November 14, 2025
The AI Dilemma: How to Evaluate Talent When Every Résumé Is Perfect
Do employers care if a resume or cover letter is written by AI? Good question. Ask it to different people and you’ll get a range of answers. One indisputable fact, though, is that AI has made it ridiculously easy to spin up a clean, tailored application. That’s great for candidates but has become a headache for hiring teams who used to rely on writing quality and “customized” cover letters as a sign of effort or fit or any other number of factors. When everything from basics to fine-tuning is free, they stop telling you much about the person behind the resume and letter.
“Not very long ago, it wasn’t hard to see which candidates were taking the time and effort to polish up their applications, and that was a factor for many hiring teams in their initial evaluation of a candidate,” says ECLARO Co-Founder Paul Sheridan. “Today, we all know that the vast majority of resumes and cover letters are being written, or at the very least polished, by AI. That means:
The Paradigm Shift in Candidate Evaluation
- The Signal Is Gone: Cover letters and application text previously served as an indicator of a candidate's commitment and focus. Now, powerful AI can generate on-brand, high-quality writing in mere seconds.
- The Homogenization of Quality: Hiring managers are overwhelmed by a surge of "great-looking" applications. The traditional shortcuts for identifying truly differentiated, high-potential talent are no longer effective.
- The Erosion of True Value: Lacking reliable soft signals, evaluation teams over-index on easily comparable but often superficial metrics, such as rates and keyword matches, potentially missing core capability and fit.
Strategic Actions for Hiring Teams
- Decouple Effort from Evaluation: Make cover letters optional. Treat any submitted written statement strictly as a baseline communication sample, not a metric of motivation or "vibe." Assume AI assistance was utilized.
- Prioritize Empirical Evidence over Essays: Implement a concise, timed, task-based work sample that directly simulates day-one responsibilities. Use a simple, anchored rubric (e.g., 1–4) for objective scoring.
- Validate Competence Live: Structure interviews around dynamic, on-the-job problem-solving. This includes pair-debugging code, critiquing a technical design, or role-playing a critical client scenario. Standardize questions and tie them rigorously to a core competency rubric.
- Demand Verifiable Outcomes: Request and validate links to demonstrable work—shipped features, active portfolios, or brief case studies detailing quantifiable outcomes and contributions.
- Leverage AI for Synthesis, Not Judgment: Deploy AI tools to draft structured prompts, summarize interviewer feedback, and flag generic boilerplate language—but reserve the critical human judgment for final candidate selection.
How Candidates Can Drive Separation
- Show, Don't Tell: Provide direct links to executed work—code repositories, design mockups, deployed features, or compelling before/after transformation snapshots.
- Articulate the "Why": In technical interviews or take-home reviews, clearly walk through the rationale for decisions, trade-offs considered, and constraints managed.
- Establish Transparency on Automation: Be upfront about the tools used. State explicitly what was AI-automated and what constitutes your original intellectual contribution. Owning the process is the new foundation of trust.
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The ECLARO Takeaway
“AI didn’t just accelerate the writing process,” emphasizes Paul Sheridan. “It fundamentally altered the talent acquisition landscape. If your hiring teams are observing applications that are consistently polished but are seeing a performance gap once the candidate is on the job, you are likely not imagining it.”
“But the solution is not to ban AI—far from it. The answer is to strategically redesign your hiring funnel and evaluation processes to accurately measure what truly matters in every role: real-world skills, sound judgment and consistent execution.”
In essence, the AI revolution demands that organizations shift their focus from filtering résumés to validating competence. By implementing updated strategies, moving away from subjective assessments of written effort and toward evidence-based measures of performance, companies can successfully navigate the AI-driven application boom. This approach ensures that your firm hires talent based on true capability and potential, not just the quality of their automated self-marketing material.
This approach isn’t without its challenges, though. It demands deep experience and sophisticated insight into the human element of talent acquisition, coupled with a nuanced understanding of how to leverage AI ethically and effectively.
“We help global organizations execute this transition successfully,” adds ECLARO Co-Founder Tom Sheridan. “We focus on implementing tight, role-relevant work samples, instrumented and objective interviews, and AI-assisted evaluation methods that amplify the true signals of capability without increasing time-to-hire. Recognizing where ‘application polish’ is masking actual performance, and where a few focused changes can deliver a major competitive advantage, is at the heart of the process of finding the Right People today.”