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Hiring is at a turning point. For decades employers relied on resumes and degrees as the default filters in the hiring process. But those tools have become outdated as employers increasingly learn that talent is defined by capabilities, not credentials. Skills-based hiring is rapidly gaining traction. But while its promises are clear, the path to implementation is complex.
Thankfully there are now AI based tools that, when applied thoughtfully and ethically, can accelerate the transition by streamlining decision-making, evaluating skills, and reducing bias at scale.
The problem with resumes
Resumes are inconsistent and can privilege candidates who have had access to elite educational institutions or worked for well-known companies. A resume is great at telling you where someone studied, but it falls short of telling you how well they can perform. It lists jobs they’ve held while generally saying little about how effectively they executed them.
But in today’s skill dependent labor market, this approach leaves companies behind as they overlook talented candidates who took nontraditional paths to success: veterans, caregivers, career changers, and so many more who have developed the skills but not their resume. The result? Narrow hiring pipelines, missed opportunities, and regrettable hiring outcomes.
Why skills-based hiring is the future
Skills-based hiring flips this model. Instead of trying to infer ability from educational background or credentials, it evaluates real-world skills. This can work in a number of ways: short assignments aligned with the role, structured interviews tied to competencies, or realistic work samples to demonstrate how candidates think and solve problems. Rather than sort by pedigree, companies can review actual performance or responses.
According to research from the Harvard Business School, fewer than 1 in every 700 job applicants can secure a job without an advanced degree. Some companies have taken the proactive step of dropping degree requirements from job listings, but despite the change, actual hires without degrees have only increased 3.5% between 2014 and 2023.
By taking a step away from resumes employers can not only truly broaden their talent pool but also improve retention by ensuring hires are truly matched to their work.
How AI makes the shift scalable
While the argument for skills-based hiring is compelling, scaling it across a large applicant pool is an immense logistical challenge. This is where artificial intelligence can help. With the right design AI enables employers to qualitatively and quantitatively analyze every applicant with the same level of scrutiny. This surfaces more qualified candidates faster and has the added benefit of reducing hiring bias.
What makes AI well-equipped for the challenge is its ability to standardize early evaluation. Instead of recruiters having to sift through hundreds of resumes, AI systems can parse applications, anonymize demographic signals like names or schools, and focus purely on relevant skills—including adjacent and transferable ones. It also makes the process more consistent. Human reviewers will inevitably differ in what they prioritize whereas AI tools can apply the same criteria across every application. This also allows recruiters to spend more time on high value activities like interviewing and supporting candidates.
The risks, and how to avoid them
But AI is not a silver bullet. Poorly designed systems run the risk of recreating the same issues they’re supposed to eliminate. Research has shown many models trained indiscriminately on historical hiring data tend to reproduce the same patterns of exclusion. Datasets used to train the AI tools must be carefully curated with fairness in mind.
That’s why AI governance is especially crucial in the hiring process. Employers need to understand what criteria are being used and how applicants are scored. They should be ready to ask tough questions of potential vendors: what data was used to train the models? How are outcomes audited for fairness? What safeguards exist to prevent rankings from being influenced by protected characteristics? While AI can accelerate progress, it can only do so when deployed responsibly.
CLARA’s approach
CLARA was designed from the ground up to make skills-based hiring easier, fairer, and more effective. Our algorithms are specifically designed to minimize inequities and sharpen evaluation of job-relevant skills. Identifying details about candidates such as names, school names, and previous employers can be anonymized before being passed on for evaluation.
Using neuro-symbolic AI, CLARA picks up on transferrable and adjacent experiences to score candidates job-related and future-ready skills, including transferable and adjacent ones. Once skills have been evaluated and measured, applications can be de-anonymized for a full human-led review. While CLARA is capable of scoring and ranking candidates, it cannot make any decisions about which applicants to promote or suppress. A human is always kept at the center of the decision-making process. The final say is up to recruiters and hiring managers.
A smarter path forward
Resumes were designed for a different era. Skills-based hiring represents a smarter, more equitable way forward. It’s designed to highlight candidates with future-ready skills that can move businesses forward, not keep them in stasis. But making it practical at scale requires tools that prioritize fairness and transparency.
AI, when applied with care, is the catalyst that makes this transformation possible. By combining the principles of skills-based hiring with technology that evaluates candidates on their true capabilities, organizations can move beyond credentials, find the best talent faster, and build workplaces where more people can thrive.
Listen to your talent, not just their resumes. Talk to our team to learn how CLARA supports organizations making the transition to skills-based hiring.