4 practical ways to solve your decentralized hiring challenges in 2023

How to improve decentralized hiring processes | Sapia Ai interview software

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 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.

Here’s a cool example of how we help our clients advertise their jobs in places their customers can easily see.

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.


Will COVID-19 be the bias interrupter we need so badly in hiring?  

The rise in video platforms for hiring suggests we still have as strong a ‘bias’ towards having to see someone to hire someone, as there has been with having to see someone working in the office to trust they are working. 

What will it take for that bias to be disrupted?

Mature organisations who have fully remote teams working in 75+ countries, hire remotely via text and/or email. No face-to-face and definitely no video interviewing, which can be a petri dish for bias.

It makes us wonder, how much do we really care about removing bias in our organisations? 

Many companies are hurting right now.  COVID-19 is forcing them to make lay-offs and tough decisions about the things that mattered to them. For some, Diversity and Inclusion initiatives have been the first to go.  Given the havoc that COVID-19 has created in our economy, this loss of focus is somewhat understandable.

Then George Floyd died after a police officer held him down so he was unable to breathe. In the week since we’ve seen unprecedented statements coming out from companies in support of the #blacklivesmatter movement. This signifies a huge shift in how companies engage with these issues, but when we’re fighting institutionalized racism, and corporate America is a very much part of the institution, it doesn’t matter how powerful your statement is – unless you’re unwilling to take action and to change internally. 

Bias in the recruiting process has been an issue for as long as modern-day hiring practices existed.

The idea of “blind applications” became a thing a few years ago, with companies removing names on applications thinking that it would remove any gender or racial profiling. It made a difference, but bias still existed though the schools that people attended, as well as the past experience they might have had. Interestingly, these are two things that have now been shown to have no impact on a person’s ability to do a job. 

Artificial Intelligence was touted as the end-solution, but early attempts still ran through CVs and amplified biases based on gender, ethnicity, age – even if they weren’t recorded, AI created profiles comparing ‘blind’ candidates to those in roles currently (ie. white men) – as well as favouring schools and experience.

Enter blind screening

True bias in recruiting can only exist if the application is truly blind (no demographics are recorded) and is not based on a CV, but through matching a person’s responses to specific questions to their ability to perform a job. It has to be text-based so that true anonymity can be achieved – something video can’t do as people are still racially profiled. 

To have a conversation about removing bias from your organisation – we would love to chat

Have you seen the 2020 Candidate Experience Playbook? Download it here.


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Does video hiring productise bias?

In recent years, we have all wisened up to the risk of using CVs to assess talent. A CV as a data source is well known to amplify the unconscious biases we have. A highly referenced study from 2003 called “Are Emily and Greg More Employable than Lakisha and Jamal?” found that white names receive 50 per cent more callbacks for interviews.

However, during COVID, we reverted to old ways in a different guise. 

HR substituted CV as a data input with video interviews. 

This isn’t a step forward.

Video hiring productises bias. It actually enables bias at scale.

It leads to mirror hiring – those who look and sound most like me. Instead of screening CVs in 30 seconds now, your team is watching 3-minute videos, so recruiting takes longer, and it’s exhausting.

Video platforms are being challenged in the US (EPIC Files Complaint with FTC about Employment Screening Firm HireVue) for concerns over invisible biases that may be affecting candidate fairness given the opaque nature of those algorithms. Facial recognition systems are worse at identifying the gender of women and people of colour than at classifying male, white faces. This year IBM openly pulled out of facial recognition, fearing racial profiling and discriminatory use, partly due to the questionable performance of the underlying AI.

How did we substitute one inferior and biased methodology with another that’s arguably even more biased? 

We get that at some point you and the candidate need to meet, although no rule says you need to see someone to hire them. That’s just a bias (much like the bias pre-Covid) that you need to see someone at work to know that they are doing the work. 

Blind hiring means you are interviewing a candidate without seeing them or knowing what school they went to, the jobs they have had. It’s a real meritocracy in that it’s fair for the candidate – and also smart for your organisation. 

If you are hanging your hat on the fact you just finished bias training- research has shown consistently unconscious bias training does not work.  

While we have all been dutifully attending it for years, the truth is the change factor is zero. 

At a recent event attended by academics and data-loving professionals –whilst there was a welcome recognition that humans are more biased than Ai, and despite hearing that Wikipedia lists more than 150 biases we humans have – the majority of the audience still believe the impossible: that we can be trained out of our unconscious biases. 

Algorithms are better at dealing with biases

The Nobel Prize winner Dr Daniel Kahneman prescribes an algorithmic approach as better at decision-making to remove unconscious biases. He claims “Algorithms are noise-free. People are not. When you put some data in front of an algorithm, you will always get the same response at the other end.”  Also, see why machines are a great assistive tool in making hiring a fair process, here

We know your inbox is flooded with Ai tools with each proclaiming to remove bias and give you amazing results and it’s tough to discriminate between what’s puffery, what’s real and what you can trust. 

 If your role requires you to know the difference between puffery and science, then read this. Buyers Guide: 8 Questions You Must Ask.

The 30-second due-diligence test that every HR professional should be asking when presented with one of these whizz-bang Ai tools is this:

  • No data scientists in the team = not likely to be based on Ai
  • No research available even under NDA to substantiate the method of assessment being used = pseudoscience or science that’s flawed if the company is not prepared to share it 
  • No regular bias testing to review = the Ai is likely to be biased in application 
  • Data used to training the models is 3rd party/ social media data = high risk of bias. 

 It’s critical, in fact, it’s a duty of care you have to your candidates and your organisation to be curious and investigate deeply the technology you are bringing into the organisation. 

We have to be careful not to think that all AI is biased. AI is based on data, and that data can be tested for bias. ‘Data-driven’ also means transparent. Testing for bias, fairness and explainability of AI models is an active area of research and has advanced a lot. If built with best practices, AI can be used to challenge human decisions and interrupt potential biases. In the end, hiring is a human activity, and the final outcome should always be owned by a human.    

If you want to know more about the research that defines the Sapia approach, look here

If you want to know more about our bias testing, look here

Have you seen the Inclusive e-Book?

Making inclusion an HR, not a PR priority.

It offers a pathway to fairer hiring in 2021. In this Inclusivity e-Book, you’ll learn: 

  • How to design an inclusive recruitment path. From discovery to offer and validation of the process.
  • The hidden inclusion challenges that are holding your organisation back.
  • How to tell if Ai technology is ethical.

Download Inclusivity Hiring e-Book Here >

Get diversity and inclusion right whilst hiring on time and on budget.

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Introducing InterviewBERT: A world-first algorithm for better interviews

Sapia labs, our R&D department, has developed a world-first innovation that will help us deepen our understanding of the contextual meaning of words in written job interviews. Called InterviewBERT, this algorithm combines Google’s model for Natural Language Processing (NLP) with our proprietary dataset of more than 330 million words. BERT, meet Smart Interviewer. Together, they’ll usher in a new generation of pre-employment assessment tools and recruitment software solutions.

Put simply, InterviewBERT makes Smart Interviewer, the most sophisticated conversational Ai in the world. Ours is no simple chat-bot – already, Smart Interviewer is capable of discovering personality traits and communication skills, accurately and reliably, using a candidate’s written responses. With InterviewBERT, Smart Interviewer will learn more about candidates than ever before, faster than ever before. With this speed and accuracy also comes reductions to the unfairnesses and biases that plague the hiring process.

Why, and how, are we the first to transform pre-employment assessment technology with BERT?

Through sound Ai infrastructure, we have been able to accumulate a vast and accurate dataset. This dataset grows by the minute – we interview a new candidate every 30 seconds – and, coupled with the expertise of our Sapia labs team, we can assess candidate suitability for a role in milliseconds.

“The smartest companies know that the fairest and most accurate way to assess someone’s suitability for a role is through a structured interview,” our CEO, Barb Hyman, said. “Text increases accuracy and speed of assessing candidates, while removing biases that come through voice or video interviews.

InterviewBERT and Google NLP | PredictiveHire recruitment software

Dr Buddhi Jayatilleke, Chief Data Scientist and head of Sapia labs, said the team is excited at the finding that InterviewBERT had such a profound impact on trait accuracy.

“Written language encodes personality signals predictive of ‘fit’,” Dr Jayatilleke said. “The ability to understand people through language has limitless applications, and we are excited to keep inventing more ways to use language data for our customers.”

Dr Jayatilleke said decades of research had confirmed that language has long been seen as a source of truth for personality. 

“What our R&D team has proven is just how powerful language data is when you combine it with enormous data volumes and scientific rigour,” he said. “This capability can be used for assessment and for offering personalised career coaching – a game changer for job seekers, universities, and employers.”

Sapia labs will present its findings from a new research paper, Identifying and Mitigating Gender Bias in Structured Interview Responses, at a SIOP symposium in April. 

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