AI is a gift – and a pest.

In 2025, anyone can spin up a role-perfect résumé in minutes. Tools like ChatGPT make it trivial to tailor wording to any job, and "easy apply" means candidates can submit at scale. Surveys now show over 45% of applicants use AI to complete job applications, with platforms reporting double-digit growth in application volume year-on-year.

The result: postings commonly receive thousands of applications. In some cases, bots automatically submit on candidates' behalf. Volume increases while meaningful signal does not. Recruiters identify their top challenge as "too many unqualified applicants," with 62.6% reporting this as a primary concern. If the playbook is "post a JD and sift," AI simply accelerates the queue – creating more noise for teams and causing missed opportunities with quality talent.

Candidates still pay the friction tax

The experience is equally problematic on the job seeker side.

Every application represents "day one" repeatedly: new portal, new forms, no carried-forward history. Roughly 49% of job seekers believe applications are excessively long and complicated, with one-third abandoning processes that feel clumsy. Another report indicates 57% abandon applications due to complexity and transparency gaps.

Staffing arrangements compound this friction: landing a contract through an agency or MSP means new onboarding, new paperwork, new timekeeping systems—even for nearly identical work. The same person with identical skills faces a completely new process. Employer funnels have been optimized while candidates repeat cycles across systems. This manifests as abandonment rates, slower starts, and preventable backfills.

Metadata over marketing: why proof beats promises

In an AI-generated résumé landscape, evidence matters most.

Most AI hiring systems still emphasize self-reported information—titles, bullet points, and keywords. When everyone produces "perfect" wording, no genuine edge exists. The issue isn't résumé quality; it's proof.

The real solution involves metadata and transaction history:

• How many hours has this person worked in similar roles?
• What outcomes were delivered—features shipped, tickets closed, accepted assets, met milestones?
• Were timesheets submitted on time? Were statements of work delivered without escalation?
• What feedback did previous clients provide upon project completion?

For candidates, this becomes portable reputation: not claims about accomplishments, but system-verified delivery evidence. For clients, it removes risk and saves time. Instead of analyzing a polished PDF, hiring teams see actual work records: "This engineer merged 40 pull requests tied to production incidents; this designer delivered 12 accepted assets across three sprints; this project manager met four SOW milestones on time." In a noisy AI environment, verified evidence carries substantial value.

The direction for recruiting should be: one verified profile, supported by transaction history that compounds trust with each assignment.

What "good" should feel like (and why it matters to you)

In this model, fundamentals are straightforward:

• One verified profile with skills validated through actual work
• Portable history—hours, outcomes, references—composing into a track record across assignments
• Transparent matching explaining why a candidate fits and how to strengthen alignment
• Human involvement at critical decision points

The experience should resemble Amazon purchasing: identity is established, quality is evident, and proceeding requires a single click. When candidates navigate streamlined processes, conversion improves, signal clarity increases, and time-to-start shrinks.

AI serves a clear function—clearing obstacles, not creating them. Properly deployed, AI sources, screens, schedules, and explains matches. Poorly applied, it amplifies spam, conceals bias in opaque systems, and undermines confidence. Nearly 88% of companies use some AI in screening, yet over two-thirds of adults hesitate applying to positions relying on AI decisions.

The distinction isn't algorithmic—it's foundational: a single source of truth, verification embedded in operations, and design where agents handle volume while humans maintain judgment. This balance builds candidate and hiring manager confidence, translating to measurable throughput improvements.

What we shipped – and the impact for your teams

WorkGenius launched a redesigned candidate application addressing friction on the applicant side to deliver speed and quality for hiring teams.

The complete experience includes:

• Fewer clicks, reduced drop-offs. A streamlined path from interest to shortlist increases completed applications from qualified talent and minimizes disengagement.
• Enhanced relevance. Improved discovery surfaces more targeted roles based on skills, history, and preferences, starting shortlists closer to ideal fits.
• Simplified timesheets and assignment interfaces. Administrative work stops blocking progress; projects maintain momentum post-offer rather than stalling on paperwork.
• Explainable matching. Candidates see match scores and improvement opportunities; hiring teams understand why someone qualifies, not just numerical ratings.
• AI candidates control. They can consult an AI recruiter to refine experience descriptions and customize résumés with explicit review before submission, raising quality without increasing noise.
• Strategic human involvement. Recruiters remain engaged for context, calibration, and final approval, as trust remains essential for closing strong talent.

Metadata and transaction history—hours, milestones, feedback—are captured automatically, ensuring over time that shortlists come with evidence rather than marketing language.

Why this matters: improved conversion, clearer signal, faster project starts, fewer backfills. When process friction doesn't penalize candidates, they progress more quickly and honestly; transparent, evidence-based matching enables confident hiring manager decisions. Integrated contracts, compliance, and time tracking keep operations clear, helping accepted offers convert to immediate delivery.

Where we're going next

Launch represents a beginning, not completion. Future priorities include:

• Expanded verification with skills tied to real work artifacts and platform-based outcomes
• Increased transparency regarding match scoring rationale
• Greater portability of candidate history across multiple clients and roles
• More human touchpoints precisely where they create impact

Recruiting transforms not through posting more positions or reviewing additional résumés. Change happens when signal, speed, and trust become one integrated design—starting with applicant experience, grounded in demonstrable work proof, and flowing through to team throughput.

Onward,
Daniel