Over the last few months, I’ve had hundreds of conversations with recruiters, talent acquisition leaders, hiring managers, and engineers. But for job seekers, one question keeps coming up over and over again:
“How do I beat the AI screening tools so a real human sees my resume?”
The funny part?
Alot of companies still aren’t using AI in the way people think they are.
Less than 10% of the TA teams I personally speak with each month (roughly 100 people/month) are using AI in any meaningful way inside sourcing or inbound application review. Even the companies experimenting with it tend to have fragmented workflows, disconnected tooling, or inconsistent adoption across teams (even if they’re using off the shelf products).
I recently saw a news article about Robert Half using AI in recruiting. The headline made it sound like recruiting had become fully automated overnight with the tool the teams have access to. But large staffing firms don’t operate like one giant machine. Different offices, recruiters, delivery teams, and managers all run different workflows. And RH is no different.
So instead of reading another generic think piece about “AI not replacing recruiters,” I wanted to talk to someone actually doing the work.
Meet Evan
Evan is celebrating his 10-year anniversary at RH, which means he’s spent nearly a third of his life inside staffing workflows.
He currently leads TDC East (Talent Delivery Center East), a national recruiting model that supports high-skill technical hiring across the eastern region of the company. Every difficult technical req that branches can’t fill crosses his desk.
His world is ERP systems, AI, software engineering, data engineering, warehouse management systems, Salesforce, SAP, and other highly specialized technical hiring.
The structure itself is interesting because it tells you a lot about modern recruiting reality:
- Staffing companies are different. For example, his branch offices handle the broader recruiting volume for RH
- TDC exists for hard-to-fill technical roles
- Internal recruiters compete against each other for speed
- Pipeline matters more than job postings
- Relationships matter more than automation
Evan manages one recruiter directly right now, and one of the more interesting parts of our conversation had nothing to do with AI. He talked about watching someone come into recruiting uncertain and slowly develop confidence, relationships, and a real book of business over time.
That human development piece kept surfacing throughout the convo.
The Reality of AI in Recruiting
Here’s the important distinction:
AI exists in staffing. It just isn’t replacing human recruiting the way people online say.
On Evan’s team, AI is primarily used for fraud detection and identity verification.
The internal tooling cross-references resumes against fake profiles, validates LinkedIn identities, and helps catch fabricated experience or credential mismatches.
That makes sense.
Because if you ask recruiters what’s actually broken right now, most of them won’t say:
“We need AI to reject more resumes.”
They’ll say:
“We’re drowning in noise.”
And the numbers are honestly wild.
The 500 Applicant Problem
Evan gave me an example of a security engineering role:
- W2 only
- On-site in Charlotte
- Specialized technical requirements
Twenty minutes after posting the role, there were already 55 applicants.
A week later?
Over 500.
According to Evan, roughly 90% were immediate disqualifies.
Not “close but not perfect.”
Immediate disqualifies.
Meaning business analysts applying to security engineering jobs. Help desk candidates applying to software engineering roles. People applying with completely unrelated backgrounds just because the posting existed.
This is a part candidates don’t see:
Recruiters are not sitting there carefully studying 500 resumes one-by-one or using a fine tuned advanced AI ranking systems.
Most are trying to survive the current volume.
The real problem isn’t just some hyper-intelligent algorithm rejecting qualified engineers.
It’s signal-to-noise collapse and that recruiters have always worked their desk differently.
The Workflow Is Faster Than People Realize
One of the biggest things I learned from this conversation is how quickly recruiting workflows move internally (esp for staffing firms).
The application review process often depends on a set of variables like: client need and timeline, the current teams pipeline and what other priorities are on the plate. The process is agile. That said, when a new req lands on Evan’s desk, his process typically looks like this:
- Check existing pipeline
- Reach out to known candidates
- Leverage LinkedIn Recruiter
- Start interviews and submissions
- Post publicly if needed
The public job posting is often the last step, not the first.
That changes how candidates should think about applying and general partnerships within outsourced staffing.
Because by the time a posting has been sitting for two weeks collecting 500 applicants, there’s a decent chance recruiters are already deep into interviews from pipeline outreach.
Quality is king but speed often matters. This is partly why I suggest finding solid recruiting contacts in your skillset and proactively reaching out. Get yourself on their radar and into their network before a req is even open online.
LinkedIn Messages: Why Most Get Ignored
We also talked about LinkedIn outreach fatigue.
Evan said most recruiter inboxes are filled with generic messages:
“Hi Evan, my name is X. I have a background in Y and looking for Z. Can you help?”
No job reference. No other context. Just a big blob of text.
The messages that actually stand out are specific.
For example, something as simple as a candidate including a req number immediately stands out and gives him something actionable to look up.
That sounds small, but operationally it changes everything.
Recruiters work in queues, systems, req loads, and urgency. Specificity reduces friction.
Another interesting point: Evan maintains ongoing email chains with candidates he trusts. Even if someone isn’t right for a current role, they may become relevant months later.
A reminder that good recruiters build talent ecosystems, not just placements.
Why Most Technical Resumes Blend Together
This part honestly mirrored a lot of what I’ve been seeing while building my resume review tool and reviewing QE resumes recently.
The two biggest resume issues Evan sees:
1. No customization
Candidates blast the same generic resume everywhere.
Evan said he’ll still take calls on generic resumes, but before submission he often asks candidates to make updates.
Not because recruiters enjoy resume formatting exercises.
Because customization is often helpful in getting that introduction brokered with the hiring team.
A customized resume tells the manager:
“I understand what this role is and this is why I’m a fit.”
2. No business context
This was probably the strongest point in the entire interview.
Technical resumes often list tools without explaining:
- what product was built
- what business problem existed
- what industry it supported
- why the work mattered
A QA engineer who worked on a fintech platform should say that.
Someone automating healthcare claims testing should say that.
Someone supporting warehouse management systems should say that.
Recruiters often pattern-match against client problems.
“Selenium” matters.
But “Selenium automation for a fintech payment platform handling transaction validation” matters a lot more.
The “Beat the AI” Conversation Is Bigger Than Reality
I told Evan about a conference call I recently joined where engineers were actively trying to learn how to “beat AI screening systems.”
And to be fair, that fear didn’t come from nowhere.
Candidates are starting to encounter AI powered/automated workflows.
But the reality on the ground is much more fragmented than social media makes it sound.
Most recruiters I speak with are still reviewing resumes manually.
Most staffing workflows are still heavily relationship-driven.
Most teams still rely on human judgment for soft skills, communication, and “fit.”
And when teams do use AI for resume filtering, the tools are configured in all types of ways (sometimes just based off the J/D and intake notes from management and other times it weighs your resume against prior successful placements, etc).
Until things standardize, it’s important to place less focus on “how to beat a screening tool” and more on “how do I create a resume that tells a compelling story and stands out against my peers”.
What Candidates Should Actually Do
After this conversation, my takeaways became pretty simple.
Apply early
If you’re genuinely qualified, speed matters.
Be proactive in outreach.
Recruiters start working pipelines immediately.
Use specificity in outreach
Include req numbers/reference actual roles when live.
Make it easy for recruiters to take action.
Add business context to technical resumes
Don’t just list tools.
Explain:
- what you built
- what problem it solved
- what industry it supported
Be explicit with technical stacks
If you know Selenium, Cypress, SAP modules, AWS, Playwright, Salesforce, or niche frameworks, spell it out clearly in the meat of the resume.
Stop assuming every rejection is AI
Sometimes it is.
A lot of times it isn’t.
Sometimes the recruiter already filled the shortlist from pipeline before the application volume exploded.
Sometimes the role was poorly written (this happens often).
Sometimes the workflow itself is broken.
And sometimes 500 people applied to the same job in two weeks.
Shoutout to Evan Ball for taking the time to connect. A lot of people online talk about recruiting theory. Evan actually lives inside the workflow every day, and the operational context here was incredibly valuable. Appreciate the transparency.