It’s an understatement to say that recruiters and talent acquisition managers have had it tough over the last four-odd years. The pressures have compounded like a line of falling dominoes: First it was the COVID-19 pandemic; then came the mass talent migration; then the advent of new concepts like ‘quiet quitting’ and ‘acting your wage’, which, like them or not, seem to be the manifestations of a tired and existentially anxious workforce.
Now, in 2024, it’s likely that we’ll have to contend with a global recession.
Hiring is tougher. Candidates are wary and they expect more. Duh.
So do companies and their CEOs. However – and somewhat counter-productively – many companies have sought to cut recruitment budgets, lay off recruiters and talent acquisition managers en masse, and deprioritize long-term recruitment marketing strategies. We’re facing troubled times, and recruitment (and perhaps HR, more generally) is being treated as a cost center.
This misunderstanding of HR as a money sink is nothing new. It happens during every trough in the market. But, if we don’t make efforts to change this perception, 2024 will be a particularly painful valley to climb out of.
Why is recruitment treated as a cost center?
CEOs have been keen on talent strategy for years, but are struggling to quantify the effects of recruitment and talent acquisition activities. They cannot see the A to B journey, the action and its result. When the market is good, talent is in abundance, and you’re hiring effectively, nobody cares. But when times are hard, nebulous processes are put under harsh light.
Relatedly, recruitment and talent acquisition leaders are struggling to prove that the outcomes of their work are driving revenue. This is primarily an issue of data capture and analysis, in our experience: When companies come to us to help with hiring quality talent, the number one issue they have is to do with metrics and KPIs. Most do not know how to reliably measure quality of hire, nor time-to-hire, nor the effectiveness of their recruitment marketing channels. Many know that their processes are plagued with inefficiencies, but are not sure how to go about fixing them.
(To be clear, totally understandable. This stuff is hard.)
The big and unmanageable HR tech stack
Where recruitment is concerned, a HR tech stack tends to look like this: an unwieldy ATS, often coupled with a conversational AI or scheduling tool.
These technologies cost big money. As a result, the question CFOs and CEOs will be constantly asking of HR is this: Is it adding real value? Can you prove it? Or are we simply stuck to a system that tackles old problems with insufficient solutions?
The bottom line is this: Enterprise companies are overstacked, overworked, and need to adopt different solutions to old problems. It doesn’t mean less tech, necessarily, although it can; it means the right tech.
Focus first on areas of lost productivity.
Easier, perhaps, than it sounds. It’s always better to iterate than to completely restructure your hiring function. So get your team together and examine your processes. How much time is spent:
- Sourcing and reviewing candidates (including, importantly, reading through resumes and cover letters)?
- Screening?
- Scheduling, rescheduling, and conducting interviews?
- Organizing and corralling hiring managers?
- Gathering, collating, and providing feedback to candidates?
- Communicating with candidates, more generally?
- Onboarding?
Ideally, you have baseline data in your ATS to help you arrive at some indicative numbers. But let’s assume that you don’t: calculating rough person-hours is enough to see where time may be spent more effectively.
In our experience, sourcing and screening are the stages in which quick wins might be gotten. As time-honored research (and our Smart Interviewer product) shows, resumes and cover letters are not useful indicators of candidate quality or potential. They can be easily falsified. What’s more, Sapia and Aptitude research from 2022 discovered that 22% of candidates drop out at the application stage and 24% at the screening stage.
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.
Interviews are another huge cause of inefficiency. Structured interviews are the best explainer (at 26%) of an employee’s performance, but many companies allow recruiters and hiring managers to conduct interviews haphazardly, causing a misidentification and loss of talent that can be hard (if not impossible) to measure. If you’re interviewing badly, how can you know if you’re capable of finding good candidates? What’s the associated cost of such a problem?
It’s no surprise, then, that according to our research, 50% of companies say they’ve lost talent due to the way they interview. Big costs involved there.
Next, focus on measurable metrics
Don’t worry: We’re not going to lay out a massive and exhaustive list of metrics you should be tracking. Not feasible; you’re overworked as it is.
Instead, we’ll prescribe three good places to start, including links to helpful blog posts explaining how you measure them effectively:
- Candidate abandonment rate
- Candidate source attribution (or where candidates actually come from)
- Candidate experience baseline
Each of these metrics can help you improve efficiencies, and in turn, start to prove that your recruitment function is having a positive effect on business outcomes.
Layer on tech that can help you drastically improve hiring efficiency, while giving you time to focus on big picture stuff
At a certain point, we must realize that force-multiplying technology is the only way to win in the unfolding ‘now’ of work. We’re spending way too much time with processes that can be repeated and automated – often out of some sense of duty to uphold 1:1 human connection (as if technology completely removes that, which it doesn’t).
And, because we do this, we weaken our position at an executive level: CEOs care about what is scalable, and the average recruitment function, traditionally speaking, does not.
In a recent episode of our Pink Squirrels! podcast, Sapia CEO and founder Barb Hyman had a chat with expert HR change management leader, Kyle Lagunas, about this very topic.
The one foolproof way to elevate recruitment in your company
We exist to help you hire better, faster, and with fewer headaches. Our Smart Interviewer takes care of the scheduling, interviewing, and assessment stages of your process – saving upwards of 2,000 recruitment hours (av.) per month, and enabling you to offer jobs to candidates within 24 hours of application.
It’s delivered in a chat-based format (hello, Gen Z!), and candidate responses are assessed according to science-backed personality models. This means you can be sure you’re getting top talent, and you can prove it with measurable, repeatable data.
That’s not all: Our tech is blind, which means it natively disrupts bias and maximizes the size of your talent pool. Everyone gets an interview, and everyone gets personalized coaching tips whether or not they get the job. Our application completion rate, for all customers, sits at around 85% on average; our candidate satisfaction rate is well over 90%!
(And, if you need a second stage interview, you can use our Video Interview tool.)
Everything you do with our platform is pulled through to comprehensive data dashboards, allowing you to see hiring efficiency, quality, time, diversity, and other metrics. CEOs love this kind of transparency.
There you go: 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.
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Sapia.ai Wrapped 2024
It’s been a year of Big Moves at Sapia.ai. From welcoming groundbreaking brands to achieving incredible milestones in our product innovation and scale, we’re pushing the boundaries of what’s possible in hiring.
And we’re just getting started 🚀
Take a look at the highlights of 2024
All-in-one hiring platform
This year, with the addition of Live Interview, we’re proud to say our platform now covers screening, assessing and scheduling.
It’s an all-in-one volume hiring platform that enables our customers to deliver a world-leading experience from application through to offer.
Supercharging hiring efficiency
Every 15 seconds, a candidate is interviewed with Sapia.ai.
This year, we’ve saved hiring managers and recruiters hours of precious time that can now be used for higher-value tasks.
Giving candidates the best experience
Our platform allows candidates to be their best selves, so our customers can find the people that truly belong with them. They’re proud to use a technology that’s changing hiring, for good.
Leading the way in AI for hiring
We’ve continued to push the boundaries in leveraging ethical AI for hiring, with new products on the way for Coaching, Internal Mobility & Interview Builders.
Blog
Situational Judgement Tests vs. AI Chat Interviews: A Modern Perspective on Candidate Assessment
Choosing the right tool for assessing candidates can be challenging. For years, situational judgement tests (SJTs) have been a common choice for evaluating behaviour and decision-making skills. However, they come with limitations that can make the hiring process less effective and less inclusive.
AI-enabled chat-based interviews, such as Sapia.ai, provide organisations with a modern alternative. They focus on understanding candidates as individuals and creating a hiring experience that is both fair and insightful while enabling efficient screening and selection.
This shift raises important questions: Are SJTs still a tool that should be considered for volume hiring? And what do AI assessments offer in comparison?
1. The Static Nature of SJTs
Traditional SJTs use predefined multiple-choice questions to assess behavioural tendencies and situational knowledge. While useful for screening, these static frameworks lack the flexibility to adapt based on real-world performance data or evolving role requirements.
Once created, SJTs don’t adapt to new data or evolving organisational needs. They rely on fixed scenarios and responses that may not fully reflect the dynamic realities of modern workplaces, and as a result, their relevance may diminish over time.
AI-enabled chat interviews, on the other hand, are inherently adaptive. Using machine learning, these tools can continuously refine their models based on feedback from real-world outcomes such as hiring or turnover data. This ability to evolve ensures the assessments align with organisations’ needs.
2. Richer Data Through Open-Ended Responses
One of the main critiques of SJTs is their reliance on multiple-choice responses. While structured and straightforward, these options may not capture the full scope of a candidate’s thinking, communication skills, or problem-solving ability. The approach is often limiting, reducing complex human behaviour to a few predefined choices.
AI-enabled chat interviews work more holistically and dynamically. These tools provide a more complete picture of a person by allowing candidates to answer questions in their own words. Natural language processing (NLP) analyses their responses, offering insights into personality traits, communication skills, and behavioural tendencies. This open-ended format lets candidates express themselves authentically, giving employers a deeper understanding of their potential.
3. The Candidate Experience: Stressful or Supportive?
SJTs often include time constraints and rigid formats, which can create pressure for candidates. This is especially true when candidates feel forced to choose options that don’t fully reflect how they would actually behave. The process can feel impersonal, even transactional.
In contrast, chat-based interviews are designed to be conversational and low-pressure for candidates. By removing time limits and adopting a familiar chat interface, these tools help candidates feel more at ease. They also frequently include personalised feedback, turning the assessment into a valuable experience for the candidate, not just the employer.
4. Addressing Bias and Fairness
Traditional SJTs are prone to transparency issues, as candidates can often identify and select the “best practice” answers without revealing their true tendencies. Additionally, static test designs can unintentionally embed bias; due to the nature of the timed test, SJTs have been found to disadvantage some groups.
AI chat interviews, when developed ethically within a framework like Sapia.ai’s FAIR Hiring Framework, eliminate explicit bias by relying solely on the content of a candidate’s responses. Their machine learning models are continuously validated for fairness, ensuring that hiring decisions are free from subjective judgments or irrelevant demographic factors.
5. An Assessment That Improves Over Time
Workplaces are constantly changing, and hiring tools need to keep up. SJTs’ fixed nature can make them less effective as roles evolve or organizational priorities shift. They provide a snapshot but not a dynamic view of what’s needed.
AI-enabled chat interviews are built to adapt. With feedback loops and continuous learning, they incorporate real-world hiring outcomes—like retention and performance data—into their models. This ensures that assessments stay relevant and effective over time.
Rethinking Candidate Assessment
As hiring demands grow more complex, so does the need for tools that can capture the whole person, not just their response to hypothetical scenarios. While SJTs have played an important role in hiring practices, they are increasingly being replaced by tools like AI-enabled chat interviews.
These modern approaches provide richer data, adapt to changing needs, and create a richer and more engaging experience for candidates. Perhaps most importantly, they emphasise fairness and inclusivity, aligning with the growing demand for unbiased hiring practices.
For organisations evaluating their assessment tools, the question isn’t just which method is “better.” Understanding the specific needs of your roles, teams, and candidates will help you choose tools that help you make decisions that are both informed and equitable.
Blog
Keeping Interviews Real with Next-Gen AI Detection
It’s our firm belief that AI should empower, not overshadow, human potential. While AI tools like ChatGPT are brilliant at assisting us with day-to-day tasks and improving our work efficiency, employers are increasingly concerned that they’re holding candidates back from revealing their true, authentic selves in online interviews.
As an assessment technology provider, we are responsible for ensuring the authenticity and integrity of our platform. That’s why we’re thrilled to unveil the latest upgrade to our flagship Chat Interview: the AI-Generated Content Detector 2.0. With groundbreaking accuracy and a candidate-friendly design, this innovation reinforces our mission to build ethical AI for hiring that people love.
Artificially Generated Content (AGC) is content created by an AI tool, such as ChatGPT, Claude, or Pi. We initially rolled out the first version of our AGC detector last year and have continued to improve it as our data set has grown and these AI tools have evolved.
What’s New?
Our updated AGC Detector 2.0 achieves an impressive 98% detection rate for AI-assisted responses, with a false positive rate of just 1%. This gives organisations peace of mind that they’re getting the most authentic assessment of every candidate.
This cutting-edge system builds on Sapia.ai’s proprietary dataset of over 2 billion words, derived from more than 20 million interview question-answer pairs spanning diverse roles, industries, and regions. It’s trained on real-world data collected before and after the release of tools like ChatGPT, ensuring it remains robust and reliable even as AI tools evolve.
The Challenge of AI in Chat-based Interviews
Our data shows that around 8% of candidates use tools like GPT-4 to generate responses for three or more interview questions. While these tools may offer a quick way for candidates to complete their interview, they can inadvertently hide a person’s true personality and potential – qualities our customers are most interested in understanding through our platform. In fact, research from Sapia Labs shows that these tools have their own personality traits, which may be quite different from the candidate applying for the role.
For Candidates: Enabling Authenticity
When a response is flagged as potentially AI-generated, the system doesn’t disqualify candidates. Instead, a real-time warning pops up, allowing them to revise their answers or submit them as-is. This ensures that candidates are encouraged to present themselves authentically, reflecting their unique communication styles and sharing their genuine experiences.
For Hiring Teams: Actionable Insights
Responses flagged as AI-generated are highlighted in the candidate’s Talent Insights profile, accessible via Sapia.ai’s Talent Hub or ATS integrations. These insights give hiring teams the transparency to make informed decisions, fostering trust while accelerating hiring timelines.
Built on Unmatched AI Interview Expertise
“Our detection model’s strength lies in its foundation of real-world interview data collected from diverse roles and regions,” says Dr Buddhi Jayatilleke, Sapia.ai’s Chief Data Scientist. This depth of understanding enables the AGC Detector to maintain its industry-leading accuracy – even when candidates subtly modify AI-generated answers to appear more human.
Why This Matters
The AGC Detector 2.0 embodies Sapia.ai’s commitment to ethical AI that amplifies human potential. As our CEO Barb Hyman explains:
“The hiring landscape has fundamentally changed since ChatGPT, but our commitment remains clear: AI should amplify human potential, not penalise it. This breakthrough fosters authentic hiring conversations. Our real-time warning system helps candidates make better choices and gives enterprises confidence in their selection decisions.”
Testing and Validation of the AGC Detector 2.0
The new detector has been rigorously tested on over 25,000 interview responses generated by humans and leading AI models like GPT-4, Claude-3.5, and Llama-3. The results speak for themselves, reinforcing the reliability and fairness of this game-changing technology.
Fairness & Transparency in AI-Enabled Hiring
By detecting AI-generated content while allowing candidates to correct their responses, our AGC Detector 2.0 ensures every applicant has the chance to put their best, most authentic foot forward when applying for a role powered by Sapia.ai. For enterprises, it provides confidence in the integrity of their hiring decisions and ensures they’re connecting with real candidates at scale.