiring.
Published in Frontiers in Psychology, the research compared AI-powered, chat-based interviews to traditional assessments, finding that structured, conversational AI interviews significantly reduce social desirability bias, deliver a better candidate experience, and offer a fairer path to talent discovery.
We’ve always believed hiring should be about understanding people and their potential, rather than reducing them to static scores. This latest research validates that approach, signalling to employers what modern, fair and inclusive hiring should look like.
The problem with traditional psychometric tests
While used for many decades in the absence of a more candidate-first approach, psychometric testing has some fatal flaws.
For starters, these tests rely heavily on self-reporting. Candidates are expected to assess their own traits. Could you truly and honestly rate how conscientious you are, how well you manage stress, or how likely you are to follow rules? Human beings are nuanced, and in high-stakes situations like job applications, most people are answering to impress, which can lead to less-than-honest self-evaluations.
This is known as social desirability bias: a tendency to respond in ways that are perceived as more favourable or acceptable, even if they don’t reflect reality. In other words, traditional assessments often capture a version of the candidate that’s curated for the test, not the person who will show up to work.
Worse still, these assessments can feel cold, transactional, even intimidating. They do little to surface communication skills, adaptability, or real-world problem solving, the things that make someone great at a job. And for many candidates, especially those from underrepresented backgrounds, the format itself can feel exclusionary.
The Rise of Chat-Based Interviews
Enter conversational AI.
Organisations have been using chat-based interviews to assess talent since before 2018, and they offer a distinctly different approach.
Rather than asking candidates to rate themselves on abstract traits, they invite them into a structured, open-ended conversation. This creates space for candidates to share stories, explain their thinking, and demonstrate how they communicate and solve problems.
The format reduces stress and pressure because it feels more like messaging than testing. Candidates can be more authentic, and their responses have been proven to reveal personality traits, values, and competencies in a context that mirrors honest workplace communication.
Importantly, every candidate receives the same questions, evaluated against the same objective, explainable framework. These interviews are structured by design, evaluated by AI models like Sapia.ai’s InterviewBERT, and built on deep language analysis. That means better data, richer insights, and a process that works at scale without compromising fairness.
Key Findings from the Latest Research
The new study, published in Frontiers in Psychology, put AI-powered, chat-based interviews head-to-head with traditional psychometric assessments, and the results were striking.
One of the most significant takeaways was that candidates are less likely to “fake good” in chat interviews. The study found that AI-led conversations reduce social desirability bias, giving a more honest, unfiltered view of how people think and express themselves. That’s because, unlike multiple-choice questionnaires, chat-based assessments don’t offer obvious “right” answers – it’s on the candidate to express themselves authentically and not guess teh answer they think they would be rewarded for.
The research also confirmed what our candidate feedback has shown for years: people actually enjoy this kind of assessment. Participants rated the chat interviews as more engaging, less stressful, and more respectful of their individuality. In a hiring landscape where candidate experience is make-or-break, this matters.
And while traditional psychometric tests still show higher predictive validity in isolated lab conditions, the researchers were clear: real-world hiring decisions can’t be reduced to prediction alone. Fairness, transparency, and experience matter just as much, often more, when building trust and attracting top talent.
Sapia.ai was spotlighted in the study as a leader in this space, with our InterviewBERT model recognised for its ability to interpret candidate responses in a way that’s explainable, responsible, and grounded in science.
Why Trust and Candidate Agency Win
Today, hiring has to be about earning trust and empowering candidates to show up as their full selves, and having a voice in the process.
Traditional assessments often strip candidates of agency. They’re asked to conform, perform, and second-guess what the “right” answer might be. Chat-based interviews flip that dynamic. By inviting candidates into an open conversation, they offer something rare in hiring: autonomy. Candidates can tell their story, explain their thinking, and share how they approach real-world challenges, all in their own words.
This signals respect from the employer. It says: We trust you to show us who you are.
Hiring should be a two-way street – a long-held belief we’ve had, now backed by peer-reviewed science. The new research confirms that AI-led interviews can reduce bias, enhance fairness, and give candidates control over how they’re seen and evaluated.
Blog
AI Maturity in the Enterprise
Barb Hyman, CEO & Founder, Sapia.ai
It’s time for a new way to map progress in AI adoption, and pilots are not it.
Over the past year, I’ve been lucky enough to see inside dozens of enterprise AI programs. As a CEO, founder, and recently, judge in the inaugural Australian Financial Review AI Awards.
And here’s what struck me:
Despite the hype, we still don’t have a shared language for AI maturity in business.
Some companies are racing ahead. Others are still building slide decks. But the real issue is that even the orgs that are “doing AI” often don’t know what good looks like.
You don’t need more pilots. You need a maturity model.
The most successful AI adoption strategy does not have you buying the hottest Gen AI tool or spinning up a chatbot to solve one use case. What it should do is build organisational capability in AI ethics, AI governance, data, design, and most of all, leadership.
It’s time we introduced a real AI Maturity Model. Not a checklist. A considered progression model. Something that recognises where your organisation is today and what needs to evolve next, safely, responsibly, and strategically.
Here’s an early sketch based on what I’ve seen:
The 5 Stages of AI Maturity (for real enterprises)
- Curious
- Awareness is growing across leadership
- Experimentation led by innovation teams
- Risk is unclear, appetite is cautious
- AI is seen as “tech”
- Reactive
- Gen AI introduced via vendors or tools (e.g., copilots, agents)
- Some pilots show promise, but with limited scale or guardrails
- Data privacy and sovereignty questions begin to surface
- Risk is siloed in legal/IT
- Capable
- Clear policies on privacy, bias, and governance
- Dedicated AI leads or councils exist
- Internal use cases scale (e.g., summarisation, scoring, chat)
- LLMs integrated with guardrails, safety reviewed
- Strategic
- AI embedded in workflows, not layered on
- LLM/data infrastructure is regionally compliant
- AI outcomes measured (accuracy, equity, productivity)
- Teams restructured around AI capability — not just tech enablement
- AI-Native
- AI informs and transforms core decisions (hiring, pricing, customer service)
- Enterprise builds proprietary intelligence
- FAIR™/RAI principles deeply operationalised
- Talent, systems, and leadership are aligned around an intelligent operating model
Why this matters for enterprise leaders
AI is a capability.And like any capability, it needs time, structure, investment, and a map.
If you’re an HR leader, CIO, or enterprise buyer, and you’re trying to separate the real from the theatre, maturity thinking is your edge.
Let’s stop asking, “Who’s using AI?”
And start asking: “How mature is our AI practice and what’s the next step?”
I’m working on a more complete model now, based on what I’ve seen in Australia, the UK, and across our customer base. If you’re thinking about this too, I’d love to hear from you.
Blog
Beyond the Black Box: Why Transparency in AI Hiring Matters More Than Ever
For too long, AI in hiring has been a black box. It promises speed, fairness, and efficiency, but rarely shows its work.
That era is ending.
“AI hiring should never feel like a mystery. Transparency builds trust, and trust drives adoption.”
At Sapia.ai, we’ve always worked to provide transparency to our customers. Whether with explainable scores, understandable AI models, or by sharing ROI data regularly, it’s a founding principle on which we build all of our products.
Now, with Discover Insights, transparency is embedded into our user experience. And it’s giving TA leaders the clarity to lead with confidence.
Transparency Is the New Talent Advantage
Candidates expect fairness. Executives demand ROI. Boards want compliance. Transparency delivers all three.
Even visionary Talent Leaders can find it difficult to move beyond managing processes to driving strategy without the right data. Discover Insights changes that.
“When talent leaders can see what’s working (and why) they can stop defending their strategy and start owning it.”
Metrics That Make Transparency Real (and Actionable)
🕒 Time to Hire
What it is: The median time between application and hire.
Why it matters: This is your speedometer. A sharp view of how long hiring takes and how that varies by cohort, role, or team helps you identify delays and prove efficiency gains to leadership.
Faster time to hire = faster access to revenue-driving talent.
💬 Candidate Sentiment, Advocacy & Verbatim Feedback
What it is: Satisfaction scores, brand advocacy measures, and unfiltered candidate comments.
Why it matters: Many platforms track satisfaction. Sapia.ai’s Discover Insights takes it further, measuring whether that satisfaction translates into employer and consumer brand advocacy.
And with verbatim feedback collected at scale, talent leaders don’t have to guess how candidates feel. They can read it, learn from it, and take action.
You don’t just measure experience. You understand it in the candidates’ own words.
🔍 Drop-Off Rates, Funnel Visibility & Automation That Works
What it is: The percentage of candidates who exit the hiring process at different stages, and how to spot why.
Why it matters: Understanding drop-off points lets teams fix friction quickly. Embedding automation early in the funnel reduces recruiter workload and elevates top candidates, getting them talking to your hiring teams faster.
Assessment completion benchmarks in volume hiring range between 60–80%, but with a mobile-first, chat-based format like Sapia.ai’s, clients often exceed that.
Optimising your funnel isn’t about doing more. It’s about doing smarter, with less effort and better outcomes.
📈 Hiring Yield (Hired / Applied)
What it is: The percentage of completed applications that result in a hire.
Why it matters: This is your funnel efficiency score. A high yield means your sourcing, screening, and selection are aligned. A low one? There’s leakage, misfit, or missed opportunity.
Hiring yield signals funnel health, recruiter performance, and candidate-process fit.
🧠 AI Effectiveness: Score Distribution & Answer Originality
What it is: Insights into how candidate scores are distributed, and whether responses appear copied or AI-generated.
Why it matters: In high-volume hiring, a normal distribution of scores suggests your assessment is calibrated fairly. If it’s skewed too far left or right, it could be too hard or too easy, and that affects trust.
Add in answer originality, and you can track engagement integrity, protecting both your process and your brand.
From Metrics to Momentum
To effectively lead, you need more than simply tracking; you need insights enabling action.
When you can see how AI impacts every part of your hiring, from recruiter productivity to candidate sentiment to untapped talent, you lead with insight, not assumption. And that’s how TA earns a seat at the strategy table.