How AI Is Changing Recruiting: Insights From the CEOs Building in the Space
Artificial intelligence is already changing how hiring teams source candidates, structure interviews, communicate with applicants, and handle an increasingly overwhelming workload.
What stood out most across these podcast conversations is that the leaders building recruiting technology are not describing AI as a replacement for recruiters. They are describing it as the layer that makes modern recruiting operations possible at scale. And that matters, because recruiting teams are being asked to do more with less.
The reality is that hiring has gotten heavier. There are more applicants, more interviews, more steps, more tools, and more pressure to make every hire count. AI is stepping into that environment not as a gimmick, but as infrastructure.
Across these conversations with Ben Sesser of BrightHire, Nikos Moraitakis of Workable, Anil Dharni of Sense, David Paffenholz of Juicebox, and Steve Bartel of Gem, a few patterns became very clear: AI is reducing recruiter workload, improving process quality, increasing precision, and helping teams move faster without losing control of the hiring process.
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Steve Bartel: Recruiting got harder before AI got better
If you want the clearest explanation for why AI matters in recruiting right now, Steve Bartel gives it.
Recruiting teams are not operating in a lighter environment. They are operating in a more demanding one. Teams are smaller. Reqs are heavier. Hiring managers are pickier. And candidate volume has exploded.
As Bartel explains:
“Applications are up 3x compared to three years ago… Teams are smaller, and what that means is each recruiter is managing up to 55% more recs on average… It’s taking eight more days to hire on average versus three years ago, and it’s taking 42% more interviews per hire compared to three.”
The result is simple: recruiters are doing far more work per role than they were just a few years ago. More applicants, more interviews, and longer hiring cycles mean the operational load on recruiting teams has increased dramatically.
That is the operating backdrop. When companies talk about adopting AI in recruiting, this is the context they should start from. Hiring teams are not deploying AI because it sounds exciting. They are deploying it because the job itself has become heavier.
And Bartel is clear about where AI fits into that reality:
“I think more generally, though, AI is making folks more efficient. And so the place I see it playing out more often than replacing jobs is as we ramp back up hiring, can we get more out of the team we have, the recruiting team we have, by deploying AI just to make everybody more efficient?”
That is the frame: not replacement, but efficiency.
Nikos Moraitakis: AI Is Already Embedded in Recruiting Workflows
If Steve Bartel explains why recruiting teams need AI, Nikos Moraitakis shows what it actually looks like when it’s deployed inside a hiring platform.
As Founder and CEO of Workable, Moraitakis has been aggressively embedding AI across the recruiting workflow. Instead of treating AI as a feature sitting on the edge of the product, Workable has integrated it directly into how hiring teams create jobs, source candidates, and run hiring processes.
The adoption numbers alone are a signal that AI is already moving from experimentation to everyday recruiting infrastructure.
As Moraitakis explains:
“Today in Workable, we’ll write your job descriptions with AI, and already 30% of the jobs created in Workable today are written by AI… We publish 700,000 jobs a year through our customers and a third of them are already done with AI.”
That level of usage shows how quickly AI is becoming embedded in core recruiting workflows. But the impact goes well beyond job descriptions.
Moraitakis describes how AI is increasingly functioning as an operational assistant for recruiting teams, helping them move faster across multiple parts of the hiring process.
“It creates interview kits, it creates scorecards, it creates video interviews for every job. It functions like a junior sourcer and it’s going to go and find you 50 passive candidates… It’s going to write the emails… Those emails have twice the response rate of personalized emails from recruiters and almost four times the response rate of templates.”
Instead of recruiters spending hours drafting outreach, building interview kits, or searching for candidates, AI systems are beginning to handle much of the operational setup work.
That shift is what makes AI so powerful in recruiting. It doesn’t replace the judgment recruiters bring to hiring decisions, but it dramatically reduces the manual work surrounding those decisions.
Moraitakis summarizes the impact simply:
“It’s clear to us that this is going to be a productivity bomb.”
Across sourcing, outreach, and hiring preparation, AI is increasingly acting as a multiplier for recruiting teams, allowing them to handle more roles, more candidates, and more complexity without dramatically increasing team size.
Ben Sesser: AI Is Turning Interviews Into Usable Hiring Data
If Nikos Moraitakis shows how AI is being embedded across recruiting workflows, Ben Sesser focuses on a very specific and historically messy part of the hiring process: interviews.
Interviews generate some of the most important information about a candidate, but that information has traditionally been poorly captured. Notes are inconsistent, interviewers forget details, and hiring teams often rely on fragmented feedback when making decisions.
Sesser built BrightHire to solve that problem by using AI to capture and structure interview conversations.
As he explains:
“Think of BrightHire… truly like an AI co-pilot for your recruiting team and your hiring teams to basically run the interview process much more effectively every day.”
The platform records interview conversations and turns them into structured information that hiring teams can review and share across the organization.
“We record, we transcribe and we summarize the conversations so, as a candidate is sharing really important information with you throughout the process, how do we not lose any of that?”
This shift matters because it turns interviews from a memory-based process into something far more structured and reviewable. Instead of relying solely on notes and individual interpretation, hiring teams can work from a more complete record of what candidates actually said during the process.
But Sesser is also realistic about the challenges of building reliable AI products in hiring. While modern AI models can generate impressive results quickly, building systems that companies trust for critical hiring workflows requires a much higher level of precision.
As he puts it:
“You can get 80% of the way there like that. But that last 20%, that’s where all the work is, because we have a really high standard around accuracy and recall and precision… because hiring is so important we can’t screw it up.”
BrightHire’s goal is not to have AI decide who should get hired. Instead, the system is designed to give hiring teams better information so they can make stronger decisions.
Sesser explains that distinction clearly:
“We’re not stack ranking… and applying AI to make those decisions. What we’re doing is we’re applying AI to give you vastly better information that’s more objective and complete.”
In other words, AI isn’t replacing the judgment of recruiters or hiring managers. It’s making the information those decisions depend on far more reliable.
David Paffenholz: AI Is Reducing the Manual Work of Sourcing
David Paffenholz is focused on another major workload problem in recruiting: sourcing.
Anyone who has done serious sourcing knows how much time disappears into search syntax, filtering, reviewing profiles, and trying to turn all of that into a shortlist that is actually worth engaging. A large portion of the job is spent navigating databases rather than building relationships with candidates.
Juicebox is built around reducing that manual drag.
Paffenholz describes the platform simply:
“We’re an AI-powered talent sourcing platform.”
But the goal is not just to make searching easier. It is to automate large portions of the sourcing workflow so recruiters can move more quickly from search to candidate engagement.
As he explains:
“We help go from initial search setup, reviewing and assessing profiles to engaging them, typically via email sequences… ultimately, we think there’s a lot of potential to go more in-depth, to do better sourcing than what is possible right now by using that technology.”
Instead of recruiters manually reviewing hundreds of profiles and trying to infer fit themselves, AI systems can evaluate candidate data and surface stronger matches automatically.
That shift fundamentally changes the nature of sourcing work. Instead of spending hours navigating search filters and reviewing profiles, recruiters can focus more on engaging the right candidates.
Paffenholz captures the difference clearly:
“Existing solutions maximize for the amount of time spent on the platform. We try to automate as much of that time as possible so you get straight to the results rather than having to spend that time on the platform.”
In other words, the goal is not to give recruiters more search tools. The goal is to eliminate as much of the manual sourcing work as possible so recruiters can spend their time where it actually creates value.
Anil Dharni: AI is fixing the broken middle of the hiring process
Anil Dharni’s view is especially useful because he is focused on the operational handoffs that make recruiting slow and messy.
Recruiting does not usually break because nobody can post a job. It breaks because candidates need follow-up, recruiters need nudges, hiring managers need reminders, interviews need rescheduling, and no one owns the friction between the steps.
Dharni explains Sense’s focus like this:
“We are focused on talent engagement… how are large companies with complex hiring needs basically simplifying their hiring needs and figuring out how to use automation and AI technology to basically create a great candidate experience and move candidates faster through the funnel?”
That last part matters: move candidates faster through the funnel.
He also makes clear that his team is thinking across the full lifecycle:
“All the way, the entire talent lifecycle automation is why people come to us… all the way from AI chatbots to voice, AI for screening sort of use cases, two-way text messaging, mass text messaging for recruiters…”
And then he identifies where the real friction lives:
“Whether it’s assessments, whether it’s background checks, whether it’s interviews, it’s all about the follow-up… it’s about nudging the recruiter, it’s about nudging the candidate, it’s about nudging the hiring manager… and that’s where these processes are broken.”
That is exactly right. Recruiting teams often do not lose time in one giant obvious failure. They lose it in dozens of small, repetitive, follow-up-dependent tasks.
Dharni also points to where LLMs are changing the product layer itself, especially in screening:
“The goal-based world is turning out to be super exciting for us and there are so many use cases for us to unlock.”
That matters because it signals the shift from rigid automation to conversational automation. Instead of rule trees that break the moment a candidate asks something unexpected, AI systems can hold onto the goal of the workflow while still supporting more natural interaction.
That is a better recruiting experience and a better operational model.
James Mackey: AI Is Transforming the Earliest Stage of Hiring
While many recruiting platforms are applying AI to sourcing, engagement, and interviews, another major opportunity sits at the very beginning of the hiring process: screening.
For most recruiting teams, early-stage screening is one of the most time-consuming parts of hiring. Recruiters often spend hours reviewing resumes and scheduling introductory calls just to determine whether a candidate meets the basic criteria for a role.
James Mackey, founder of SecureVision and host of the podcast, is building a platform designed to automate that stage of the hiring process.
His product, June, is an AI interviewing and screening platform that allows companies to evaluate candidates immediately after they apply.
Instead of waiting days for a recruiter to schedule a screening call, candidates can complete an AI-driven interview that gathers structured information about their background, experience, and fit for the role.
The system conducts a dynamic interview, asks follow-up questions, and captures responses in a structured format that hiring teams can review before deciding which candidates should move forward.
The goal is not to remove recruiters from the process. It is to eliminate the bottleneck that often forms at the very beginning of hiring.
By automating the initial screening stage, platforms like June allow recruiters to spend more time engaging with the most promising candidates rather than sorting through large volumes of applicants.
In many ways, this approach reflects the broader pattern seen across the recruiting technology ecosystem. AI is not replacing the human side of recruiting. It is handling the operational work that slows hiring teams down.
As recruiting continues to evolve, platforms like June represent the next step in that shift: bringing AI directly into the interview process itself.
Taken together, these conversations point to a broader shift in how hiring is evolving. Recruiting hasn’t become more complex because of AI, it became more complex long before AI arrived. Teams are managing more applicants, more interviews, more tools, and more operational coordination than ever before. What AI is changing is how much of that operational weight humans have to carry.
Across sourcing, candidate engagement, interview analysis, workflow coordination, and screening, AI is increasingly taking on the manual work that has historically slowed hiring teams down. The result isn’t the replacement of recruiters, but a restructuring of the recruiting process itself, one where technology handles the repetitive operational layer while humans focus on judgment, relationships, and decision-making. As the leaders building these systems make clear, the future of recruiting isn’t AI versus recruiters. It’s AI becoming the infrastructure that allows recruiting teams to operate at a completely different scale.