You know the common definition of insanity? The one where the same thing gets done over and over again, but the end result doesn’t change? It might not be a big deal when talking about your daily commute, but taking the same old approach to hire key personnel could be an expensive mistake.
Industry studies estimate bad hires cost up to 2.5 times the dollar amount of that person’s salary – and the damage doesn’t end there. Mismatched employees disrupt workplace chemistry, productivity, and profitability.
In response to poor hiring decisions, a growing number of companies now employ predictive screening and hiring models. Engaging predictive analytics and artificial intelligence (AI) – or algorithms that ‘think’ like humans – to help with the legwork historically performed by recruiters.
AI and predictive analytics look at historical data and then apply the learnings to new data to predict future outcomes. So, predictive hiring models can predict who will make it through the interview process, outperform their peers and still be around a few years down the road.
Progressive HR professionals have realised the potential of predictive and data-driven hiring, and hiring managers seem to agree.
“Today, HR has a seat at the table, and in order to maintain that business partnership, you need to have an analytics framework.”
Andy Kaslow, CHRO, Cerberus
A 2016 survey revealed a strong desire to drive talent acquisition through data and analytics. Two hundred executives at large U.S. firms want technology to play a bigger part in the hiring process. And the clamour for analytics isn’t confined to a younger crowd. Two-thirds of decision-makers who desire data-driven solutions fell between the ages of 45-64.
So, why isn’t everyone doing it?
Although there is a general consensus that data-driven and predictive hiring will make hiring decisions more accurate, many HR professionals still view it as cumbersome and costly to implement.
And it can be true.
Understanding the data needed to make an impact, and figuring out the best techniques and algorithms to use is difficult.
And it can be expensive to hire data scientists, and other key technical personnel needed to implement a full scale HR analytics system.
But, there’s no need to go it alone or to do it all at once.
Rather than setting up in-house HR analytics teams, most companies opt to engage a vendor who specialises in custom predictive screening and hiring models. Finding a vendor that works with you to solve your hiring challenges will significantly cut cost and time to implement.
So, if you are considering implementing predictive hiring, we have put together a few tips to ensure you get off to a great start.
1. Define what you want to improve
The crucial first step of any successful project is to define what that success looks like. And implementing predictive hiring isn’t any different.
Have a think about the biggest issue your organisation is facing at the moment that better hiring decisions will solve.
For example, you might have the issue that a lot of new hires are leaving your organisation after a few months. Or you might have a company culture in need of strengthening, and need to hire people who fit with your ideal culture.
When you have honed in on the issue you want to solve, you also need to start thinking about the data that will be required to solve your challenge.
To give you an indication of the type of data you might need, consider these examples;
- If you have an issue with turnover, the data needed would be employees’ start and end date.
- Or, if you were looking to improve your customer service level at a call centre, you would need some sort of customer service KPI data – for example NPS.
(These indications are based on the data required if you were working with us at PredictiveHire)
2. Find a shortlist of vendors
After defining the issue you want to address with predictive hiring, it is time to find a shortlist of vendors that can help you achieve your goal.
Make sure you look for vendors who are able to build predictive hiring models focused on your specific issues, whilst making sure the candidate experience isn’t compromised.
3. Perform your due diligence
When you have your shortlist of vendors narrowed down, make sure you perform your due diligence. Some vendors will be a better fit for the challenge you wish to solve with your predictive hiring model.
Make sure your shortlisted vendors address these key questions;
- What are they basing their predictions on? Predictive hiring experts should be able to tell you the scientific basis of their predictions.
- How do they address the potential of existing biases being incorporated into the algorithms?
- How do they fit in with your current hiring process? Can they fit in without causing too much disruption/change?
- What is the user experience of their product like? How about the candidate experience? For a predictive hiring solution to be useful it should be easier to use than not to use.
- How do they support you during implementation, on-boarding and roll-out of the tool?
- What is required from you in order to maximise the project outcome?
Ai for Hiring – Buyers Guide: The 8 Questions You Must Ask
All of these questions are important to address to ensure the project’s success.
Implementing new software and processes will always require some level of change management, for example; following the ADKAR or Kotter change management approaches. Make sure you are comfortable with the level of support the vendor will offer you during the roll-out.
Following these three steps will ensure you are off to a good start with your predictive hiring project – and can start reaping the rewards quickly.
Predictive HR analytics is here to stay.
Resisting this change may put your company at a distinct disadvantage in the marketplace.
A recent MGI study found that AI can significantly improve the bottom line for businesses willing to incorporate them into their core functions. And the time really is now. Early adopters will enjoy a significant data-advantage in only a few years.
“[Leading businesses] use multiple AI technologies across multiple functions. As these firms expand AI adoption and acquire more data, laggards will find it harder to catch up.”
McKinsey Global Institute, June 2017
In the words of Gartner Research’s senior vice president Peter Sondergaard, “Information is the oil of the 21st century, and analytics is the combustion engine.”
You can try out Sapia’s Chat Interview right now, or leave us your details to book a demo
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Joe & the Juice Partners with Sapia.ai, Scaling an Exceptional Candidate Experience and Cutting Time to Hire
Read the full press release about the partnership here.
Joe & the Juice, the trailblazing global juice bar and coffee concept, is renowned for its vibrant culture and commitment to cultivating talent. With humble roots from one store in Copenhagen, now with a presence in 17 markets, Joe & The Juice has built a culture that fosters growth and celebrates individuality.
But, as their footprint expands, so does the challenge of finding and hiring the right talent to embody their unique culture. With over 300,000 applications annually, the traditional hiring process using CVs was falling short – leaving candidates waiting and creating inefficiencies for the recruitment team. To address this, Joe & The Juice turned to Sapia.ai, a pioneer in ethical AI hiring solutions.
A Fresh Approach to Hiring
Through this partnership, Joe & The Juice has transformed its hiring process into an inclusive, efficient, and brand-aligned experience. Instead of faceless CVs, candidates now engage in an innovative chat-based interview that reflects the brand’s energy and ethos. Available in multiple languages, the AI-driven interview screens for alignment with the “Juicer DNA” and the brand’s core values, ensuring that every candidate feels seen and valued.
Candidates receive an engaging and fair interview experience as well as personality insights and coaching tips as part of their journey. In fact, 93% of candidates have found these insights useful, helping to deliver a world-class experience to candidates who are also potential guests of the brand.
“Every candidate interaction reflects our brand,” Sebastian Jeppesen, Global Head of Recruitment, shared. “Sapia.ai makes our recruitment process fair, enriching, and culture-driven.”
Results That Matter
For Joe & The Juice, the collaboration has yielded impressive results:
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33% Reduction in Screening Time: Pre-vetted shortlists from Sapia.ai’s platform ensure that recruiters can focus on top candidates, getting them behind the bar faster.
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Improved Candidate Satisfaction: With a 9/10 satisfaction score from over 55,000 interviews, candidates appreciate the fairness and transparency of the process.
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Bias-Free Hiring: By eliminating CVs and integrating blind AI that prioritizes fairness, Joe & The Juice ensures their hiring reflects the diverse communities they serve.
Frederik Rosenstand, Group Director of People & Development at Joe & The Juice, highlighted the transformative impact: “Our juicers are our future leaders, so using ethical AI to find the people who belong at Joe is critical to our long-term success. And now we do that with a fair, unbiased experience that aligns directly with our brand.”
Trailblazing for the hospitality industry
In an industry so wholly centred on people, Joe & the Juice is paving the way for similar brands to adopt technology that enables inclusive, human-first experiences that can reflect a brand’s core values.
If you’re curious about how Sapia.ai can transform your hiring process, check out our full case study on Joe & The Juice here.
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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.
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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.