Recruiting used to be *a lot* of instinct, hustle, and handwritten notes. But in 2025, that’s no longer enough.
Hiring today demands a process. One that’s repeatable, consistent - and smart.
And at the heart of it all? AI in hiring.
We’re not just talking about job boards and interviews. We’re talking about:
Enter the 10-80-10 rule.
It’s a framework that’s helping forward-thinking hiring teams make better use of their time - and of AI in hiring.
In this article, we’ll break down:
Let’s start.
You might’ve heard of the 10-80-10 rule in leadership, where the idea is to focus most of your time on the high-performers (top 10%) and low-performers (bottom 10%), while managing the middle 80% with consistency and guidance.
But in the AI context, it looks a little different. Here’s how the principle gets reimagined for hiring:
This rule helps recruiters focus their energy where it counts. You don’t need to do everything… just the parts that require your expertise.
According to LinkedIn’s Future of Recruiting report, 73% of hiring decision-makers say that AI will change how organizations hire. And it’s already happening.
AI doesn’t just save time (by 20% - which is a whole day's work saved) it also brings along other benefits.
And that’s exactly what tools like Quil are doing.
@https://www.youtube.com/watch?v=F1GM4jCstMw
Ryan and Josh share how one agency recruiter used Quil’s # AI recruiting assistant to:
Because when you’re running back-to-back interviews, having the ability to listen again is an absolute game-changer.
So you’re not making decisions on vague gut feelings. You can make decisions based on facts - go back, analyze, and make clarity-based decisions.
Because AI is only as smart as the input you give it. Simple.
It will get smarter in the future, but that’s not happening for a while. Or this year at least (with AI advancing every day - there’s not a certain timeline you can give on things)
Here’s an example one of our agency recruiter teams shared with us:
They used prompts to generate DISC-style personality insights from interviews, then ran the prompts three times.
It delivered the exact same result each time. That made them realize how powerful AI can be when it’s structured well.
So, whether you're creating candidate scorecards, prompting for summaries, or building job descriptions, your templates are your foundation. Your 10% defines the quality of the 80%.
While AI is super powerful, it should not be used for everything. Recruiters get excited, of course. It’s new. It’s efficient. And it feels like magic.
But that excitement can sometimes be detrimental. Because then recruiters start relying entirely on it. And that’s where things go wrong.
One example: using AI to write candidate summaries based on resumes alone (without factoring in actual interview performance).
The summary may look fine, but if it doesn't match how the candidate presented themselves in conversation, it could derail the process later.
Here’s a common scenario:
And that’s the missing 10% at the end - the human touch that needs to be there to elevate what AI creates.
A few simple steps to fix this:
And above all - read the thing.
Let’s look at a few other examples of where AI goes wrong in hiring.
Here are the top time-wasters AI can handle today:
One executive recruiter shared how they often meet a candidate across 3–4 stages: screener, principal, managing director.
Each person leaves notes. Rather than copy/pasting it all into a report, they use AI to combine everyone’s notes into a single candidate brief.
For exec search firms, this is gold.
Another thing we’ve learned when working with hundreds of recruiters over the past 2 years: recruiters often manually rewrite resumes for candidates before submission, to align better with the job.
But that takes forever. So they’re now using AI to:
That’s 80% of the job done in seconds. But remember the final 10%: check for bloated claims, weird phrasing, or off-brand tone.
Because nothing screams “lazy” like a resume that says “synergistically leveraged paradigm shifts.”
There’s always a debate that goes around when to hire in-house or when to work with external recruiting firms. Since Quil is explicitly built for recruiting agency firms, we can share a few insights (view the above podcast for more).
Plus, AI is shifting how both internal and external recruiters operate.
Internal teams are starting to use AI to:
External recruiters… especially those focused on executive search - are using AI to:
In both cases, the goal is the same: to spend more time talking to top candidates (and less time stuck in repetitive, manual tasks.)
And it’s working. According to the 2024 Employ Recruiter Nation Report, over 55% of recruiters now use AI for candidate matching, and 44% use it for intelligent sourcing.
That number is only going up.
Applicant tracking systems were never built for modern hiring workflows. Most are glorified databases.
They weren’t built to:
That’s why Quil is built to integrate with your favorite ATS - fill those gaps, and make your hiring workflow easier.
Here’s your cheat sheet:
First 10%:
Middle 80% (AI zone):
Final 10%:
Let the machines do what they do best.
Then add the finishing touch that only you can bring.
One thing you need to understand is: AI isn’t necessarily driving growth - it’s enabling efficiency.
Because your candidate pipeline doesn’t always have to be full.
You can do more with less.
Instead of adding headcount to handle:
You add AI to handle the load. Then hire only when it makes economic sense (and treat AI as an assistant).
Use it to create cushion space to do what you’re actually great at: strategy, building trust, and making remarkable hires.
In short: use AI as leverage. Use your brain for decisions.
Want to see how recruiters are using Quil to put the 10-80-10 rule into action? Try Quil for Free or Book a Demo to see how it works.
