For us, the biggest boost in diversity came when we automated one very specific part of our hiring process: sending out a competency assessment between the first and second interview. That single change reshaped the entire experience. Before this, we leaned heavily on resumes and first impressions; both of which are highly susceptible to bias. Candidates who looked good on paper or who happened to be charismatic interviewers naturally rose to the top, while others who might have thrived in the role were often overlooked. Once we automated the assessment step, every finalist walked into the second interview with a clear, objective picture of their performance profile, their cognitive ability, behavioral tendencies, learning style, motivation, and interpersonal style, all tied directly to the demands of the job. With that information, the second interview shifted from "Do I like this person?" to much more meaningful questions: Where will this candidate likely excel? Where might they need support? Does their intrinsic motivation align with the day-to-day work? It also leveled the playing field for candidates who may not interview as confidently. The assessment gave them a voice and a presence in the process that wasn't dependent on performance or polish. The result was simple and powerful: we began advancing candidates who might have been screened out early due to resume bias or because they weren't naturally strong interviewers, but who were actually very strong fits for the work itself. This mirrors the insight from the classic HBR study Job Matching for Better Sales Performance, which showed that when you match people to the right work, both performance and satisfaction improve. And when satisfaction improves, retention follows; something we've seen repeatedly. One candidate in particular stands out: they were quiet and methodical, not someone who would traditionally shine in an early interview. But their assessment showed near-perfect alignment with the demands of the role, and the second interview (grounded in data, not impression) allowed us to see their true potential. They went on to become one of our highest-rated team members and have since been promoted. Automating that one step didn't remove humanity from our hiring process; it removed noise. It helped us see people more clearly and more fairly. Our diversity, in background, working styles, and lived experiences, has grown because of it.
For us, at Tall Trees Talent, it just made sense to start at, well, the beginning. So we first automated the very top of the funnel -- the initial resume sweep. It was really a test to see if removing the human element early own lessened patterns of inadvertent bias. Humans, without meaning to, can often gravitate towards what feels familiar: certain schools, certain companies, certain titles. It's unconscious. I've done it myself. I've watched incredibly seasoned recruiters do it. And automating that first pass did far more than I'd even hoped. Suddenly, people from non-traditional backgrounds, people who'd broke into the field sideways, veterans retraining into industry, women and people of color who hadn't followed the classic career path -- all were winding up in our funnel. This was huge, because in energy, where we specialize, we've been dealing with a shrinking labor pool for years -- and apparently, sometimes sidelining qualified talent without meaning to. That's not just bad for diversity; it's bad for business. So, any step we can take to undermine this habit is valuable, and thankfully, technology is making it easier to do just that.
By automating sourcing and profile evaluation with our RiC platform, a large Florida insurance company identified diversity gaps from relying on a narrow set of universities and expanded sourcing to GitHub and Google to surface overlooked high-potential talent. As a direct outcome, they expanded access to diverse talent pools.
Automating resume screening has really paid off in terms of our diversity initiatives. Because we took out some of those subjective filters that always seem to sneak in when you're making early hiring decisions, we could ensure every candidate was given equal consideration-regardless of background, education, or personal style. This opened the candidate pool dramatically, and more people received interviews. I really think this is why we have seen over 19% more applicants from a non-traditional or under-represented background make the shortlist. It's great to see such progress!
For a small brand like Co-Wear, our biggest barrier to diversity wasn't bias; it was simply time—the lack of it. We couldn't manually source and read through hundreds of applications from diverse platforms. So, the automation that helped us wasn't some fancy A.I.; it was automating the initial candidate sourcing and tracking across specialized job boards. The specific change was using a simple tracking system that let us easily post the job everywhere—not just on the obvious places—and then tag every candidate based on where they came from. This eliminated the manual bias of only looking at the applications that landed easily in my inbox. Automating that first step forced us to fish in totally different ponds. The specific outcome was a huge shift in our creative network. We now have a roster of collaborators and team members from three new countries, specifically in our photography and styling roles. This means our brand's core purpose—inclusive fashion—is now being interpreted through a much wider, more authentic lens, and that is a win for our business and our customers.
We automated our initial skills test to create balance in the process and this helped us reduce early judgement that can ignore emerging talent. We also ensured that measurable performance became the foundation for every decision. One example came from a data learning role where automation revealed candidates who performed strongly even though they had limited formal experience. We also saw greater diversity in the shortlisted group and this created space for fresh thinking. We noticed stronger creativity from applicants with different backgrounds who approached problems in unique ways. This also strengthened trust in our overall selection method. The shift supported a more inclusive and balanced talent pipeline that continues to grow.
Automating the initial resume screening process helped increase diversity by enforcing Blind Structural Competency Screening. The conflict is the trade-off: traditional manual screening relies on human review, which creates a massive structural failure due to implicit bias from managers favoring familiar names or career paths. We needed to guarantee a clean, verifiable assessment of core skills. We automated the process of extracting and scoring only verifiable structural competencies—certifications, documented safety training hours, and measurable project experience with heavy duty materials. This process stripped away all identifying demographic data (names, school dates, personal information) before the human hiring manager saw the profile. The specific outcome we attribute to this change is a 25% verifiable increase in the number of female and minority candidates reaching the second-stage, hands-on interview where actual skills are tested. This demonstrated that the bias wasn't intentional malice; it was a simple, structural flaw in the manual process. By forcing the human decision-makers to focus solely on the applicant's structural capability first, we secured a foundation of equitable opportunity. The best way to increase diversity is to be a person who is committed to a simple, hands-on solution that prioritizes verifiable competence over abstract familiarity in the hiring pipeline.
Automating our initial resume screening process has been a game changer. By focusing on skills rather than backgrounds right off the bat, I was able to ditch those unconscious biases that can sneak in at the beginning of the hiring process. Instead, I could concentrate on reviewing applicants based on their experience, aptitude and potential, and that was a huge change. I noticed that this tweak helped us fairly consider applications from underrepresented groups, which in turn led to a more diverse team. Plus, we saw a huge speed up in the process, allowing us to interview a ton more candidates much more efficiently. The numbers bear this out, I saw a 35% hike in hires from non-traditional career paths. It totally shifted the team dynamic and brought in fresh perspectives that we'd never explored before.
We automated our screening process with sapia.ai, which led us to screen applicants based on an initial text-based interview that focused on showcasing their skills and qualifications instead of grading their compatibility for the job based on their demographics. This allowed us to approach our talent acquisition process more objectively as well as be able to give equal chances for every candidate hailing from different backgrounds. As Cafely's owner, I personally liked how this change influenced my team to take more risks and opt for more creative and innovative ideas for our marketing campaigns. I found it difficult to encourage teamwork at the start though, but we eventually managed to cultivate an inclusive culture through weekly check-ins via Slack, which also made it easier to make them trust our company so they can easily approach us should they start to feel uncomfortable about our existing policies at work.
One way automation helped us increase diversity was by removing names, photos, and schools from early resume screens. We let tools rank candidates based only on skills and experience first. The result was immediate. The interview pool became noticeably more diverse, and we started seeing strong candidates who likely would've been overlooked before because of unconscious bias.
As the first step of our application process, we now used a skills test to find out if someone can do the job or not. Only after they've completed the test and we know they're a good fit, we can see their details. This gives everyone an equal chance at getting their foot through the door and there is no discrimination based on the data from the resume. We use a tool called Vervoe, but there are plenty of others that can do the same thing. Just make sure that you first get the test results and select candidates based solely on that.
We automated the initial resume screening stage to remove name, age, and background indicators. As a result, more candidates progressed based purely on skills and problem-solving ability. From my perspective as CEO, this led to a noticeably more diverse shortlist and stronger hires who might have been filtered out before.
By automating segments of the talent acquisition process, we placed less emphasis on resumes and more emphasis on matching skills with applicants and programmatic consistency; thus, we were able to increase diversity. We combined different types of specialized DE&I focused technology and tools instead of relying solely on conventional ATS, reducing manual decisions by standardizing early screening phases. Additionally, DE&I focused on technology and tools assisted in identifying varying forms of biased language in job descriptions, as well as determining where candidates were exiting the application process. Utilizing a combination of AI generated information and systematic evaluations led to a more equitable and transparent hiring process and outputted a more diverse applicant pool. This applicant pool was able to move forward in the interview phases based on their abilities as opposed to just their prior experiences.