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We’ve all been there; you’re actively (or passively) on the search for new job opportunities and while your network has proven useful, everyone still needs to send in multiple cold applications to job postings. You click on the apply button and up pops a series of grueling sign-on steps, redundant questions, clunky ‘copy/paste’ workflows and everything in between. All of these items (both important and very much not important) are driven by the applicant tracking system (ATS) the organization is using to post, collect and administer their recruiting process.
If you’ve always thought that the first barrier to getting an interview is based solely on the judgement of internal HR, internal recruiting, or an external source, think again! Resume filtering (due to volume and lack of resources) has become a growing trend within companies large and small. They use these tools to filter out resumes before an actual human even reviews it. You read that right; we can all but guarantee that a few of those 30-40 minute applications that you filled out were never even reviewed by a recruiter.
So what do you know about ATS’s? If the answer is ‘not much’ other than they are incredibly annoying (they are) then you’re missing out on a few opportunities to get noticed. In this article, we’ll talk about what an ATS is, how they work, and most importantly, how to use them to your advantage.
1. What are Applicant Tracking Systems?
Put simply, ATS’s are software packages that provide recruiting and hiring tools for company HR and Recruiting teams. They perform a variety of tasks but at the heart of it all, they advertise jobs as well as collect and sort thousands of resumes from those marketing efforts.
When you apply to a job online, your resume in almost all instances won’t get filtered directly to a recruiter or hiring manager’s inbox. Rather, it’s first being algorithmically processed by the ATS the organization has chosen. The chances an actual human ever sees or reviews your resume could depend solely on how well your resume or cover letter is optimized to the formula. I know, it’s disheartening. So why do companies use these systems?
The short answer? Volume. Even the smallest companies receive hundreds of resumes for market center job postings. Applying for a job has become ‘easier’ than ever (albeit no less frustrating) so many applicants send their applications without even reading the job description or figuring it’s all worth an attempt to get lucky. That puts overwhelming pressure on the review process internally. These systems keep applications in one place, helping recruiters and hiring managers stay organized. Most systems have some form of ‘rating’ system that essentially filters keywords in resumes to ‘guide’ the hiring teams to the ‘preferred’ candidates. Let us be clear; this absolutely saves time, and yes, the algorithms work in theory, but top candidates consistently fall through the cracks for a variety of reasons, which we’ll cover later on.
You can’t escape them. Every company has or will utilize an ATS for recruiting. The volume dictates its necessity. So let’s dive into how they actually work.
2. How do Applicant Tracking Systems work?
ATS’s collect and store resumes in a database for recruiters, hiring managers, and HR employees to access. Resumes could be stored for a really long time after the original application, and internal recruiters can search the database in the future for candidates that have applied in the past. In sum, when you apply to a job at XYZ Company, you’re giving them access to your resume and contact information for not only the job you’ve applied for, but also future opportunities (which is inherently a good thing).
Recruiters view resumes in a variety of ways. Some choose to view applicants in a short form process (think quick hits on achievements, education, titles and tenure). Within 6 seconds, recruiters could make a determination on your application using ONLY these quick hits. We’ll talk about how to make these skills jump out later on.
ATS’s, for the most part, incorporate some version of a ‘ranking system’ to highlight desirable applications (based on an algorithm). It’s typically a percentage score (82% match) or a grading system (A-F). Our worry, and unfortunately it’s been well founded, is that recruiters are leaning too heavily on this automation to select candidates to speak with. That being said, it’s the reality of the software so we’ll talk about how to increase your score in section 3.
Recruiters also have the luxury of using keyword searches to filter resumes in an ATS. For example, there are many versions of the job “Project Manager”. A recruiter might be looking for a project manager that has experience driving initiatives in IT procurement specifically. As such, they will not only search “project manager” but they will also search words like ‘procurement, infrastructure, machines, networking, laptop{s}, etc). Keep this tactic in mind when writing your resume; if you can predict what keywords we’ll use to find you, odds are we will!
3. So, how do you beat these things?
Unfortunately, it’s hard to answer this without saying you have to get creative as possible. Getting past an ATS and landing an actual interview come down to a well-crafted resume and cover letter that is targeted to mimic the keywords that are listed in the job description. The algorithm’s will love the customization and increase your chances of catching the eye of both our human and AI overlords.
As standard practice, we recommend the following advice to making your application jump out of the pack:
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Tailor your resume to each job you apply to. Use verbiage that is used in the description in your resume. They say, imitation is the best form of flattery!
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Use long AND short form (MBA & “Master of Business Administration) or (PM & Project Manager)
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Absolutely use chronological order when formatting your resume. ATS’s dislike any other format
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Try your best to keep the formatting as text only to avoid parsing issues
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Use a normal font. Ornate or expressive fonts also don’t parse well
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Use standardized section language like {Work Experience}, {Summary}, {Education} as opposed to getting cute with things like {The Journey I’ve Been On…)}
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Save your file as a docx or pdf ONLY
So where do you start with this resume tailoring? We have just the tool for you. Using Careerwell’s Resume Refinery, you can create multiple versions of your resume that are built to beat the algorithm. Take a look here and get started on your newest versions of your resume now.
Using this knowledge and writing toolbox should help land you more conversations with organizations that you’re really interested in. May the odds be ever in your favor!
If you have any questions or would like to chat, feel free to reach me at jeff@hirewell.com.
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Over the last year, hiring teams have started seeing a wave of new job titles pop up across tech, sales, and operations.
Some are legitimate new roles.
Others are existing jobs with a slightly different name.
And many of them have one thing in common: AI is suddenly part of the job description.
From Go-to-Market Engineers to AI Specialists, companies are experimenting with new roles as they figure out how automation and AI fit into their teams.
But most of these positions aren’t entirely new. They’re evolutions of existing roles.
The Rise of Go-to-Market Engineers
One role that is gaining traction is the Go-to-Market Engineer.
Depending on who you ask, it is either:
- A new role focused on automation and AI-driven growth
- A modern version of Revenue Operations (RevOps)
In practice, it is a bit of both.
As Matt Tokarz recently pointed out after closing a search for an Outbound & Go-to-Market Specialist, the role looked very different from traditional RevOps. The focus was not reporting or CRM hygiene. It was building prompts, leveraging tools like Clay and Smartlead, and enabling SDRs and AEs with backend insights to accelerate pipeline growth.
Instead of traditional RevOps work like reporting and CRM management, the focus was on:
- Building AI prompts
- Creating campaign messaging
- Automating outreach workflows
- Using tools like Clay, Smartlead, and Trigify
The goal was not simply managing sales data. It was accelerating pipeline generation through automation.
AI Isn’t Creating New Functions. It’s Changing Existing Ones
One trend is becoming clear. Companies are not replacing entire departments with AI.
Instead, they are changing how existing roles operate.
Sales teams still need pipeline.
Marketing teams still need content.
Engineering teams still need to build software.
The difference is that employers now expect candidates to use AI tools as part of their workflow.
As Zac Colip noted during the discussion, we are currently in a transitional phase where companies are labeling roles with “AI” as they experiment with how the technology fits into teams.
But that may not last forever.
Why Many “AI Job Titles” May Disappear
Right now, AI still feels new enough that companies highlight it in job titles.
But eventually, AI will likely become a baseline expectation, not a specialty.
Think about it like cloud technology or data analytics.
At first, companies hired “cloud specialists.” Now most engineers are expected to understand cloud infrastructure.
The same shift will likely happen with AI.
Instead of hiring “AI-enabled marketers” or “AI engineers,” companies will simply expect employees to know how to work with AI tools.
The Talent Pool Is Still Small
One challenge with these emerging roles is simple: there aren’t many candidates with real experience yet.
Many of these positions didn’t exist two years ago.
In one recent search, we started looking for a candidate locally in Chicago. Eventually we expanded nationwide because the pool of people with relevant experience was extremely limited.
This is a common issue with emerging roles:
- Companies want experienced candidates
- But the roles themselves are still new
That gap will likely persist for the next few years.
Hybrid Roles Are Becoming More Common
Another noticeable shift is that roles are becoming more hybrid.
Instead of hiring for narrow responsibilities, companies are combining multiple functions into one position.
As Matt Mulcahy highlighted, one example is the rise of Forward Deployed Engineers, a model popularized by Palantir.
These engineers:
- Work directly with clients
- Gather product requirements
- Write user stories
- Build the software themselves
What used to involve several roles, including product managers, engineers, and solution architects, can now sometimes be handled by one person. AI development tools are part of what makes this possible.
AI Adoption Is Happening at Different Speeds
Not every industry is moving at the same pace.
As Ashley DuBois pointed out, some sectors, such as transportation, are applying AI to specific workflows like load booking and operational automation.
At the same time, some companies are adding “AI” to job titles even when the core responsibilities remain largely traditional.
In many cases, it is still essentially an IT manager role with AI familiarity layered in.
This reflects a broader transition period where companies want to signal modernization and candidates want to signal relevance.
Automation Is Changing Operations Roles
In logistics, AI is increasingly handling scheduling, tracking, and coordination tasks.
According to Brittany Lasky, operational roles such as logistics coordinators may experience the greatest impact from automation.
However, freight brokers who manage negotiation and strategic RFPs remain in demand.
AI can optimize processes. It does not replace relationship management or strategic negotiation.
Across industries, a pattern is emerging.
Execution becomes automated. Strategy becomes more valuable.
Finance Roles Are Becoming More Analytical
Automation is also reshaping finance and accounting roles.
As Adam Slater noted, accounts receivable jobs that once focused on high-volume manual processing are evolving into more analytical positions centered on reporting and insights.
The work is not disappearing. The expectations are increasing.
Organizations are now hiring for:
- stronger technical skills
- analytical thinking
- systems understanding
Even roles traditionally considered administrative now require deeper technical capability.
Analyst Roles Are Expanding
AI is not eliminating analyst roles. It is expanding them.
Financial analysts are also expected to understand tooling, sourcing, and data transformation.
In many cases, two or three roles are being combined into one.
This raises a long-term question.
If entry-level roles become more complex or disappear entirely, how will organizations develop senior talent in the future?
Sales Development Is Becoming More Human
The traditional model of high-volume cold calling is changing.
According to Jack Smith and Emily Canna, teams are shifting toward:
- personalized LinkedIn videos
- voice memos
- highly tailored messaging
At the same time, companies are moving away from activity-based KPIs and focusing more on outcomes such as demos set and SQLs generated.
In a market saturated with automated outreach, authentic communication has become a competitive advantage.
Several clients have said it directly. They want a human in the seat.
Go-to-Market Teams Are Rebuilding the Fundamentals
Every six to twelve months, hiring trends in go-to-market teams shift.
As Jennifer Salerno noted, companies move through cycles.
One quarter it is BDRs.
Then RevOps.
Now it is go-to-market engineers.
Many companies experimented heavily with AI to accelerate pipeline generation.
What those experiments exposed were structural gaps, particularly in outbound strategy.
AI can support execution. It does not replace a well-built top-of-funnel engine.
Inbound momentum can hide weaknesses. Outbound forces clarity.
The companies gaining traction right now are not chasing trends. They are rebuilding the fundamentals of their go-to-market strategy.
What This Means for Hiring
For employers, the takeaway is straightforward. Job descriptions and expectations need to evolve alongside technology.
Across functions, we are seeing the same shift play out. AI is not eliminating entire roles. It is changing how those roles operate and increasing the baseline skill set required to perform them well.
Hiring managers should start thinking less about traditional titles and more about capabilities. That often means prioritizing candidates who can:
- Learn and adapt to new tools quickly
- Think in terms of systems, automation, and workflows
- Combine technical and business skills within a single role
In many cases, the perfect candidate with the exact title simply does not exist yet. The strongest hires are often people who have developed adjacent skills and shown the ability to adapt as the tools evolve.
The Bigger Trend
The broader trend is that AI is accelerating a shift that was already underway.
Roles are becoming more hybrid. Expectations are increasing across nearly every function. And repetitive tasks are being automated, leaving more strategic work behind.
Sales teams still need pipeline.
Operations teams still need coordination.
Finance teams still need reporting and analysis.
Engineering teams still need to build software.
What is changing is how the work gets done and what skills are required to do it well.
Right now we are in a transitional phase where companies are still labeling roles with “AI” as they experiment with new workflows and technologies.
Over time, that label may disappear.
AI will simply become part of how work gets done.
And the roles themselves, while evolving, will look more familiar than the titles might suggest.
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