The Hiring Process In The Era Of AI—Here's What You Need To Know

Forbes

27-05-2025

Gergo Vari is the CEO at Lensa, Inc. Passionate advocate for recruiting and human resources technology that puts people first.
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Imagine you're a software engineer with 15 years of experience. You've applied to 100 jobs without getting a single response. Why the radio silence? The problem isn't a lack of skills—it's that AI systems are rejecting your résumé before humans ever see it. I witness this every day in my work in hiring technology. The machines are making decisions about your future, but they're not always making the right ones. This scenario plays out on both sides of the hiring process, and it's time to fix it.
I experienced the problem firsthand after graduating. My applications disappeared into the void despite my qualifications. My résumé contained the right skills but missed the terminology that AI screening systems scan for. Learning how these systems worked transformed my approach and results. This experience gives me perspective from both sides of the hiring equation. Let me share what I've learned about making AI an ally rather than an obstacle in the hiring process.
Most companies today use applicant tracking systems (ATS) to scan résumés. These AI tools filter out applications before a human sees them. A candidate I worked with last month had led major projects at Google, but his résumé used "Project Lead" instead of "Project Manager." Unfortunately, the AI missed the connection.
What's the solution?
For Job Seekers: Avoid keyword stuffing. Instead, carefully study the job description. Match terminology precisely—when they say "project manager," use "project manager." Keep formatting clean and simple—fancy graphics and columns often confuse AI systems.
For Hiring Teams: Your ATS likely enforces overly rigid requirements. Create synonym clusters for important job skills and titles in your system. When "Project Lead" and "Project Manager" represent equivalent skills, configure your system to recognize both. Regularly audit which applications get filtered out to check for concerning patterns. Many qualified candidates get missed because of terminology differences rather than skill gaps.
Modern AI tools can generate cover letters quickly. Hiring managers have grown adept at spotting AI-generated text, and some companies now automatically flag suspiciously polished applications.
Here's how to solve this:
For Job Seekers: Consider AI as a starting point for structure, then incorporate your personal experiences. Include challenges and solutions from your career. AI won't know how you turned around a failing project or solved a team conflict.
For Hiring Teams: Train your reviewers to look beyond surfaces. Some candidates use AI tools to overcome language barriers or organizational challenges before adding personal details. Focus on whether the application contains specific examples demonstrating relevant skills. Application questions requiring personal insights often reveal which candidates bring genuine experience versus AI-generated responses.
Job seekers often waste hours daily searching job boards, missing opportunities because companies use different titles for essentially the same role. AI-powered job platforms can decode the skills behind various titles. How to solve?
For Job Seekers: These platforms analyze your work history to suggest roles you might never discover otherwise. Your experience managing remote teams could qualify you for positions labeled "distributed workforce coordinator"—a title few would actively search for.
For Hiring Teams: Evaluate your job titles and descriptions through the lens of searchability. Do you use industry-standard terminology that qualified candidates search for? Focus requirements on core capabilities rather than credentials that AI might apply as absolute filters. Exceptional candidates often come from adjacent fields with transferable skills that conventional keyword matching overlooks.
Beyond résumé screening, AI now conducts interviews. Many companies use AI to evaluate video interviews, analyzing everything from word choice to facial expressions. My advice:
For Job Seekers: Focus on clear, structured responses. A technique that consistently works well: Describe the situation, explain what needs to be done, detail your specific contribution and share measurable results. Practice speaking confidently without filler words—AI systems react more negatively to hesitation than human interviewers.
For Hiring Teams: Provide transparency about how AI evaluates interviews. Remember that these systems struggle with cultural differences in communication styles and may penalize candidates with speech patterns outside their training data. Always have human reviewers validate AI assessments, particularly for candidates with strong qualifications but lower AI scores.
AI tools routinely reject candidates with employment gaps, nontraditional career paths or industry transitions. Talented professionals get screened out for taking time off for family responsibilities or career changes.
For Job Seekers: You can work around these limitations. Consider a scenario where a job seeker has a two-year gap after caring for a sick parent. By highlighting relevant volunteer work and skills development during that period and framing the experience in machine-readable terms, previously closed doors can open.
For Hiring Teams: Regularly audit your AI systems for bias in screening decisions. Examine who gets rejected—are candidates with unusual career paths filtered out disproportionately? Implement override protocols that flag potentially valuable candidates with unconventional backgrounds for human review. Consider a random sampling process where some rejected applications still receive human evaluation.
The future of hiring lies in the partnership between humans and machines. Understanding AI helps both sides connect more effectively. But we should never forget that hiring is about human potential, which algorithms can never fully measure.
Your experience, skills and potential are real. As a job seeker, don't let an algorithm tell you otherwise. Learn to work with these systems, but work around them when needed. The right opportunity is out there—sometimes you just need to help the machines understand why you're perfect for it.
As a hiring manager, remember that your company's next great hire might be someone your AI nearly filtered out. Make sure you've built systems that help you find talent, not just eliminate applications. The most successful organizations will be those that find the right balance: using machines to process information while relying on human insight to spot potential that algorithms miss.
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