How Hiring Teams are Putting AI to Work Without Giving Up Control

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AI in hiring rewards Intentionality. The teams getting value out of it picked specific workflow gaps where they were already losing time, wrote rules for what AI is allowed to do, and kept humans on the work where judgment matters most. Trust came later, earned inside those rules.

Episode 3 of The State of Industrial Hiring: Live brought Dre Williams of CLX Engineering and Kristin Gladki of Apex Placement & Consulting into conversation with FactoryFix's Gianna Fornesi. Dre is the HR Generalist at CLX, a growing automation and controls engineering company in Central Florida, where he and his director run the whole hiring function. Kristin is the Director of Client and Candidate Success at Apex, a woman-owned staffing firm operating across 10 states with a 19-person team. Both started the year as AI skeptics. Both spent it figuring out where AI fits and what to keep human.

The concerns are well documented. The 2026 FactoryFix Industrial Hiring Benchmark Report asked hiring leaders to name their biggest AI challenges, and three came back ahead of the rest: trusting AI recommendations (40%), managing candidate experience alongside automation (35%), and ensuring data quality and accuracy (28%). Underneath those numbers sit deeper concerns about bias, compliance, candidate privacy, and how much control the team keeps over the process. Dre and Kristin built their AI work around those concerns from the start.

Three takeaways worth sitting with.

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1. Control how AI shows up to candidates.

Dre Williams learned this one the hard way. He set his AI to sound as human as possible. Candidates noticed anyway. The conversations felt off and the response rates didn't hold.

"I didn't want it to introduce itself as AI. I wanted to keep it as humanly in touch as possible," Dre said. "But because it's AI, it still came off as AI. So naturally my applicants were a little frustrated because they thought they were talking to a real recruiter."

So he flipped the setup. Now the AI introduces itself as "CLX automated recruiter" before it asks anything, explains what it's collecting, and tells the candidate a person takes over once the pre-screen is done.

"Our candidates were so much more understanding once it was truth out there: hey, this is AI but we're using it to help speed the process up," Dre said. "That's what really changed the applicant's mind, once they realized this is used as a tool to push me through the hiring process quicker."

That shift made it into the report. Dre is quoted on the same point: "We trained our AI recruiter to introduce itself as AI early in the process. Once candidates understood they were interacting with AI, engagement and follow-through improved, and the human handoff felt like an upgrade."

Kristin Gladki made the same call at Apex, and framed it as the foundation of the candidate relationship.

"There's a lot of respect and trust that comes from our candidates when they know, even though it may be AI contacting them, that we're providing transparency around that and that they're not losing the human connection," Kristin said.

Not every team lands in the same place. Some run AI through a short initial screen without an explicit identification and see no issues, especially when the conversation is tight and heavily customized. What they share with Dre and Kristin is that they decided on purpose.

Audit your AI outreach first. Decide whether it identifies as AI up front or runs as a short initial screen without that identification. The right call depends on how thorough the screening conversation is. Whichever way you go, make the choice deliberate and make sure your setup matches it.

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2. Set the rules before the AI runs.

The 40% of hiring leaders who name trusting AI recommendations as their biggest challenge are usually stuck on the output. Did it surface the right candidate? Did it score them correctly? Kristin moved the question earlier. At Apex, she spent the year writing rules of engagement before any AI touched a candidate. She calls them SOPs, and they cover what data goes into AI, what stays out, what gets cited, and who has the final read.

The first rule is citation.

"When I specifically use AI, whether it's FactoryFix or ChatGPT or any of the AI tools we use, I ask to source my data," Kristin said. "It may provide me some great information about the labor market in Metro Detroit, but if I don't know where that information came from and I can't cite it to our clients, then we know that knowledge really isn't there. It becomes less fact and more opinion."

The rest is data hygiene. Apex recruiters don't paste full client job descriptions into open AI tools. They don't put candidate personal information into AI. Those rules protect client confidentiality and candidate privacy, and they protect the team's trust in the system.

"Setting those boundaries not only helped continue to build trust for teams who aren't comfortable using the tools yet," Kristin said. "It really sets a consistent guardrail to ensure our team and Apex stay safe as a whole."

Then the trust compounds. Inside Apex's guardrails, the AI has learned the company's tone, Kristin's voice, what Apex looks for in a candidate, and how hard to screen across different role types. She still reviews the output. The review just takes less time as the AI gets more accurate.

"It can speak in the voice of Apex or it can speak in my voice. I still have to review the output," Kristin said. "But it's a lot less as AI learns what Apex is looking for and what voice we want to put into the world."

Write the rules before the AI gets near a candidate. Define what data goes in, what stays out, what has to be cited, and who has the final read. The trust your team builds with AI grows inside those rules over time.

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3. Find one task. Customize it. Expand from there.

Neither team turned AI on across the whole workflow. They each picked one task where they were already losing time and grew outward from there.

For Dre, that task was resume screening. CLX gets a lot of applicants for engineer and field tech roles, and he and his director couldn't get to all of them with the depth the hiring needed. So he put AI on the technical screen, kept his team on the character and cultural read, and built the handoff between the two.

"AI can help speed up the process in recruiting. It can reach out to applicants, it can help me screen resumes quicker," Dre said. "Once the resume is screened, they're screening for the technical side of it. Now I can pick up and take the human side of it, and that's where the character comes in and the cultural fit comes in. I think oftentimes we hear AI and think it's gonna replace the full hiring process, not realizing it's just an additive tool in the hiring process."

Kristin started with after-hours engagement. Apex was getting applications from candidates working shift jobs who couldn't talk during the day, and the team couldn't physically cover those windows. So they put AI on the first touch overnight and on weekends, with humans picking up the conversation as they came in.

It paid off fast. Apex took an order for 18 machinists from a client that traditionally used multiple staffing vendors, and didn't have a prebuilt pipeline ready. AI sourced from scratch overnight. By the time the team came in the next morning, 12 of the 18 roles were filled.

Both gave the same starting advice: find one task that eats time, that AI could plausibly start, and that you can write specific rules around.

"Take one task, one small task that takes a lot of time out of your day, and just say: could this be automated with AI?" Kristin said. "It doesn't mean it does the whole work for you, but could AI start it for me and gather information so that when I sit down to put a business case together, I'm educated on what I need without spending hours hand-pulling all of that information?"

Find one task that eats time and produces the least judgment-dependent output. For most teams that's screening inbound resumes or running the first candidate outreach, the work Dre and Kristin each automated first and the same work FactoryFix's AI recruiter does on the platform: screening applicants against the role and starting the conversation so your team steps in for fit and closing. That first-touch screening is why candidates on FactoryFix respond at about 64%, roughly 3x the rate of general job boards. Pick one, customize it to your team's tone and rules, and watch what changes before you add the next.

That's the thread running through all three. Dre and Kristin didn't hand the process to AI and hope. They decided how it would show up, drew the lines it had to stay inside, and pointed it at one job before the next. Start there and you keep the judgment that makes the hire worth making.

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Watch the full conversation

The full 30-minute recording is available here: Watch Episode 3.

This was the third and final episode of The State of Industrial Hiring: Live, a webinar series built on findings from the 2026 FactoryFix Industrial Hiring Benchmark Report, which analyzed 1.2 million applications, 15,500 roles, and survey responses from 83 hiring leaders across 18 industrial role categories.

Watch all three episodes here: Watch the full series. Download the report here: 2026 Industrial Hiring Benchmark Report.

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