When we think about the future of work, so much focus is on the individual — the employee — but the workplace is changing just as fast, and the way we think about traditional processes and tasks is rapidly evolving.
In nearly every aspect of a business, you analyze and iterate to make processes more efficient, to spend less money, to make more informed decisions, to have a greater impact on outcomes — and yet, so much of how we approach hiring and managing the people of our business is stuck in this outdated and qualitative place.
While eliminating 100% of the human element of recruiting is not likely (or desired), we can and should be working to innovate and implement tools and elements that can make recruiting more intelligent and data-driven.
What are the practical steps you can take to make your process more efficient? Why does it matter? And what can we expect down the road?
Benefits of an intelligent, data-driven recruiting process
A vast majority of resumes that come across a recruiter’s desk will be unqualified or a mismatch for any given role and a majority of candidates that are submitting applications for work are merely passively starting their job search.
What does that mean for a company’s hiring process? The period from initial job description posting to job offer letter going out is unnecessarily long.
With a competitive job market and an increasingly stellar pool of candidates, companies cannot afford a lengthy hiring process. Closing top talent requires efficiency and speed.
But, to make that happen, people teams need the proper tools.
Recruiters spend, on average, 23 hours per role just sourcing and shortlisting potentially viable candidates. That’s half a work week’s worth of time spent on a menial task that usually involves matching a candidate’s resume to the job description and standards set by the hiring manager.
It’s not just time that is saved — using resume screening tools will also help standardize the way a recruiter or hiring manager initially thinks about talent. It’s formulaic and consistent, rather than dependent on a person’s patience or willingness to read through the resume, and it creates an opportunity for a more diverse pool of candidates.
Beyond screening tools, software that automates aspects of the hiring process is also beneficial to closing top candidates.
The use of Applicant Tracking Systems (ATS) not only keeps tabs on candidates in every stage of the process and creates clear lines of communication, but it also offers a clean and automated collection of data, which can help your team make informed decisions on how you’re marketing and attracting candidates, and help with DEI efforts as you look to create a more representative workplace.
Introducing new, potentially costly, tools might seem like a burden, but, a clear and communicative process will help companies close top talent faster, and will decrease overall turnover. Finding the “right match” can help decrease turnover by 35%, ultimately saving cash, time, and human resources on repeated hiring processes.
Challenges we’re facing with AI tech in recruiting
There are numerous benefits to incorporating AI tech into your recruiting processes. However, there are potential drawbacks or considerations to mull through as you decide what and where to add in AI software.
First and foremost, your team must have an honest understanding of the state of your data. AI software requires large and clean data sets to learn and have stronger output. If your organization has not prioritized clean and accurate data collection, this might be a red flag that AI software is a better longer-term goal, rather than something to jump on quickly.
It can also be costly, especially initially. A new ATS or resume screening tool might be an expensive purchase or subscription and getting the “buy-in” or budget sign off from department head or executive teams might take a bit of effort, but, if your team can afford it, the data shows that the reward far outweighs the costs.
Beyond cost and data, there are also risks involved in replacing recruiting teams with software. As we continue to introduce new smart tools into our workflows, we must be mindful of the importance of human capital in recruiting and talent management.
Tackling bias in AI
Innovators are, generally speaking, well-intentioned. However, unintended biases can and do creep into algorithms.
To perform the expected task, algorithms must be taught the relevant identifiers and skills required of the given task. In some ways, you can think of teaching an algorithm in the same way that you teach a toddler — your implicit biases will be learned by the model.
Some people have the idea that mathematical models are objective and don’t have bias unless it’s designed that way intentionally. The reality is that many algorithms need data to learn from, which typically consists of past decisions made by humans. The model learns the human behavior, including any bias.– Senior Data Scientist, Global Hedge Fund
A well-known case example is Amazon’s recruiting software that was scrapped soon after its launch. The tool, which reviewed resumes and shortlisted top candidates and was expected to be the “holy grail” of modern recruiting tools, was found to be reviewing candidates with a bias towards applicants who identify as men.
To learn what “good” looks like, Amazon’s algorithm was fed hundreds of resumes of top applicants and hires. The problem? A majority of those resumes were male applicants and employees, showcasing, and in this case, reinforcing, the male-dominated complexion of the tech industry.
Of course, efforts are being made to tackle the inefficiencies and biases in the algorithms that are driving so much of what we do and how we live. For further reading on what this looks like, we recommend starting with this HBR piece.
Beyond bias and the lack of diversity in AI, There are also ethical questions to be considered, and a multitude of pros and cons to be weighed when we think about the best applications of AI in our world.
But, another conversation for another day.
What can we expect to see in the future?
When it comes to AI in recruiting, it’s safe to say that the best is yet to come. Recruiting and talent management is a notoriously outdated industry, and companies have only just begun the process of advancing and prioritizing the efficiency of their hiring strategy.
As more interest builds around the need to improve hiring practices, we can expect to see new technologies that match the needs of people teams at companies of all sizes.
What might that look like? Enhanced screening software is likely a more immediate tool that will come to the market. Yes, we already have and use screening tools, but they are not without their faults. Continuing to perfect how we can automate the resume (applicant) screening process will offer a reprieve from hiring teams large and small.
We might also see greater use of chatbots and digital interviews. As an HR-tech startup, Scouted was ahead of the curve on digital interviewing: Our Virtual Interview is a critical component of the candidate profile and key factors in why so many great companies choose to partner with us on their searches.
And, lastly, we might also see the introduction of predictive algorithms. Imagine a world where we could predict whether or not a candidate would likely receive an offer — from any company and any “match fit” role — within minutes of receiving the candidate’s profile?
But, for now, teams can and should rely on the screening, tracking, and interviewing tools that are at our disposal to create a smarter, more efficient, and faster process for the candidates — and the recruiting team.