On behalf of our team at Recruitment Intelligence™ and American Recruiting & Consulting Group, here is our response.

We use data and analytics from our AI Recruitment Intelligence Consultant, RiC, to improve diversity across our clients’ hiring pipelines. By analyzing ATS data, we can identify exactly where bottlenecks or bias may appear in the process.

For example, while working with one of the largest insurance companies in Florida, RiC helped uncover patterns in sourcing and applicant flow that limited diversity. Our data showed that most applicants were coming from a narrow set of universities and regional networks. By identifying these trends, we were able to diversify our outreach and attract candidates from new markets and professional backgrounds.

We adjusted our sourcing mix to include platforms beyond traditional job boards, tapping into Google, GitHub, and Stack Overflow profiles. This expanded reach gave us access to 100 percent of both active and passive candidates rather than the 15 percent visible through job boards.

The result was a more inclusive and well-rounded talent pool without sacrificing quality. RiC’s algorithm focused on skills and experience alignment instead of keywords, which helped us surface exceptional candidates who might otherwise be overlooked.

Once the shortlist was generated, our recruiters applied the human lens by assessing motivation, communication, and cultural fit. This partnership between AI and human insight improved hiring speed by nearly 80 percent while maintaining a 93.8 percent hire rate across industries.

We have seen that data-backed decision-making leads to real progress. By combining analytics with human expertise, we help organizations reduce bias, expand access, and make more confident hiring decisions that stand the test of time.

In today’s hiring landscape, data is one of the most powerful tools for building diverse and equitable workplaces. While many organizations claim to prioritize diversity, few fully leverage their Applicant Tracking System (ATS) to turn that goal into measurable action. When analyzed intentionally, ATS data can uncover hidden biases, highlight underrepresented groups, and reveal where in the hiring funnel diversity is being lost. By moving from intuition to insight, recruiters can transform their strategies and build more inclusive pipelines that reflect the communities they serve.

The key to using ATS data for diversity improvement lies in diagnosing the drop-off points. Many companies attract a diverse range of applicants but lose them midway—often during resume screening, interview scheduling, or assessment phases. By tracking candidate demographics (anonymously and ethically) at each stage, teams can identify where bias or procedural barriers occur. The next step is redesigning job descriptions, evaluation rubrics, and outreach efforts based on these insights. For example, replacing biased language in postings, expanding sourcing beyond traditional networks, and training hiring managers to evaluate skills rather than pedigree can dramatically shift outcomes.

In one project I led, our ATS data revealed a troubling pattern: while 45% of applicants identified as women or from minority backgrounds, only 18% reached the final interview stage. We discovered that many qualified candidates were being screened out early due to rigid keyword filters and narrow job requirements. To fix this, we broadened our job descriptions, restructured the pre-screening criteria to emphasize transferable skills, and partnered with diversity-focused job boards. Within six months, the diversity of our shortlisted candidates rose by 38%, and our hires became more representative of the market talent pool.

ATS data, when used strategically, can be a catalyst for equity and innovation. By pinpointing where diverse talent drops off and acting on those insights, organizations can build hiring systems that are both fair and effective. Diversity isn’t achieved through good intentions—it’s achieved through data-informed action. When recruitment becomes inclusive by design, it doesn’t just change who gets hired—it transforms how companies grow.

I’ll share what we learned at VIA Technology when we implemented our Applicant Tracking System (ATS)–the data completely changed how we approached recruitment timeline bottlenecks, which ended up directly impacting who made it through our pipeline.

We noticed our average time-to-hire was 47 days, but when we filtered by referral source, candidates from our Small, Minority, Women, and Business Enterprise (SMWBE) committee networks and local tech training programs were getting stuck in “resume review” for 12+ days longer than others. The ATS showed us that these resumes were being screened out early because they listed certifications and project-based experience differently than traditional four-year degree formats–our initial filters weren’t even surfacing them to hiring managers.

We rebuilt our screening criteria to weight hands-on Internet of Things (IoT) and low-voltage experience equally with formal education, and added specific keywords around vocational certifications and community college programs. Within two hiring cycles, our interview slate went from 8% to 31% candidates from non-traditional backgrounds, and our time-to-fill dropped to 34 days because we were suddenly seeing qualified people we’d been missing.

The real win wasn’t just numbers–it was that our field teams started reflecting the San Antonio communities we serve, which matters when you’re installing security systems in schools and hospitals where trust is everything. Track where your qualified candidates stall out, not just where they come from.

One way we used ATS data to improve diversity was by analyzing candidate drop-off rates by source and stage. We noticed that diverse applicants were applying at similar rates but were underrepresented in later interview rounds. The data showed a pattern: candidates from community job boards and referrals advanced less often after the first screening.

That insight pushed us to retrain hiring managers on bias awareness and to standardize interview scoring rubrics across all roles. We also broadened outreach by building partnerships with local professional associations that supported underrepresented groups.

Within six months, the proportion of diverse candidates reaching final interviews increased by 35%. The key lesson—diversity starts with visibility in your data, not assumptions.

I don’t use traditional ATS systems at Evolve–we’re a small clinic network, not a corporate recruiter–but I’ve learned a ton about diversity through mentoring new grad therapists and hiring across our Brooklyn locations. The key insight came from tracking where our best clinicians actually came from versus where we were initially looking.

Early on, I was defaulting to hiring from the same programs I knew–mostly large institutions with established names. But when I tracked patient outcomes and retention after two years, our strongest performers were often new grads from smaller programs or career-changers who brought life experience from completely different fields. One of our best PTs today was a former construction worker who went back to school at 35.

Now I actively recruit from community colleges with PT assistant programs, reach out to career-change candidates, and I’ve stopped requiring “X years experience” for roles where we can train effectively. We also started offering paid mentorship positions specifically for new grads, which opened our pipeline to people who couldn’t afford to work unpaid internships. Our team demographics shifted dramatically–we went from mostly matching my background to having therapists who actually reflect our patient population in South Brooklyn.

The uncomfortable truth I had to face: my “gut feeling” about candidates was just pattern-matching people who reminded me of myself. Once I started measuring what actually predicted clinical excellence (empathy scores during patient interactions, willingness to take complex cases, collaboration in team meetings), my hiring criteria completely changed.

I don’t work with ATS systems in my web design agency, but I’ve tackled a similar challenge when auditing user flows and conversion data for B2B SaaS clients. The principle is the same–look at where people drop off, then fix the friction points.

When redesigning Asia Deal Hub’s dashboard, we noticed through user testing that certain terminology and UI patterns were confusing for users from different Asian markets. We simplified the onboarding modal and reduced data fields by 40%, which made the platform accessible to non-native English speakers and less tech-savvy users. That single change improved completion rates across all user segments.

For Hopstack’s website redesign, we analyzed heatmaps and found that visitors from smaller warehousing operations were bouncing because the messaging felt too enterprise-focused. We added industry-specific landing pages with relatable use cases and testimonials from mid-sized companies. Traffic from that segment increased 35%, and demo requests became more geographically diverse.

The lesson: diversity improvements start with identifying where your current experience excludes people, whether that’s language barriers, cultural assumptions in your copy, or UI complexity that favors certain user backgrounds. Fix those specific friction points with real user data, not assumptions.

Our ATS data revealed that we were losing a significant percentage of diverse candidates during the technical assessment stage of our hiring process. The insight was that our test was inadvertently biased toward a very specific academic background, not practical skill. We immediately redesigned the assessment to focus purely on job-relevant, hands-on problem-solving, which successfully diversified the pool of qualified candidates reaching the final interview stages.

I don’t use traditional ATS systems, but I track similar pipeline metrics when hiring security guards, maintenance techs, and towing operators across my Houston-based companies. The data that shocked me most was seeing that 90% of my applicants were coming from just two zip codes, even though we serve properties across the entire metro area.

I started requiring my hiring managers to post openings at community centers and workforce development programs in underserved neighborhoods, not just Indeed and Facebook. Within three months, our applicant pool expanded to cover 15+ zip codes, and we hired bilingual security officers who could actually communicate with the diverse tenant populations we serve—something our previous teams struggled with.

The real win wasn’t feel-good diversity metrics. Properties with security teams that matched their resident demographics had 40% fewer tenant complaints and better lease renewals. One property manager told me residents finally felt comfortable reporting issues because guards spoke their language and understood their concerns.

Track where your candidates live relative to where you need them. If there’s a mismatch, your recruitment channels are broken, and you’re leaving qualified people on the table while wondering why positions stay open for months.