AI in Hiring, Artificial Intelligence, ATS, Candidate Experience, Candidate Qualification Screening, Change Management, Company Updates, Data Analytics & Reporting, Diversity, Equity, & Inclusion (DE&I), Hiring Assessments, Hiring Manager Engagement, Job Descriptions, Leadership, Managing Costs, Resumes, Talent Acquisition Problems
Talent acquisition teams have many priorities, and it’s hard to balance them all. What’s interesting is that many of the same priorities show up on the top initiatives list year-after-year.
If you are looking to make significant progress in any or all of these areas, then you need a solution that can PROVE they address all of these initiatives with practical examples.
With SmartRank:
Productivity – you could give your recruiters back 30% – 70% of their time back in their day
DE&I – you could have zero unconscious bias in your screening process
Hiring Manager Engagement – simply give them applicants that are EXACTLY what they want
Compliance & Legal – no bias data + 100% inclusive and objective screening = no calls from the EEOC and/or OFCCP
Data Analytics – remove assumption and opinion driven anecdotal conversations with data driven conversations
Applicant Experience – something actually new, different, better, and ultimately benefits the applicant
We can show how we specifically address each of these initiatives in less than 5 minutes each. If you are curious, schedule time with us to learn more (https://lnkd.in/gpyUcDF2)!
AI in Hiring, Artificial Intelligence, ATS, Candidate Qualification Screening, Change Management, Data Analytics & Reporting, Job Descriptions, Managing Costs, Resumes, Talent Acquisition Problems
Research and survey data consistently tell us that “reviewing & screening” applicants is the most time consuming task a recruiter has. The time spent on this task ranges anywhere between 30% – 70% per day. This is staggering and highly inefficient.
One has to immediately ask, what process on earth could create this level of inefficiency? The answer is easy when you look at how most recruiters review and screen applicants, manually and tediously. Opening each applicant in a traditional ATS one-by-one, matching keywords on a résumé to keywords on a job description is going to take a long time. What’s worse…it’s not very effective. It is definitely what most recruiters do, but it is definitely not efficient or effective.
What other manual and tedious tasks do recruiters have?
* Sending applicants to hiring managers and following up for feedback
* Following up on feedback after an interview
* Working through the rest of the applicants one-by-one, either inviting them to interviews, dispositioning, etc.
So the big question is really WHY do we do this? Short answer…because this is how we’ve always done it. These problems with recruiters never having enough time, which ultimately leads to burnout, are not going away until we stop doing it the same way we’ve always done it.
One last thing to consider. If your risk tolerance only allows you to try a “slight evolution” then it will likely produce a “slight improvement.” Maybe you go from 50% to 48.5% time spent on screening. Or you can look at a “revolution” which could take you from 50% to 5% of your time being spent on screening/reviewing applicants. It’s all about how motivated you are to solve that problem.
AI in Hiring, Artificial Intelligence, Candidate Experience, Candidate Qualification Screening, Change Management, Hiring Assessments, Leadership, Resumes, Talent Acquisition Problems
“92% of companies view skills-based hiring as more effective than using a traditional CV (curriculum vitae), another word for résumé.” YOU THINK? Of course skills-based is more effective, but there are nuances to getting skills-based hiring right.
Are we finally at a point where we can stop using résumés to screen talent? We all hope so, because it’s highly inefficient, ineffective, and biased. You can find tons of research to back this up, or just go to LinkedIn and read the posts and comments from job applicants, recruiters, and even hiring managers lamenting about how bad the current hiring process is working today. I would argue the root causes for that dysfunction are the tools (e.g., résumés, job descriptions, legacy ATSs, etc.) we use in recruiting, and the processes those tools dictate we use.
There is so much talk in companies about “Skills Gaps” and “Improving Retention” and “Improving Productivity.” You fix those problems by hiring the best possible people you can. And you are NOT going to hire the best possible people you can by using résumés. In the same way there is so much talk within talent acquisition about “Improving Efficiency” and “Quality-of-Hire” and “Improving DE&I.” Guess what? You are NOT going to solve those problems by using a résumé either. In fact, you are likely to only exacerbate them.
There are smart, practical, highly-effective, and easy solutions that completely automate the screening process without using/needing a résumé, and I’m not talking about some ineffective biased AI algorithm. I know these solutions exist because I’m a founder of one of them!
AI in Hiring, Artificial Intelligence, ATS, Candidate Experience, Candidate Qualification Screening, Change Management, Hiring Assessments, Hiring Manager Engagement, Leadership, Resumes, Talent Acquisition Problems
Almost exactly one year ago I posted about a coming tsunami of applicants hitting the market (link in comments). Since that post, tech companies alone have laid off over 244,000 people (link in comments), and that’s not including smaller startups. To put that number in perspective, those same tech companies “only” laid off 80,000 at the height of the COVID pandemic from March – December of 2020, and only 15,000 layoffs total in 2021.
And that is just the tech industry. According to a recent Randstad Risesmart study (link in comments), 96% of companies took some sort of downsizing action over the past 12 months, and 94% anticipate taking further action in 2024.
You can see this tsunami of applicants playing out by just looking at some of the jobs posted on LinkedIn. They’ll be posted for hours and get hundreds of applicants.
In my post a year ago, I wrote about how talent acquisition (TA) teams were going to get downsized. With a lack of innovative tools, recruiters (or even TA leaders) would find themselves drowning in a wave of applicants. Unfortunately that is exactly what is playing out.
I probably don’t need to enumerate all the challenges that come as a result of having too many applicants and no way of efficiently and effectively screening all of them.
What does all this have to do with a lion and gazelle running?
Well, unfortunately many TA teams didn’t fundamentally change their tools or processes to handle this wave of applicants, and as a result they are dealing with those consequences.
In 2024 TA teams are either going to be hiring, albeit with an even smaller team, which means an even higher ratio of applicants to recruiters. Or, they are not going to hire in hopes the market returns in 2025.
Whether you are hiring (i.e., lion) in 2024 or not (i.e., gazelle), you should be implementing innovative technology NOW (i.e., running) as opposed to waiting until the problem is out of control, as I wrote about in a different post (link in comments).
You can’t control the market. You can’t control the hiring your company wants to do. But you CAN control the technology you use to manage those hiring peaks and valleys. If you don’t plan to put solutions in place in early 2024 to solve the coming problems in 2025, by the time you realize you have a problem…it’s going to be too late!
AI in Hiring, Artificial Intelligence, ATS, Candidate Qualification Screening, Change Management, Data Analytics & Reporting, Hiring Manager Engagement, Job Descriptions, Leadership, Managing Costs, Resumes, Talent Acquisition Problems
I was reading through a recent study named “Recruitment & Retention: Two Sides of the Same Coin” conducted by Aptitude Research which had some great data.
One data point in particular jumped out at me, “84% of recruiters…stated they do not have the tools they need to do their job well.” This statistic makes perfect sense. While the business world continues to change exponentially, HR and Talent Acquisition (TA) teams struggle to make the necessary changes to evolve at the same pace. Unfortunately these decisions impact everyone, not just the HR or TA teams.
This point above is specifically called out in the study and is probably the biggest challenge of all for HR and TA teams to overcome. The study states, “One reason that recruiters have not embraced this modern role is that they do not understand what it is or what they need to do. Most companies do not train, incentivize, or motivate recruiters to manage modern tasks. Yet, recruiters and recruiting departments tend to fall into the same patterns and routines, even when those routines are not bringing results.”
Sales doesn’t sell, marketing doesn’t market, and product development doesn’t develop the exact same way they did 25 years ago. If they did, their competition would crush them. I would even argue that business functions don’t do things the same way they did 5 years ago. And AI is changing the way we’ll be doing business 1-2 years from now.
If HR and TA leaders don’t immediately start breaking these patterns and routines in very significant and impactful ways (e.g., NOT introducing some new way of writing job descriptions), they will likely find themselves being replaced by people or technology that will.
As Leon C. Megginson once wrote in reference to Darwin’s Origin of Species, “It is not the most intellectual of the species that survives; it is not the strongest that survives; but the species that survives is the one that is able best to adapt and adjust to the changing environment in which it finds itself.” In maybe no other function of business is this quote more relevant right now than HR and TA.
AI in Hiring, Artificial Intelligence, ATS, Change Management, Data Analytics & Reporting, Hiring Manager Engagement, Leadership, Talent Acquisition Problems
Software should make our lives easier, not harder.
There is a concept called “opinionated” vs. “unopinionated” software. Here’s the very high-level definitions for both:
Unopinionated software – highly configurable but generally more manual task management because you make the decision for every single last thing that needs to happen within the system. In other words, there is not really any automation happening, you’re the one that has to do everything.
Example: Old email systems without spam filters. They are not going to filter anything because you have to make the decision on every single email as to whether it’s spam or not.
Opinionated software – less configurable but essentially involves more automation with tasks because it can assume the next logical step based on some assumptions. In other words, it automates much of the work for you, but you have to be comfortable with the assumptions it makes.
Example: Gmail spam filters. They make logical assumptions about what email is spam and what is not. They are not 100% correct all the time, but they’re pretty darn close.
Both types of software naturally have pros and cons. Which one is right for you really depends on preference. In the email spam filter examples above, you have to ask yourself whether it’s worth it to you to save the time with Gmail filtering out spam emails for you under some assumptions, realizing they won’t get it perfect 100% of the time. Or, maybe you are okay with your inbox being bombarded with every single email because you want to decide which emails are spam and which are not, but you’re going to waste a lot of your time filtering emails.
ATSs work in the same way. Generally speaking, unopinionated ATSs (which are most of them) create a lot of manual tasks and clicks for every single thing you want to happen. If you don’t take action, it won’t happen, and that can waste A LOT of your time. In other words, you end up working for your ATS.
SmartRank is more opinionated which creates automation. This saves time and reduces frustration. Your ATS ends up working for you.
If you want to see opinionated and therefore massive time-saving software in action, send us a note!
AI in Hiring, Artificial Intelligence, Candidate Qualification Screening, Data Analytics & Reporting, Hiring Manager Engagement, Talent Acquisition Problems
We’ve posted about this subject before…but why not do it again!!
Most recruiters and hiring managers know there is a disconnect when it comes to communicating job role qualifications. But do those two groups actually know how wide the separation really is?
Let’s look at an example. Let’s say we’re hiring for a Full-Stack Rails Developer. A typical job description would say things like:
– Proficiency in Ruby on Rails
– Ability to write clean ruby code
– Good understanding of front-end technologies like Javascript
– Bachelor’s degree in computer science, computer engineering, OR RELATED FIELD (this last part is always hilarious)
These ambiguous and undefined terms will not qualify any applicants in or out, and the role of screening the applicants will rest solely on the hiring manager.
Below are just 3 simple examples (which would probably be just 3 out of 12 total) of how SmartRank would screen and qualify these applicants:
AI in Hiring
Talent acquisition technology has mostly failed to help answer two simple, but vitally important questions:
- The Recruiter’s Question – of this group of applicants, which are the best ones to invite for an interview?
- The Candidate’s Question – of all the jobs currently available, which ones am I most qualified for?
The Recruiter’s Question
Picking the best applicants to invite to an interview is not an easy task. Doing it effectively requires the recruiter to have subject matter expertise for each position they hope to fill and clairvoyance to know what each individual hiring manager is looking for. Recruiters are often expected to look through dozens, maybe hundreds of resumes in a short period of time to pick out the best ones. In practice this amounts to recruiters performing a quick (few seconds) scan of each resume for “skills”, i.e. keywords, that match those on a job description. It’s no surprise that 77% of hiring managers say that recruiters’ candidate screening is inadequate.
This process of matching via keywords – e.g. a job description that says “must be proficient in Excel” matches a resume that includes the word “Excel” – does not lead to very good predictions. It wastes an enormous amount of recruiter and hiring manager time interacting with presumed matches (via phone screening or interviewing), only to discover e.g. the candidate’s experience with Excel does not match what the job calls for. In some cases this discovery occurs after the candidate is hired, which can be very costly. Such false positives happen repeatedly, resulting in hiring managers becoming increasingly disappointed with the candidates sent to them, and recruiters becoming increasingly frustrated that hiring managers reject so many of their candidates. This dysfunctional recruiter/hiring manager relationship is common to many organizations.
Exacerbating the problem is all the time recruiters and hiring managers spend looking at stacks of resumes of unqualified candidates. All the time spent focusing on unqualified candidates means less time spent on qualified candidates, which often results in the most qualified candidates being missed and failure to respond to applicants in a timely manner (if at all). False positives and false negatives both contribute to higher attrition rates across the hiring organization.
The Candidate’s Question
Jobs boards like LinkedIn and Indeed offer alerts and other matching services to assist job seekers in finding out about jobs that they would like and are qualified for. Nearly all professionals have tried these at one point or another and realized how terrible they are. The common frustration is that candidates are sent too many jobs that aren’t even close to what they are looking for and their time is wasted going through them to hopefully find one that is truly a good match. Although the jobs were ostensibly selected based on the candidate’s unique skill sets and experience (as advertised by the candidate), most are left wondering how most of these jobs were selected.
The answer is often by matching keywords on our resumes to keywords in the job description. The problem is, as it is with the recruiter’s question, that high-level summaries such as resumes and job descriptions don’t contain nearly enough information to make good predictions about who is qualified for a job let alone who the hiring manager is going to like.
The Common Root Cause
Screening candidates by trying to match resumes to job descriptions is the root cause of both of these failings. Resumes and job descriptions are, at best, high-level summaries geared toward advertising, containing a heavy bias toward false positives. They cannot provide nearly enough detailed information to make a good prediction about whether a given candidate would be qualified for a job.
Surface-level information such as “proficiency in Excel” or “willing to travel” may indicate a match, but once you go below the surface by asking each candidate specific questions about the job – e.g. if they are “comfortable creating pivot tables, using macros, and writing VBA code in Excel” and if they “can travel up to 60% of the month” – it becomes easy to see exactly where an applicant is not qualified for the job.
Figure 1 Matching with surface-level data results in too many false positives
Figure 1 illustrates a typical false positive arising from keyword matching and not looking below the surface, where all the disqualifying information is found. The false positive will result in the recruiter’s and hiring manager’s time being wasted on further interaction with this candidate, instead of actually qualified candidates.
False negatives also occur from looking only at surface-level detail, and these result in great candidates being missed because they didn’t include the right keyword or otherwise get past the hasty resume scan.
AI Doesn’t Fix This
There is no shortage of technology that attempts to improve this matching process with highly-structured skills taxonomies, advanced search tools, and AI. These technologies might save recruiters some time by speeding up the resume matching process, and might make better predictions than a human taking a few seconds to scan a resume. However, their predictive capability is severely limited by using only surface-level detail contained in resumes and job descriptions. Machine learning algorithms’ predictions are only as good as the data they use (to see for yourself, just ask ChatGPT who won the 2022 Oscars). AI algorithms trained to find resumes that resemble those of “good” employees learn to perpetuate and amplify the systemic bias that exists in the organization (prompting legislation at federal, state, and local levels) and their predictions will still result in a large number of false positives and false negatives because they don’t address the root cause: resumes.
The only way to save time and achieve better outcomes is to go below the surface and make predictions based on the critical details and context of the candidate’s qualifications and job requirements.
The Solution: Actual Intelligence
Better predictions lead to better outcomes, and the best way to improve predictions is with more data; not social media profiles or personality tests, but the critical details and context that pertain to the job. How do you get that data? You simply ask for it on the application.
The key to screening efficiently and effectively is asking highly-specific questions that have multiple choice answers that enable automation and create an even playing field for all applicants. When you ask all applicants the same set of questions pertaining to the qualifications for the job, you find out exactly what you need to know about every applicant instead of just what they want you to know.
Because all of the questions have multiple choice answers, applicants can more quickly go through the application (especially on a phone) while learning what the job really entails, and their answers are more honest and precise than what’s written on their resume.
When each answer has a point assignment you can automatically quantify exactly how well each applicant matches what the hiring manager is looking for and objectively stack rank all applicants. This eliminates the tedious work of looking at resumes and conducting screening calls with unqualified applicants.
When you track hiring manager sentiments and correlate them to selected answer options, you can use those correlations to better predict which applicants the hiring manager will like the most based solely on their answers. 
You can also source qualified candidates with a much greater precision than boolean searches for keywords and you can make significantly better predictions about which jobs to recommend to applicants.
SmartRank is technology that is enabling all of the above benefits and more for its customers.
SmartRank eliminates resumes entirely from the screening process, which addresses the root cause of poor predictions: using surface-level advertisements to match applicants to hiring manager preferences. Instead SmartRank uses actual intelligence to deliver unprecedented match prediction capabilities that deliver more qualified candidates to hiring managers without placing unrealistic expectations on recruiters to act as clairvoyant subject matter experts. This addresses the two root causes of the frustration cycle that leads to recruiter/hiring manager dysfunction.
SmartRank uses machine learning where it makes sense and can be effective. For example, we use large language models such as ChatGPT to help generate highly-specific, role-based screening questions. This significantly reduces the investment and increases the return for hiring managers. The predictive quality of the questions gets better over time with reinforcement from actual intelligence gathered by capturing and associating answers with hiring manager sentiments and other outcomes.
Replacing resume-based screening with actual intelligence removes the bias associated with resume data and places every applicant on an even playing field. It enables massive automation that reduces the amount of time spent on unqualified candidates, enabling recruiters and hiring managers to focus on the most qualified ones. When empowered with SmartRank organizations spend much less time and money on the hiring process so they can hire the best candidates faster.
Focus on the best and automate the rest with SmartRank. Get Started.
