Decentralized recruitment, while enabling larger companies to hire efficiently, suffers in a labor-short market.
Under ordinary circumstances – like, say, the world before COVID and the Great Resignation – it’s ideal to let local hiring managers build their own workforces. Generally speaking, the decentralized approach is better for productivity, candidate experience, and the overall satisfaction of hiring managers, who look favourably on the trust and autonomy they get from head office.
However, when good candidates are hard to come by, the dearth of talent puts stress on the joints of such a sprawling network. We hear this frequently from companies who come to us to help improve efficiency, diversity, and quality of hire.
Here are the common problems companies are having with decentralized hiring in 2022:
- Hiring managers are frustrated, because they have a trickle of applicants and little control over employer branding and recruitment marketing.
- Consistency is hampered by inconsistent processes and rogue hiring managers, who frequently abandon workflows and ATS protocols in order to acquire warm bodies by any means necessary.
- Job advertising budgets are distributed unevenly, resulting in consternation for already-strained teams.
- Diversity is put on the backburner, both because hiring managers have the final say, and because they have little-to-no accountability over decisions.
- The company’s recruitment centre (i.e. head office) is unable to collect and analyze sufficient data to diagnose and fix recruitment problems across its decentralized network.
- The company is using an ATS with which either some (or all) of hiring managers are unhappy. Head office may know this, but in any case, it decides that the process of researching, purchasing, and implementing a new ATS is not worth the pain.
- A staunch desire to stick to the status quo, or ‘the way we’ve always done things’, because the company assumes that this period of hiring difficulty will soon pass.
These challenges (and others) have effected a drop in confidence in the way companies interview and process candidates. An Aptitude Research and Sapia.ai report from earlier this year found that 33% of companies aren’t confident in the way they interview, and 50% have lost talent due to poor processes. Meanwhile, 22% of the average talent pool is drained at the application stage.
Statistically speaking, roughly one in five people, at minimum, are bailing out of your application process at the very beginning.
How to improve efficiencies across a strained decentralized hiring network
As with many things in business, the answer to alleviating organizational pain lies in small, iterative improvements. Our recommendations do not include haphazard technological upgrades, nor do we advocate for widespread process changes. These will more than likely cause your decentralized hiring network to fall apart.
Here are some good places to start.
Look at removing time-wasting entry barriers, like resumes and cover letters
This is particularly important for the retail and hospitality industries, but certainly applies to any companies that hire entry-level team members at volume. Given the average level of job experience at this level of employment, most resumes and cover letters aren’t useful in gauging candidate quality. On the contrary – they take up precious hiring manager hours, are cumbersome for candidates to write, and are the main cause of the 22-24% candidate drop out rate we mentioned above. That’s not even accounting for the fact that anywhere between 60-80% of resumes contain falsifications.
Implement a simple, standardized process for capturing a candidate experience NPS baseline
Decentralization, almost by definition, makes capturing useful information difficult. But if you use an ATS as a tool for centralization, consider adding a candidate NPS measurement step to your application process. It can be as simple as a Net Promoter Score scale (1 to 10). If you hire at volume across multiple localities or regions, asking this one simple question can help you produce meaningful insights about how candidates find your process. What gets measured, gets managed, and though there are many other data points you might want to collect, this is a good (and relatively easy) place to start. If you’re keen to learn more about this, check out our podcast episode on candidate experience with Lars van Wieren, CEO at Starred.
Speak to your hiring managers regularly
Quantitative data is gold, but qualitative data is platinum. Make a habit of interviewing (not surveying, interviewing) your hiring managers on the ground. You’ll uncover invaluable insights that may enable you to make fast changes at scale. We help our clients collect qualitative feedback from hiring managers as a matter of course, leading to increases in productivity and hiring manager satisfaction.
Here are some useful questions to ask your hiring managers:
- Take me through how you run your local (e.g. instore) hiring process, from start to finish.
- Explain your process for interviewing candidates.
- Where do you think you waste the most time?
- What doesn’t work as well as it should?
- What kinds of candidates are you seeing, and how would you rate the overall quality?
- How might we support you in hiring more effectively?
This kind of bottom-up research aims to understand how hiring managers are actually behaving and interacting with systems. Some may be breaking from established protocols, but if you ask them why and how, you might uncover tactics and efficiencies that can be brought back to the rest of the organization, thereby improving the way all hiring managers operate. Two adages apply here: ‘Necessity is the mother of invention’, and ‘People will always find the path of least resistance’.
This fact-finding method is better than surveys because surveys impose a limited scope in which potential problem areas are preset. “We’re asking you about these things,” you’re saying, “and therefore, we’re suggesting they’re most important.” As a result, other problems and possible solutions are likely to be excluded from discovery. You’ll always learn more by having real conversations, because they can go in any conceivable direction.
Look for novel ways to encourage applications from otherwise passive candidates
Again, incredibly useful for retail, but applicable in a wide range of industries and contexts. Think about the universal touchpoints you have with customers (a.k.a candidates) across your decentralized network. In retail, some good examples might be your receipts and carry bags. These provide you invaluable real estate to advertise your jobs and employer brand. Consider putting a URL or QR code on these assets, and you might drastically increase the amount of people who know about and apply for the jobs you advertise. This tactic has the added benefit of capitalizing on active and loyal customers; after all, if they’re buying from you, they’re a prime target for recruitment marketing.
The best part about this manner of advertising? You already own the space, and the design can be centralized and rolled out at scale.
We’d be remiss if we didn’t point out that Sapia’s Ai Smart Interviewer is a dynamite solution for the inevitable pain points of decentralised recruitment. Our technology can be rolled out across your entire company, and takes care of the application, screening, interviewing, and assessment stages of your process.
Hiring managers save time – as much as 1,600 hours per month, for some of our customers – but they still get the option to approve and interact with short-listed candidates. Better still, our platform captures vital data on diversity and candidate experience, enabling you to see exactly how your network is performing, individually and collectively.
Best of all, Sapia tech integrates directly with the leading ATS platforms, and can be rolled out in as little as four weeks.
Woolworths Group, Australia’s largest private employer, uses Sapia to hire more than 50,000 candidates per year, nationwide. To see how they flourish in a labor-short market, check out our case study 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.
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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.