Get the inside track on how artificial intelligence is transforming recruiting operations as industry leaders tackle the 10 most pressing questions submitted by practitioners.
Most talent acquisition, HR and recruiting operations professionals agree that AI is reshaping our industry. Everything from how we source candidates to how we screen applications, and even how candidates approach their job search, now bears the fingerprint of artificial intelligence. This shift is sparking plenty of discussion and raising just as many questions for those of us working in the field.
Many teams feel the optimism of new possibilities, the pressure to keep up, and the uncertainty that comes with radical changes to how work gets done. That’s why, with the help of Ben Abear, who leads Recruiting Operations at Google Fiber, and Jeremy Lyons, Co-Founder of RecOps Collective, we’re answering the top questions about what AI really means for recruiting operations.
To keep things interesting, we also put these same questions to AI itself (Gemini), just to see how the answers compare!
1. How can RecOps teams get ready for AI, both right now and for the future?
Ben Abear says the first step is making prompt engineering and hands-on use of AI tools part of your regular practice: “Changes are just so rapid that even three months of not participating is going to set you back a ton.” He suggests teams try out AI for real tasks like talent intelligence, intake meetings or summarizing candidate debriefs, and then share what works and can be replicated at scale. Breaking it down into bite-sized pieces gives RecOps teams an opportunity to figure out what their priorities are and the capabilities in the market, and work out what they’re actually ready for versus what still needs time to mature or doesn’t fit their current needs.
Jeremy Lyons suggests RecOps teams lean into their “tendency to be pedantic” and clearly articulate what the organization’s goals are and how you are going to achieve them using technology, human ingenuity, or a mix of both. From there, it’s a case of “being curious” and “encouraging people to break things in a constructive way.” He recommends using free trials and not being afraid to “break something, figure out where you went wrong.”
How Gemini answered: Recruiting Operations teams can prepare for AI by first identifying repetitive, administrative tasks that AI can automate, such as resume screening, interview scheduling, and drafting job descriptions. For the future, they should focus on upskilling their teams in data analysis, ethical AI usage, and strategic relationship-building, while continuously monitoring and adapting to evolving AI tools and regulations to ensure fair and effective hiring practices.
2. What should recruiting operations teams look for in AI tools, and how can you tell what’s real versus just hype?
The first step is to know your own AI strategy before you even look at vendors. As Jeremy Lyons put it, “You have to understand what your AI strategy is first before even evaluating a tool.” If your goal is to add copilots, ignore tools that only pitch full replacement. If you want to replace a process, your criteria will be different.
All our experts recommended taking advantage of free trials. AI companies will let you do free pilots, so ask to see the tool in action before making any decisions. “Or negotiate very short-term contracts,” Lyons adds, so you’re not locked in.
Investing in AI should be about supporting your existing processes, not overhauling them for the sake of technology. Leah Daniels, Chief Commercial Officer here at JobSync, recommends you “Walk through your business and your process and outline where you need AI first and honestly ignore the rest—tackle one thing at a time.” Otherwise, “all you’re going to do is see shiny objects that are really cool,” and your team will burn out testing tools that don’t solve your real needs.
How Gemini answered: Recruiting Operations teams should seek AI tools that offer clear, measurable benefits like reducing time-to-hire, improving candidate quality, and enhancing diversity by mitigating bias. Key features to look for include strong data privacy and security, seamless integration with existing ATS/HRIS, and the ability to automate specific, repetitive tasks while maintaining a positive candidate experience. To distinguish real AI from hype, evaluate tools based on whether they use genuine machine learning (i.e., they learn and adapt over time) rather than just rote automation or simple keyword matching, and prioritize vendors who can provide transparent metrics and case studies for their stated benefits.
3. I have an overwhelming number of AI vendors reaching out to me weekly. How do you figure out which are actually worth your time?
While process analysis and free trials will get you most of the way, be aware that many tools on the market don’t integrate well with existing systems. Focus on solving a real business problem, and test tools in a controlled way so you’re not creating operational silos or introducing downstream issues that could disrupt your workflows.
The experts recommend isolating your testing—try new AI tools on a single role or workflow first, rather than opening up your entire system. Sandboxing lets you see how the tool really performs without risking disruptions across your whole operation. Pay close attention to how data flows and whether the tool actually delivers the value you need.
How Gemini answered: To cut through the noise, prioritize AI vendors who offer clear, data-backed case studies relevant to your specific recruiting challenges, demonstrating quantifiable ROI like reduced time-to-hire or improved candidate quality. Focus on solutions that integrate seamlessly with your existing tech stack and emphasize ethical AI use, transparency in their algorithms, and strong data privacy practices. Ultimately, the best way to determine worth is through pilot programs or demos that allow you to test their claims with your own data and processes.
4. What legal and compliance issues should RecOps teams watch out for when rolling out AI?
Legal and compliance checks are your company’s responsibility, not the vendor’s. The Mobley v Workday lawsuit has exposed the clear risks that flow from using AI to make recruiting decisions, yet few know the nuances of legislation—like the fact that explicit opt-in is needed for AI-driven outreach, such as cold calls or texts, under laws like TCPA. The best advice is to include your legal team in any AI adoption and make sure you know your own responsibilities before you start.
Ben Abear recommends starting every vendor conversation by asking for a full list of sub-processors and understanding how your data is retained and used: “Usually, that weeds out a fair number of vendors.” He points to the growing need for SOC II testing, bias testing, and keeping up with requirements that change state by state and country by country.
All AI products need to comply with regulations like GDPR and state-level privacy laws, both now and in the future. Think about what happens if regulations change—will you be reliant on a tool that is no longer compliant if the law shifts during your contract?
How Gemini answered: Recruiting Operations teams must prioritize legal and compliance issues like preventing algorithmic bias, which can lead to discrimination under equality laws, and ensuring GDPR compliance, especially regarding candidate data privacy and consent for processing. It’s crucial to be transparent with candidates about AI usage, provide human oversight for significant decisions, and conduct thorough Data Protection Impact Assessments (DPIAs) to mitigate risks.
5. How accurate are AI tools in recruiting operations, and where do they fall short?
When Abear asked Gemini for a number, it claimed “from a recruiting perspective, we’re 85% accurate.” Sounds impressive but, in reality, that figure only holds up for simple, single-threaded tasks. “When you start to peel that back and ask it more complex questions combined together, where does it start to unravel, how accurate is the response? We all know that in the hiring space, it’s not straightforward.”
There’s also a risk of overusing AI, especially when it comes to reporting on outcomes or automating too much of the process without enough oversight. When you start putting AI into your reporting, you have the AI reporting on the AI outcomes of the AI. If it reaches this point, you can bet that humans have lost touch with what’s going on.
As much as RecOps teams need to experiment with AI tools, you can’t just trust the output at face value. Train recruiters in prompt engineering and quality control, and make sure they have the confidence to question outputs.
How Gemini answered: AI tools can be highly accurate in automating repetitive tasks like resume screening, scheduling, and initial candidate matching, leading to significant efficiency gains and potentially reducing human bias by focusing on objective data. However, they fall short when it comes to nuanced human elements such as assessing cultural fit, emotional intelligence, and complex problem-solving skills that require genuine human judgment and empathy. Additionally, AI’s accuracy is heavily dependent on the quality and impartiality of the data it’s trained on; biased training data can perpetuate or even amplify existing biases, leading to discriminatory outcomes.
6. What’s the best way to roll out AI when your team has different comfort levels with new tech?
Every team has a mix of people, some eager to jump in and try new tools, others naturally more skittish about AI. RecOps leaders need to make space for everyone and encourage people to experiment. Creating a culture where testing and iteration is safe, and where everyone feels comfortable asking questions and learning as they go, is going to be critical here.
Practical steps, like hosting “lunch and learns” or informal sessions, where the most AI-forward team members can show how they use different tools in real workflows, help bridge the gap between early adopters and those who are more cautious. Consistent training and open conversation ensure everyone has the chance to build confidence and develop the critical thinking skills needed to use AI effectively.
How Gemini answered: To effectively roll out AI with varying comfort levels, start with a pilot program involving early adopters who are enthusiastic about new tech, allowing them to champion the benefits and iron out initial kinks. Crucially, emphasize that AI is a tool to empower recruiters by automating tedious tasks, freeing them for more strategic, human-centric work like relationship-building and complex problem-solving. Provide comprehensive, ongoing training and support in multiple formats, address concerns transparently, and celebrate early successes to build wider trust and adoption across the team.
7. Who owns the output data from AI tools used in recruiting operations?
This is a complex area. While the data in your ATS is clearly yours, once it leaves the ATS and is processed by AI, things get murkier. Data leaving the ATS can be manipulated. You can see the output but, as Abear says, “no one knows the black box of what it did in the middle.”
Ownership of AI-generated outputs is often shared in a co-ownership relationship: “We own it, you own it…that’s where I’ve seen the majority of the conversations land with the legal team,” Abear says. But it really depends on your contracts, the tool’s terms, and how your data is used. If you’re free trialing something, you’re probably not signing anything, which means there may be no clear agreement about who owns the output data. If you create custom Gems in a corporate account and that person leaves, you may not be able to transfer those Gems at all. When in doubt, and there’s always doubt, bring in your legal team to protect your organization’s interests.
How Gemini answered: Generally, the organization using the AI tool in recruiting operations owns the output data. This is typically stipulated in the terms and conditions with the AI vendor. However, it’s crucial to differentiate between the raw data fed into the AI, the AI model itself, and the generated output (e.g., candidate scores, summarized resumes). Legal ownership of AI-generated content can be complex and is still evolving in many jurisdictions, particularly regarding intellectual property, so RecOps teams should clarify these points with vendors and legal counsel.
8. Where are the quick wins for AI in recruiting operations?
Communication is the easiest place to see fast results. “Better emails, better automation, simpler, more consistent, more effective. There’s a lot of information and data out there that you can just program and make a lot cleaner. You’re going to increase conversion rates and consistency in your process,” says Ben Abear.
Simplicity is another big win. “As RecOps, we often try to solve a problem by adding too many things, too many pieces of the puzzle, too many tools,” Jeremy Lyons explains. “The minute you fine tune that, you simplify it. You create the clear north. My advice is to make it as Occam’s Razor as possible.”
How Gemini answered: The quickest wins for AI in recruiting operations lie in automating highly repetitive and administrative tasks that consume significant recruiter time. This includes resume screening and shortlisting based on predefined criteria, automated interview scheduling to eliminate back-and-forth emails, and personalizing candidate outreach and follow-ups at scale. These applications directly reduce time-to-hire, free up recruiters for more strategic work like relationship-building, and can immediately improve candidate experience.
9. How do you keep the “human” in candidate and hiring manager experiences when using AI in recruiting operations?
We all know that AI is great for handling volume and making processes more efficient, but humans need to stay involved in the parts of the process that require empathy and intuition. A thoughtful conversation with a recruiter can provide a better candidate experience than the most advanced algorithm ever could.
But the line between human and machine is shifting. Jeremy Lyons pointed out that as agentic AI gets better at handling early-stage interviews and data collection, the “human” moments may move further downstream. Teams are already using tools that let every candidate complete an initial interview on their own time, generating more indicators for review and letting candidates share more than a resume, or even a conversation with a recruiter, ever could. AI is starting to replicate the human experience in new ways, because “now candidates feel like, hey, I’ve had two touch points within this experience, and now I feel like I’ve actually had the opportunity to explain what a piece of paper couldn’t explain.”
Still, the jury is out on whether this will make the candidate experience closer to the human touch that is crucial in hiring, or further away. RecOps teams will need to keep a close eye on how these tools are used and make sure that technology creates space for genuine connection, not just more automation.
How Gemini answered: To maintain the “human” touch when using AI in recruiting operations, focus on deploying AI for repetitive, administrative tasks like initial screening, scheduling, and basic communication, freeing up recruiters for high-value human interactions. Recruiters can then concentrate on building relationships, conducting empathetic interviews to assess cultural fit and soft skills, and providing personalized feedback and communication at critical stages of the candidate journey. Transparency about AI usage and human oversight for all significant hiring decisions are also crucial for building trust with both candidates and hiring managers.
10. Which parts of the recruiter’s job will AI replace first?
AI is already taking over the most repetitive and administrative tasks in recruiting operations, especially those that support collecting more indicators from candidates up front. “I’m seeing a lot of people use tools like Talent Llama and basically saying, okay, you’ve submitted the application. We’ve set up an auto move step, everybody gets an interview with these tools,” says Jeremy Lyons. This means that tasks like initial screening, scheduling, and managing high-volume candidate communications are being AI-ed away.
The impact is showing up in team sizes. In this year’s CandE benchmark research, over 80% of the participating companies had reduced their recruiting team size, sometimes dramatically. With team sizes shrinking, are we looking at a forced shift in the skillsets of RecOps teams?
Lyons believes so. He says that, as tool complexity grows and data becomes more central, RecOps professionals will need to build up data literacy and financial literacy, and learn to use business data, like P&L sheets, to anticipate needs and drive decisions. AI is the tool that allows teams to bring in this wide variety of learning. “AI is allowing RecOps to not be the place where you park things. We’re being proactively involved, and that’s what’s making recruiting operations as a field so attractive.”
How Gemini answered: AI will primarily replace the most repetitive, high-volume, and administrative tasks in a recruiter’s job first. This includes initial resume screening and shortlisting, automated interview scheduling, and drafting basic job descriptions and candidate outreach messages. By automating these time-consuming activities, AI frees up recruiters to focus on more strategic and human-centric aspects of their role, such as building relationships, conducting in-depth interviews, and negotiating offers.
Gemini: proving that even the smartest AI can sound like a broken record.
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