We upgraded our ATS to one with "AI-powered resume parsing" that promised to "extract candidate data with 99% accuracy" and "automatically categorize skills and experience."

Two weeks in, the system confidently informed us that a software engineer's primary skills included "island geography" and "snake handling."

The engineer had listed Java and Python as their programming languages.

The AI took this literally.

How We Discovered Our $20K Resume Parser Is Illiterate

A recruiter pulled up a candidate profile and stared at the screen, confused.

"Why does this backend engineer's profile say their skills include 'Indonesian island vacation destinations'?"

We looked at the parsed data. Sure enough, the AI had extracted "Java" from the resume and categorized it under "Geography & Travel."

Python was listed under "Zoology & Animal Sciences."

This candidate's actual skill set—15 years of software development experience—had been transformed into a Jane Goodall / travel blogger hybrid.

Cool. Cool cool cool. This is fine.

We Checked Other Profiles

Naturally, we started reviewing how the AI had parsed other resumes. It was... a journey.

Candidate with "R" experience: Parsed as "knowledge of pirate speak"

Candidate who mentioned "Ruby on Rails": Categorized under "gemstones" and "transportation"

Candidate with "Swift" expertise: Listed as "fast runner" under Athletics

Candidate who worked with "Go": Parsed as "knowledge of board games"

Candidate experienced in "React": Somehow became "chemistry - chemical reactions"

The AI had apparently never encountered technical terminology and decided to interpret everything as literal English words. Every single programming language or technology was hilariously misunderstood.

The "C++" Disaster

My personal favorite: The AI saw "C++" on a resume and parsed it as "C " (C with extra spaces).

When we asked the vendor why, they explained that their parsing AI "occasionally struggles with special characters in non-standard contexts."

C++ is one of the oldest and most common programming languages in existence. It's been around for 40 years. It's not a "non-standard context"—it's literally one of the most standard things you could possibly parse in a technical resume.

But sure, "occasional struggles." That's one way to describe complete failure.

The Work Experience Section Was Creative Fiction

The AI didn't just mangle skills—it completely rewrote candidate work histories.

One candidate's experience at "Amazon Web Services" was parsed as "Retail / E-commerce."

I mean, technically Amazon does retail. But AWS is a cloud computing platform. The candidate was a DevOps engineer, not a warehouse worker.

Another candidate who worked at "Apple" got categorized under "Agriculture & Food Services."

The AI apparently thought they picked fruit for a living, not that they worked for one of the largest tech companies in the world.

The Education Section Had Strong Opinions

The AI's interpretation of education was equally creative.

"BS in Computer Science" was sometimes parsed as "Bachelors in Science" and sometimes as just "Science degree." The AI couldn't decide and picked randomly.

"MIT" was occasionally parsed as "MIT" and other times expanded to "Massachusetts Institute of Technology" and once—I swear this is real—parsed as "Military Intelligence Training."

"PhD" sometimes became "Doctorate" and other times became "Post-High School Diploma."

I'm not sure which is worse: that the AI doesn't understand common degree abbreviations, or that it invented "Post-High School Diploma" as a degree type.

The AI's Confidence Was the Best Part

Here's what really killed me: The parsing system showed a "confidence score" for each extraction.

"Java = Island Geography": 97% confidence "Python = Snake handling": 94% confidence "Apple = Agriculture": 89% confidence

The AI wasn't just wrong. It was confidently, aggressively wrong. It looked at "Java" in a technical resume context and thought "definitely an Indonesian island" with near-certainty.

If I could be that confident while being that wrong, I'd run for office.

The Contact Information Mishaps

You'd think parsing contact information would be straightforward. Names, phone numbers, email addresses—this is basic resume parser stuff from 2005.

The AI found creative ways to mess it up:

Phone numbers were sometimes parsed correctly, sometimes broken across multiple fields, and once—memorably—interpreted as a zip code.

Email addresses with dots or dashes confused it. A candidate with the email "jane.doe@company.com" had their name parsed as "Jane Doe Company."

LinkedIn URLs were categorized under "Social Media Marketing" as if the candidate's professional skill was understanding LinkedIn rather than just having a profile there.

And my favorite: The AI parsed "(he/him)" pronouns as part of one candidate's name. According to our ATS, we interviewed someone named "Michael Henderson He Him" who presumably has a very patient mother.

The Skills Extraction Was Just Vibes

The AI's skill extraction seemed to work by scanning for nouns and hoping for the best.

A candidate mentioned they "led a team" in their work experience. The AI extracted "Led" as a skill. Like the metal. Or past-tense of lead. Neither of which are relevant skills.

Another candidate mentioned working in an "agile environment." The AI extracted "agile" and categorized it under "Physical Fitness."

One candidate mentioned "driving growth" in a business development role. The AI extracted "driving" and listed it under Transportation Skills. As if they were applying to be a truck driver.

At this point, I'm convinced the AI was just scanning for random words and making up categories based on whatever popped into its neural network's head.

The Résumé With Accents Broke Everything

A candidate with an accented é in "résumé" completely broke the parser.

The AI apparently encountered the accent, panicked, and parsed the entire document as if it were in French. It then attempted to translate from French to English, despite the entire resume being written in English except for that one accented letter.

The result was a fever dream of partially translated phrases and confused category assignments. This candidate's job title changed from "Senior Product Manager" to "High Product Director" because the AI translated "Senior" to French ("Sénior") and then translated it back wrong.

The Vendor's "Solution"

We contacted the vendor to explain that their AI thought Java was an island.

Their response: "The AI is constantly learning and improving. You can manually correct mis-parsed data, and the AI will learn from those corrections."

So... we're training the AI by manually fixing every resume it mangles? Doesn't that defeat the entire purpose of automated parsing?

We asked why the AI didn't understand common programming languages in a technical recruiting context.

"The AI is trained on diverse resume data across all industries. Technology terminology requires industry-specific training."

Translation: They trained the AI on everything except the thing it's supposed to do (parse technical resumes) and now we get to manually teach it the difference between Java the programming language and Java the island.

For $20,000 per year.

What a deal.

The Hidden Cost

The vendor marketed this as a time-saving tool. "AI-powered parsing reduces manual data entry by 90%!"

What they didn't mention: You'll spend that saved time fixing the AI's hilariously wrong interpretations.

Manual data entry at least produces correct data. AI parsing produces confidently incorrect data that you have to review and fix anyway.

We're not saving time. We're just shifting time from "enter data correctly the first time" to "figure out why the AI thinks this candidate's primary skill is Indonesian geography."

We Downgraded Back to "Dumb" Parsing

After three weeks of the AI's creative interpretations, we downgraded to the ATS's basic parsing option—the one without AI.

It's less sophisticated. It doesn't try to categorize skills or understand context. It just extracts text from standard resume sections and puts it in the right fields.

And you know what? It works way better.

It doesn't think Java is an island. It doesn't transform backend engineers into zookeepers. It just... parses resumes like a normal piece of software from 2015.

Radical.

What We Learned

1. "AI-powered" parsing is often worse than basic parsing that doesn't overthink things
2. Confidence scores don't mean the AI is correct—they just mean it's confident
3. Training data matters, and if your AI wasn't trained on technical resumes, it will make technical resumes its enemy
4. Sometimes the "old" technology works better than the "new" AI version
5. If your time-saving tool requires you to spend time fixing its mistakes, it's not actually saving time

The Bottom Line

AI resume parsing sounds great in sales demos. In practice, it's like hiring an overconfident intern who's never seen a technical resume and doesn't believe programming languages are real.

Basic resume parsing that extracts text from standard fields works fine. It's been working fine for 15 years. Adding AI doesn't make it better—it just adds opportunities for the software to confidently misunderstand context and categorize software engineers as travel bloggers.

We're sticking with the "dumb" parsing that understands Java is a programming language, not an island.

Revolutionary insight, I know. But apparently in 2025, we need to specifically request software that doesn't think Python is a snake.

What a time to be alive.