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.
“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.
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.
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;
(These indications are based on the data required if you were working with us at PredictiveHire)
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.
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;
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.
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
Despite all the rhetoric, it seems that the world is becoming worse at removing bias from our workplaces, leveling the playing field for all employees, and improving diversity, equity, and inclusion.
COVID was tough for everyone, but the one good moment that seemed to come out of it was how people galvanized around the Black Lives Matter movement. Companies dedicated large advertising budgets to sophisticated promotional campaigns to convince us that they supported the movement.
At work, people demanded better from the companies they worked for. They demanded real and measurable progress on matters like diversity and inclusion, not just better benefits.
Employees weren’t going to accept the hypocrisy of their employer, a consumer brand spending millions on advertising about how woke they are when nothing changed internally. Bias was just not something that people were prepared to accept. It seemed like progress was being made, at least in the workplace.
Fast forward to 2023, and things have gotten worse than they were before the movement. What happened to push us so far backward on all the progress we’d made? The answer is video interviewing, specifically when it comes to amplifying bias in recruitment.
Video interviewing took off as a solution to the challenges of remote recruiting. However, video is a flawed way of assessing potential candidates as a first gate. It invites judgment, adds stress to the candidate, puts added pressure around hair and makeup, and turns a simple interview into a small theater production. Additionally, simply automating interviews with video doesn’t create any efficiencies for hiring teams, who are still watching hours and hours of interviews.
Video also excludes people who are not comfortable on camera, such as introverts, people with autism, and people of color. These factors do not influence a person’s ability to do a job, but using video at the start of the interview process puts them at a disadvantage. We are excluding a significant percentage of people by using video as a first gate.
We analyzed feedback comments from more than 2.3 million candidates across 47 countries using smart chat invented by Sapia.ai to apply for a role, and the overwhelming theme is that “it’s not stressful.”
As an industry, we must put a stop to this. Already, there is growing cynicism when companies talk about “improving candidate experience” because we like to say we care about something that will win us good PR, but we do little to hold ourselves accountable. We care more about optics than results.
However, you cannot say you care about candidates or diversity and inclusion and only use video platforms to recruit people. Frustratingly, there is technology that solves for remote work, improves the candidate experience, and truly reduces bias, and that is text chat.
Some of the most sought-after companies, like Automattic (the makers of WordPress), have been using it for years.
Chat is how we truly communicate asynchronously. It needs no acting, and we all know how to chat. Empowered by the right AI, text chat can be human and real. It can listen to everyone, it is blind, reduces bias, evens the playing field by giving everyone a fair go, and gives them all personalized feedback at scale.
It can harness the true power of language to understand the candidate’s personality, language skills, critical thinking, and much more.
Video should only ever be used as a secondary interaction, for candidates who are already engaged in the process and have been shortlisted. In that case, it does give hiring teams a chance to meet candidates, and candidates are more likely to be comfortable with video as they know they’ve progressed, and they’ve had a chance to present themselves in a lower pressure format already.
Why are we settling for video as a first interaction, when we can actually do more than make empty marketing promises to candidates? Why choose a solution that erodes all the hard gains we’ve made in diversity and inclusion?
It used to be all about Mobile First, now it’s about AI-first. Google now calls itself an ‘AI first’ company.
“How do you decipher the truth from puffery? Are there any shortcuts to really understand where AI is best applied in your business?”
I’m not a data scientist, but I spend my days talking to users and buyers of AI technology who are befuddled and increasingly cynical about the hype. Here is my 3-step guide to cutting through the noise.
Most products are using standard statistical techniques like regression. Without any machine learning baked into the technology, they are just matching tools. There are efficiency gains, but no ‘smarts’ and no learning in technology.
For example, in recruitment a stock-standard AI product would merely find you people with the same profile as those you have already hired, matching applicant profiles to hired profiles. CV parsers do this kind of thing. Now, that can be helpful to you if all you want to do is a short-cut to the same profile fast, and it can definitely save your recruitment team time.
But unless you know that these characteristics also match performance, you will not make a difference to your organisational outcomes.
If your Sales Director tells you that every new hire over the last year is hitting or exceeding budget, then absolutely keep using that tool. If she tells you that a third or more are underperforming or leaving the business, your AI tool is merely amplifying that bias and doing quantifiable damage to your company’s bottom line.
If you want both efficiency and business bottom-line impact – your AI needs to have machine learning baked in.
Takeaway: If the organisation selling you an AI tool has no Data Scientists, there is no machine learning in the product.
If I imagine a Maslow’s hierarchy of AI it would look like the below:
First up you need to have the data.
About 50% of companies don’t pass this threshold, but assuming you have it the next step is understanding the data context: What is the business problem you are trying to solve?
Next is the housekeeping. This means consolidating, cleaning, categorising and cataloguing the data. And then finally the optimisation is at the top – this is where the magic happens. Optimisation is the last mile and is what gets you to the big savings, but you need everything underneath it in order first.
In recruitment a genuinely smart AI tool with machine learning baked-in works best in these conditions:
Takeaway: It’s critical to have a solid understanding of what you’re trying to fix, and the means to measure the changes you’re making. Ask yourself why you are considering AI if you can’t quantify the problem, to begin with.
AI is about optimisation. For credit card companies it’s detecting fraud quickly. For online retailers better product recommendations. In recruitment, it’s finding the best new hires in a massive group of rookie players.
In each case, you are optimising for efficiency and accuracy, as the cost of getting it wrong is huge.
It means trusting the technology to find the patterns. You have to suspend theory, and your assumptions, a lot. You feed in a large amount of relevant and unbiased data and the machine learns on its own, finding the patterns. It is looking for the ‘signal in the noise’. Humans are unpredictable and more often than not unreliable.
The current hiring processing by humans is extremely resource-consuming and the result is not always satisfying. Using AI will free up your time if you allow it to, improving efficiency or outcomes, often both. But AI built just off CV data only adds bias and we’ve all see how badly that ends.
Takeaway: Predicting human decision making is not easy and not quick. The only way to get to the ‘answer’ is to start now and expect this to be a journey.
If you liked this article, suggested reading:
A CV tells you nothing
To AI or not to AI
MELBOURNE, July 2020: Australian AI recruitment start-up Sapia, has published peer-reviewed research validating a new AI-based approach to talent assessment that determines personality and job suitability through text.
The research was published by IEEE. https://ieeexplore.ieee.org/document/9121971
Personality assessments have long been used to supplement CV data. It is widely accepted that one’s personality can be a predictor of job performance and suitability. Thus, Sapia uses structured text-based interviews, NLP, and machine learning to identify personality traits by analysing text answers to questions related to the job being applied for.
Every candidate gets a “chat based smart interview”. As no demographic data is gathered from other sources such as CVs, the process is blind to gender, race and characteristics that are not relevant in candidate selection. The research validates the accuracy of Sapia’s AI approach. Lastly, it also signals a huge improvement to personality tests, where the candidate experience is underwhelming.
Also Know, Personality AI refers to the use of artificial intelligence (AI) technologies to analyze and understand human personality traits, tendencies, and behavior patterns. This field of AI has gained significant attention in recent years, as businesses and organizations seek to better understand their customers, employees, and other stakeholders.
Barbara Hyman, Sapia (Formerly PredictiveHire) CEO says chat-based interviews address the three big failures of current assessments – ghosting, bias and trust.
“Recruiters are the ultimate ghosters,” Ms Hyman says. “With Sapia, the fact that every single candidate receives a personalised learning profile is gold for candidates and your employer brand. Using text to analyse fit that’s blind to gender, race, age and any personal factors is a must-have in today’s current climate and means every company can introduce bias interruption for every hire and promotion. Imagine what that will do to diversity in hiring”
Principal Data Scientist Buddhi Jayatilleke says “language has long been seen as a source of truth for personality- it defines who we are. This technology offers a direct way to understand personality from language. All is done by using an experience that is human and empowering. Additionally, this capability can be used for assessment and personalised career coaching. Furthermore, it could be a game changer for job seekers, universities, and employers.”
Candidates across 34 countries have experienced Sapia’s unique chat-based interviews. More insight into how the technology works can be found here. https://sapia.ai/science-explained/
Sapia (Formerly PredictiveHire) is a team of data scientists, engineers and HR professionals. Together we have built a product suite that is based on science and built to humanise hiring. Sapia believes that relying on data to drive your most important decisions. Who you hire/ promote, enhances trust and confidence that decisions are fair. We also serve customers in the UK, South Africa, India Australia, and New Zealand.
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Finally, you can try out Sapia’s Chat Interview right now, or leave us your details to get a personalised demo.