Applying for a job is already an emotionally charged process. Candidates invest hours refining their resumes, crafting compelling cover letters, and gathering supporting materials—only to encounter an Applicant Tracking System (ATS) that treats them as data-entry clerks rather than potential hires.

Today’s ATS platforms, used by companies of all sizes, offer a subpar user experience that introduces unnecessary friction into the hiring process. As they prioritize HR compliance and recruiter efficiency, they neglect the candidate experience entirely. This leads to frustration, disengagement, and even talent attrition before the hiring begins.

Most ATS, like Workday, Dayforce, Taleo, or iCIMS, force candidates to:

• Create a user account before even starting the application process.
• Upload documents like resumes and cover letters.
• Enter personal data, often beyond what is necessary, such as requiring a full home address.
• Experience incorrect parsing that repopulates sections such as experience, education, and skills inaccurately, forcing candidates to review, remove, and manually correct errors before submitting.

Other systems, such as Greenhouse or Bamboo, provide a more modern experience. However, candidates have no clear indication of whether the system is correctly reading their resumes.

Nowadays, the consensus is that candidates must tailor their applications to each job post, with recommendations suggesting at least 30 minutes per application. In 2025, 58% of the workforce will seek a job. At this scale, improving the candidate experience and reducing the time required to apply for a job translates to massive savings of wasted time.

The Candidate’s Pain Points

1. Repetitive Data Entry
   • After uploading a resume, the chances that the ATS correctly extracted the work history, education, and skills are slim. Candidates then have to take on a reviewer/editor job to ensure the information is correctly captured, usually by copying and pasting what’s in their resume. This inefficiency signals a disregard for candidates’ time.
2. Limited Flexibility for Candidates
   • Candidates cannot clarify or expand upon critical qualifications even when parsing works correctly. If an important skill or project isn’t explicitly stated in their resume, the ATS won’t surface it. For the candidate, it becomes a game of guessing and hoping for the best.
3. Lack of Empathy in the Process
   • Hiring is fundamentally about people. Yet, most ATS interactions feel mechanical, prioritizing recruiter convenience over candidate experience. This is a classic example of what I’ve discussed before: product teams must combine data-driven efficiency with user empathy to create meaningful solutions. In a time when AI-driven applications are simplifying complex workflows across industries, there is no excuse for forcing applicants to navigate outdated, cumbersome interfaces that strip away the human element of hiring.

Do you want to learn more? Check out r/recruitinghell, which has hundreds of daily messages highlighting candidates’ struggles today.

Why This Problem Matters

The consequences of a flawed ATS experience extend beyond candidate frustration:

• Higher Drop-Off Rates: Lengthy, tedious applications deter top talent, particularly in competitive fields where candidates have multiple job options.
• Poor Employer Branding: A frustrating application process leaves a negative impression of the company before the first interview.
• Inefficiency for Recruiters: If the ATS fails to extract and present relevant qualifications accurately, recruiters must spend additional time manually reviewing applications.
• Missed Opportunities: Companies risk overlooking qualified candidates simply because their ATS couldn’t interpret their resumes correctly. A candidate with 10+ years of experience in Python may be ignored simply because the ATS didn’t recognize their skills in an unconventional resume format.

A Modern Solution is Long Overdue

With the rise of AI-driven automation, these inefficiencies are no longer justifiable. A more intelligent, empathetic approach to ATS design—one that balances candidate experience with recruiter efficiency. All it takes for companies to bring these systems to market is to consider candidates as meaningful stakeholders to attend to.

If I were in a position to rethink the candidate experience, this is the PRD I would use to start the conversation:

Product Requirements Document (PRD)

Objective

• Improve data extraction and candidate experience by reducing redundant manual entry.
• Ensure better decision-making for recruiters by surfacing relevant qualifications in a structured manner.

User Stories & Use Cases

• As a candidate, I want my resume and cover letter to be automatically and correctly parsed so I don’t manually enter redundant information.
• As a candidate, I want to review and edit extracted information to ensure accuracy.
• As a candidate, I want the opportunity to add clarifying information about my skills and experience if my resume does not fully reflect my qualifications.
• As a recruiter, I want a structured summary of candidates’ qualifications mapped to job requirements.

Key Features & Functionalities

• LLM-powered Resume & Cover Letter Parsing
  • Automatically extracts work experience, education, skills, and achievements.
• Qualification Mapping
  • Compares parsed data against job requirements.
• Candidate Self-Validation & Enhancement
  • Allows candidates to supplement missing details to strengthen their application.
• Recruiter Dashboard
  • Provides structured, enriched profiles for quick assessment.

Success Metrics

• Reduction in time spent on candidates filling out manual forms.
• Increased accuracy of candidate profiles.
• Improved recruiter efficiency in assessing applicants.

Prototype

Process

Technology

• Haystack to implement the pipelines to read the documents, extract data, and perform assessments.
• FastAPI-based web service.
• A Next.js web app to mock the prototype.

Code for the service is available at https://github.com/estebanf/better_ats_service

Experience

We’ll assume the candidate is a software developer. In their resume, they have references to experience with Python. The cover letter mentions they had worked on machine learning projects. Both documents use Microsoft Word templates and are rendered as PDF files.

 

The job opportunity calls for these experiences

• 5+ years of Python development experience
• Experience with AWS cloud services
• Strong background in machine learning
• Experience with FastAPI or Django
• Experience is sales

So, the candidate submits their resume

Using a small model like GPT-4o-mini, we can get better extraction than most ATS will do when resumes have styled formats like the used resume.

The ATS would also ask questions about those requirements that can’t reasonably be determined by reading the documents.

 

If the candidate had submitted their resume and cover letter, the service would have found references to their machine learning experience and marked that requirement as met.

As a candidate, an experience like this is efficient and relevant. It would help them provide additional information to highlight why they are a good candidate, or it would have helped them realize there are gaps between their background and the job post.

This process could feed recruiters with more accurate and relevant data for their evaluation and assist them in making more informed decisions when deciding whether engaging with a candidate makes sense.

Conclusion & Key Learnings

Technology should solve problems, not create new ones. The most effective products stem from deep user empathy, ensuring candidates and recruiters benefit from AI-driven improvements. By prioritizing user experience, ATS vendors could create a smoother application process and strengthen the quality of hiring decisions.

Furthermore, AI-driven solutions have real, tangible applications in workflow automation. When used correctly, they don’t just replace manual processes—they enhance them, enabling candidates and recruiters to focus on what truly matters: finding the right fit.