‘Don’t use these fonts, section headings, or file types, unreadable.’
OK.
‘No creativity, please, no wordart, or graphics, don’t stand out, unreadable.’
OK.
‘Don’t clutter your CV with photos, tables, columns, headers, or footers, unreadable.’
OK.
How many of these little nuggets have you read or heard about when the topic of résumés comes up? I heard them all and many, many more – from ‘experts’ and fellow job seekers alike. Everyone wants that edge, but not too edgy. Clarity, but using a specific format. Showcasing you, but … there’s always a but.
While researching this topic, I had my résumé open the whole time to see if it stood the test. It never did. I’d rephrase and rewrite sections according to what I read. Only to revert back or make new changes. Each article gives hope of the ‘perfect formula’ to pass the Applicant Tracking System (ATS) and get the attention of the human on the other side.
But here’s where it gets interesting – there’s no unified ATS that all companies use. No. Each company uses a different system, with its own unique algorithms. And you, dear applicant, need to figure out which ATS the company uses so you can tailor your résumé to pass it.
How about that for a plot twist.
ATS in Action
The way ATS works is simple: it reads résumés by electronically analyzing (parsing) the relevant information, like name, education, and experience, then sorts them for the recruiter in a searchable format.
One of its common features is keyword search – the system looks for specific words already mentioned in the job description, e.g. a specific number of years’ experience, certain skills, or a location. It also tracks candidates through the whole process, from application to interview results, and saves their information even if they were not selected, for future opportunities.
Beyond screening, it also handles job postings, interview scheduling, compliance reporting, and candidate notifications – managing the entire hiring workflow from start to finish. The system is built to handle large numbers of résumés, which is why it’s widely used to streamline the hiring process and free up time and resources.
A Timeline
The hiring process was not as sophisticated as it is now. Companies sent job postings to newspapers, you read the ad and sent your résumé by post or in person, recruiters waded through the lot and chose the most suitable candidates and invited them – by mail or by phone – for an interview.
Too slow. Something had to be done to speed things up. Enter ATS.
In the 1970s, it was just basic data entry with limited reporting capabilities. The 1980s saw added features like résumé parsing for faster sorting and analysis. The drawback was that it was expensive and difficult to use, which made it only implemented by large enterprises.
With the emergence of the internet in the 1990s, job postings and applications moved online. The system saw more advanced algorithms like candidate evaluation and ranking.
From the 2010s onwards, the Cloud enabled smaller companies to use ATS due to scalable and flexible subscription payments. The system’s analytics and reporting capabilities became more advanced, tracking criteria like cost per hire and time to fill. As mobile technology evolved, more and more candidates began using their mobile devices and social media accounts to apply.
When it comes to integrating AI, ATS is no exception – many providers have already built it into their products. There are even predictions that the future might even bring virtual reality and augmented reality technologies that could change the interview process completely.
ATS as a Business
The business of ATS software is booming.
A 2025 report shows that the global ATS market was worth $2.5 billion in 2024 – a 12.3% jump from 2023. The top ten vendors alone controlled over 50% of that market, led by iCIMS at 10%, followed by Oracle, Workday, and Greenhouse Software. Annual growth among the top vendors was sharp: in one year, Workday grew 15.3% and Greenhouse 13.2%.
Reading the profiles of these leading vendors, one thing is very clear – AI is the star of their products. iCIMS has expanded its AI footprint with conversational tools and assessment features, even piloting autonomous AI Agents to handle sourcing and interview tasks.
Oracle integrates generative AI into its Recruiting Cloud, using embedded co-pilots for job description optimization and candidate ranking. Workday is building a multi-agent ecosystem through its Illuminate system. Greenhouse is evolving into an agent-oriented platform, enabling third-party conversational AI to autonomously screen and schedule interviews.
Together, these plans paint a picture of ATS companies positioning AI as the backbone of the entire hiring process – shifting routine tasks to automation, with more on the way.
A 2026 report by 360 Research Reports puts the market at nearly $5 billion and is projected to reach $13 billion by 2035. As of 2025, 94% of Fortune 500 companies use an ATS, and 78% of large enterprises have one built into their hiring process. Small and medium businesses are not far behind – 62% have adopted cloud-based platforms. AI-powered screening tools grew 46% year-over-year, and demand for mobile-friendly and analytics-driven systems has grown 59% since 2022. In the new analysis, Workday now holds 14% of global market share, while Oracle holds 12%.
Two things stood out in the 360 report key findings: a ‘56% rise in AI-driven candidate scoring and predictive analytics tools’, and ‘47% of new product launches focusing on AI, machine learning, and mobile optimization.’
What this means is that the AI-powered ATS train didn’t just leave the station, it’s almost at its destination. The demand for it is growing faster than you can say ‘bias.’ Companies implementing it will not look back now, even with four out of ten – according to the same report – saying they struggle with integrating and migrating data from older HR systems.
AI in hiring is here to stay.
Built on Broken Data
The study by Douglas Guilbeault and colleagues reveals how generative AI like ChatGPT perpetuates gender and age bias in hiring. When prompted to create over 34,500 résumés for 54 jobs using typical male or female names, ChatGPT portrayed women as younger and with less work experience than men.
When asked to evaluate the same résumés, the AI ranked older men highest in quality, putting older women and younger applicants at a disadvantage – the same groups that already face discrimination in the real world. This happens because the model draws from internet data filled with stereotypes (e.g., men are better at ‘fixing things’ and therefore suited for roles like construction) – amplifying societal biases rather than reflecting objective reality.
Guilbeault observes that AI companies are aware of the problem. But their fix is to add filters to block the most obviously biased outputs. He argues that this barely scratches the surface – it misses subtler biases like the age and gender gaps the study found. Real progress means tackling bias at the core of how these models are built, not patching after the fact. Until then, his advice is simple: be cautious. These tools can make you believe the issue of bias is resolved when it’s really not.
Another study tested several LLMs from OpenAI, Google, Anthropic, and Meta by having them score over 360,000 randomized résumés with different gender and racial identities. Compared with equally qualified White men, most models gave higher scores to female candidates (both Black and White) but lower scores to Black male candidates. These differences translate into real hiring impacts: women would have a higher chance of being selected, while Black men faced reduced odds.
Together, the two studies show that AI bias in hiring cannot be solved with patchwork fixes. It shows up in how résumés are generated, how they’re scored, and whose careers pay the price. With these inherent biases baked into the data, why do we expect AI-powered ATS to be fair in résumé screening?
Gaming the System
Writing a résumé should be an easy task – you put in the basic information an employer needs to consider you for a job, right? Wrong. It’s more than that, much more. I thought I knew a thing or two – I’ve had one for years. But after a career break, getting back into the job market, writing my résumé was anything but easy.
There is so much advice out there on how to write one, so I won’t repeat it here. What I want to focus on is something most of that advice misses – it’s not the usual suspects. Many websites advertise ATS-friendly templates complete with checker and scorer services, and if it doesn’t meet the minimum score, you need to rewrite it. Fine, I can live with that. What I’m not fine with is having to look for which ATS the company is using to tailor my résumé according to its algorithms.
For a simple experiment, I tried a LinkedIn job search, clicked on the ‘Apply’ button – not ‘Easy Apply’ – and it took me to the original website for the job. The platform the job was sourced from uses iCIMS. It was easy to see, I found it at the bottom of the page: ‘Powered by iCIMS.’ It’s also in the URL: ‘nameofplatform.icims.com/jobs/’
‘What do I do with that info?’
Easy, you google ‘how to optimize résumés for iCIMS’, which will give you many articles that try to explain how that specific system works. For example, it prefers simple fonts – Arial, Calibri, Times New Roman – and verbatim keywords from the job posting. This doesn’t just work for iCIMS, but for Workday, Oracle, and others. And if you can’t find the ATS yourself, Jobscan – for a fee – can do that with simple steps and give you optimization tips as well.
Is that something you needed to know? Absolutely, yes.
Will it make job searching easier for you? A resounding no.
Being a minimalist, I thought since my résumé has the basics, what more would an employer need? But I was mistaken. It’s the ATS I need to get past to reach the actual human. I hope you think of this new information as intel, not extra work.
But sometimes job candidates take it too far.
I recently read about how Amazon is banning job seekers from using AI tools during interviews. Given the volume of candidates it receives, the company issued a set of guidelines to its internal recruiters to create ‘a fair and transparent recruitment process.’ Unless explicitly permitted, applicants may be disqualified if they used AI tools during the interview.
The problem became so widespread that Amazon shared tips on common signs the applicant is using an AI tool. Sometimes candidates sound as if they are reading instead of speaking naturally, even correcting themselves when they misread a word. Their eyes may follow text or drift away rather than focusing on the conversation. They might give confident answers that don’t directly address the question, or appear distracted and confused when reacting to AI‑generated outputs that don’t make sense.
And honestly? That’s only fair. You already have a foot in the door – showing your true self and answering naturally is the right way to go.
That said, and as a fellow traveler down the same road, I completely understand. Each step – learning about the job, tailoring your résumé, waiting for a response, and finally getting the ‘we invite you for an interview’ email – all that takes its toll, and you want the edge, any edge to ace that interview.
But if you’re considering using AI at that specific stage, think about it. What if it’s just a chat to tell you about the company and field your questions? What if it’s a work test? Who’ll be doing the actual work if not you?
What if the company uses a platform like HireVue? Instead of a standard interview, candidates complete short game-based tasks designed to measure things like pattern recognition, working memory, and problem-solving. You could be tasked with performing actual job scenarios to test whether you can do the work, not just talk about it. What then?
The Human Cost
When hiring systems shut out qualified people, the economic consequences go far beyond the individual. Historically marginalized communities, including women and people with ethnic backgrounds, end up missing out on jobs – meaning fewer chances to grow in a career, build stability, or move toward long‑term security.
Over time, this kind of exclusion widens existing wealth gaps and keeps certain groups stuck in cycles of underemployment and limited opportunity. And because well‑paid jobs often come with benefits like health insurance and retirement plans, being pushed out of those deepens inequalities even further.
Disclaimer: The following might be sensitive for some. If you find that it resonates with you to the point of disrupting your daily life, please, seek professional help.
Taking the consequences a step inward to what it does to the person on the receiving end – a psychological phenomenon called ‘job rejection fatigue.’ It’s the ‘emotional and mental exhaustion that builds up from receiving repeated job rejections over time.’ This doesn’t happen from one email – it’s the compound effect of several disappointments. It affects not just your confidence but also your health and social relations.
But how does it compare to the stress that naturally comes from job searching? The simple answer: they’re two different beasts. When you’re looking for a job, you’re evaluating everything – from the role itself, to how many applicants, to your fit, to the company, etc. The uncertainty of the process causes stress. Job rejection fatigue is very specific to those rejection emails. Every single one piles it on till you reach a point when you fear opening the email to read the verdict.
The common advice you hear in this situation is ‘don’t take it personally.’ It doesn’t work and here’s why. The phenomenon is deeply connected to evolution: when the early human was rejected from the tribe/group/clan, it meant potential death. Those who took it ‘seriously’ survived, and you’re their descendant. So, it’s only an instinctive response – feeling the weight of it the way you do.
Here are the common signs to watch out for:
Emotional Symptoms: decreased motivation to apply, anxiety before opening emails, doubting your qualifications or career choices.
Behavioural Changes: reduced application quality, delaying search tasks, avoiding networking events, or withdrawing from social gatherings.
Physical Symptoms: changes in sleep patterns or in appetite, increased headaches, muscle tension, or fatigue even after getting rest.
If ignored, it might affect your life in the long run:
Relationship Strain: feeling irritable, withdrawn, volatile, or negative about your prospects can affect the people in your circle.
Career Stagnation: ‘settling’ for a role that doesn’t align with your qualifications or goals, and accepting terms without negotiating for salary or benefits.
Long-Term Confidence Issues: continued anxiety even after landing a job, imposter syndrome – doubting your abilities even when you’ve clearly earned your place – not pursuing better opportunities, nor building connections that might lead to better prospects.
If caught early, job rejection fatigue is manageable, here’s how:
Start with your mindset: think of every ‘no’ not as a judgment on who you are, but as a sign that it was the wrong fit.
Change your tactics: instead of exhausting yourself with many applications, focus on a few thoughtful ones. This way you stay connected to your intentions and goals.
Practice mindfulness: have small rituals to process disappointment – take a walk or do breathing exercises, just set aside some time for your body to recover.
Take stock of how far you’ve come: be it skills, clarity, or resilience – you haven’t been standing still. It’s just a dry spell not a failure.
You need to always remind yourself that there’s more to you than a job seeker – you have your routines, your people, your hobbies, the parts of your life that shine despite someone else’s decision. Through all of it, give yourself grace; so much of hiring is outside your control, and the effort you’re making is already evidence of what you’re made of.
Fighting Back
One of the most famous cases is Derek Mobley v. Workday, Inc.
In 2023, Mobley sued Workday alleging its AI tools discriminated against him based on race, age, and disability. The case was first dismissed because Workday was classified as an employment agency, but was later accepted in 2024 after a federal judge ruled that the company had a role in the decision‑making process by using workforce data from its customers’ companies to train its AI without accounting for the bias already present in that data.
In May 2025, a California district judge certified the case as a collective action suit – meaning anyone affected by the platform’s AI decisions could join. By July 2025, the case expanded to include individuals affected by HiredScore, an AI tool used by Workday customers to score, sort, rank, and screen applicants. The court also ordered the company to produce a list of their customers.
A more recent case was filed in California by job applicants Erin Kistler and Sruti Bhaumik against Eightfold – an AI-hiring platform – in January 2026. The platform works by assessing applicants and predicting whether they’d be a ‘good fit’ for a job based on résumé and job listing data.
According to the lawsuit, Eightfold builds profiles on job seekers that go beyond listing their qualifications. They assign personality labels, such as ‘team player’, rank the quality of their education, and predict where their career is headed. It’s accused of doing so by collecting this data without the applicants’ knowledge, consent, or the chance to correct any mistakes.
Kistler and Bhaumik use an already existing law – the Fair Credit Reporting Act (FCRA). They argue that Eightfold’s profiles function just like credit reports: sensitive data, collected and used to make decisions about people’s futures. FCRA makes sure that sensitive information collected by credit bureaus and similar agencies is only shared with people who have a legitimate reason to see it. It also requires companies to investigate disputes and to tell consumers if a credit, insurance, or job decision goes against them because of what’s in their report.
At the time of writing, a ruling hasn’t been issued for either case.
For many, lawsuits are a last resort – who wants the stress, the hassle, and the cost of a long litigation process? But sometimes it’s the only way to get any justice at all.
If successful, those involved could get some closure. More importantly, job hiring platforms would be pushed to put their house in order – auditing their tools and cleaning up biased training data before it hurts someone’s future.
‘…, There’s a Way’
In 2021, Dr. Sandra Wachter, professor of technology and regulation at the University of Oxford’s Internet Institute, developed a bias test – the Conditional Demographic Disparity (CDD). It’s designed to reveal when one group is rejected more often than it is accepted.
Here’s what it would look like in hiring: we have equal numbers of men and women applying for the same job. Now think of their applications falling into two piles – the rejected pile and the hired pile. If women keep landing in the rejected pile more than the hired pile, while the opposite happens to men, something is clearly wrong. That imbalance is what the test flags as demographic disparity. The group losing out is marked as ‘disfavored,’ while the group benefiting is marked as ‘favored.’ The data simply speaks for itself.
A strong example of how effective this test can be comes from a 2024 audit in the Netherlands. Investigators found that the Education Executive Agency (DUO) was unfairly flagging students with non‑European migration backgrounds for extra checks, leading to indirect discrimination. The issue was serious enough that it was sent to the Dutch Parliament, and the minister for Education, Culture and Science issued a formal apology once the findings were published.
Wachter’s CDD has already proven it works. The real question is why it hasn’t been widely adopted – it’s in companies’ best interest to fill positions with suitable candidates the first time around, without waiting for a lawsuit to force the issue.
Another solution comes from a study where participants were given AI-generated résumés with White, Asian, Black, or Latino-sounding names or other indicators of race, and asked to recommend who should be invited for an interview.
The results show that when people made hiring choices alongside AI recommendations, they often mirrored the system’s biases. With fair suggestions, participants chose fairly – but when the AI displayed moderate bias, people followed it almost completely. Even under severe bias, they still followed the AI’s lead about 90% of the time.
The researchers concluded that human decision-makers tend to trust AI guidance unless the bias is very obvious, and propose a solution: the implicit association test – a psychological tool used to reveal hidden or subconscious biases people may not realize they have. According to the study, biased decisions dropped by 13% after taking it. They recommended adding such training alongside educating recruiters about AI’s limits as a way to reduce hiring bias.
There are also recommendations for companies to better streamline hiring without compromising transparency or efficiency. The frustration of job seekers who spend six to twelve months looking might make some turn to litigation, which could be incentive enough for hiring teams to take staff training for better practices more seriously.
Companies should optimize their tools for fairness and not just efficiency by reevaluating historical data the AIs are trained on – it might be perpetuating bias. The assessment and predictive tools meant to test the applicant’s ‘cultural fit’ with a prospective employer might discriminate against certain demographic groups.
With the EU AI Act and California’s SB 53 (Transparency in Frontier AI Act) treating the use of AI in hiring as a ‘high‑risk’ activity, companies must now exercise stricter oversight throughout the process and meet compliance requirements – otherwise they risk facing severe legal consequences.
The bottom line for companies: audit current screening processes, check whether AI is independently making decisions, and ensure each step is fair and job-related. The goal is a system that catches and mitigates inevitable human mistakes – perfect hiring doesn’t exist.
When I read about these methods and recommendations for the first time, I put myself in the shoes of employers and thought implementing them might be difficult, expensive, or time-consuming. But then again, do companies really thrive on never-ending hiring rounds, turning away the best person for the job, negative reviews, or liability lawsuits?
If not …
Your move.
What’s ahead
Some problems just don’t have an easy fix – AI-powered ATS is shaping up to be one of them. The signs are there, the effects are becoming more visible every day, and the consequences are easy to predict. ‘Where there’s a will, there’s a way.’ It’s the will that is missing.
In some cultures including my own, work is not just about having an income – it’s tied to the self-respect and social standing that come from an honest day’s work. Being unemployed and actively looking should not be an extra burden on top of the demand of making ends meet. It cannot turn into a game of charades: I’ll pretend to apply to a human, a human will pretend to hire, and AI is right in the middle making all the decisions.
How about making life easier for everyone by dropping all pretenses. No one is buying it anymore. All those webinars, podcasts, articles, and advice might be helpful if the system were open and its decisions understood. Not knowing why you were rejected is frustrating enough. Getting a rejection email that lists possible reasons and you pick which one applies – that’s not transparency, that’s busywork.
Looking for an edge in the job market should come from your qualifications, potential, talents, skills, and experience, not from fixing a résumé with the right fonts and keywords so an AI can read it – that’s not building a career, that’s choosing wallpaper.
Let it stop with us.
Make the next person sending out applications feel that they’ll be evaluated for their achievements. That the process is fair and the decisions explainable. That the barriers are not invisible. That the system actually works.
