Intelligent Hiring: 5 Expert Advice for Maximizing AI in Recruiting Efforts (+Use Cases)

“AI alone won’t solve all your recruiting challenges, but it can free up time and let recruiters focus on what truly matters.”

It’s a statement you’ve likely heard before, but when José Benitez Cong, Chief People Officer at Humane, reiterated it during our recent FlexOS x Juicebox expert roundtable, “The State of AI in Recruiting,” it resonated on a whole new level. 

With over 30 hiring managers and TA executives gathering live, this insight wasn’t just repeated—it was validated with new urgency. 

AI isn’t a silver bullet, but it becomes a powerful tool for real transformation in the hands of innovative teams when approached wisely.

From this vibrant exchange of ideas emerged The Intelligent Hiring Playbook, a practical guide filled with strategies and best practices to help you fully leverage AI in your recruiting process.

Here are key takeaways from the panel discussions to rethink your AI approach:

1. Define Your Problem Before Adopting AI

HR leaders should first identify the specific recruiting challenges they aim to solve with AI before selecting a solution.

Jess Von Bank, Global Advisor at Mercer, highlighted that many businesses rush to adopt AT without fully understanding their problem. This leads to ineffective solutions that may not address the most critical pain points, ultimately wasting resources and missing opportunities.

You then risk implementing AI tools that merely automate existing tasks rather than help you transform the way work is done. The FOMO of adopting AI without "obsessing over the problem long enough" causes teams to overlook key aspects of success, like understanding who they are solving for and what success should look like.

Without this clarity, businesses may adopt AI solutions that only add complexity, missing the chance to truly enhance both recruiter and candidate experiences.

Take Jess's words: “Don’t just throw tech at old problems. Think about new solutions.”

2. Create a Culture of Purpose-Driven Experimentation

Leaders should foster an environment where experimentation with AI is encouraged, paired with a mindset of curiosity and continuous improvement.

AJ Thomas, CXO at A.team and ex-Google X, highlighted that companies often jump into AI experimentation without a solid understanding of what they are trying to achieve. 

This leads to superficial experiments that might offer short-term results but fail to align with broader recruiting and performance management strategies, resulting in disconnected efforts that don’t deliver true value​.

“What are we experimenting on? Are you experimenting on AI, or are you experimenting on driving an outcome for your business? I think those are two very different.” - AJ Thomas, A.team

Don’t start experimenting just for the sake of “being innovative.” Clearly identify how AI can address a real business challenge. Establish a clear hypothesis before launching any of your AI experiments.

3. Treat AI as “Augmented” Intelligence

Time is the most valuable gift out of AI, as José Benitez Cong, CPO at Humane, soon realized from his team’s early experiments with the technology.

By strategically automating routine tasks—such as opening requisitions, scheduling meetings, transcribing interviews, and generating job descriptions—AI dramatically streamlines the recruitment process.

His team at Humane spent time first mapping out their processes, carefully identifying where automation could create the greatest impact. José told us, illustrating the clear impact AI had on their operations:

“And so we start to do calibration in real-time. These are things that used to take hours to do. We can now do in minutes.” 

Shift your focus and allow AI to take over routine functions. You then free up valuable time for higher-order activities—those that require human judgment, empathy, and the personal touch that defines successful recruiting.

4. Future-Proof Your Team for the Future of AI

“The same thing is going to happen if we rush to the solution without using data and design thinking. Mapping journeys, not just creating automated transactions. There are some real skills that we need to build before we can design really good solutions for ourselves. All of those same principles still apply. And I would encourage that, especially with AI, because it’s changing so fast.” - Jess Von Bank, Mercer

HR leaders must adopt a long-term approach that grows with both the technology and the business. As AJ said:

"We have to think about experimentation... how do I prove what I’m thinking about in this space wrong so that I can actually find the right thing that we need?" 

This captures the essence of a forward-looking AI approach—one that focuses on continuous learning, adaptation, and evolving alongside the technology.

AI should be part of a scalable, dynamic strategy that aligns with future business needs rather than a quick solution to today’s challenges. And just as important is upskilling the team as the real competitive advantage lies in how well your team can leverage AI’s evolving capabilities.

Phenom has a helpful infographic to summarize how AI changes the role of recruiters:

At the end of the day, it’s about “business outcome-first through candidate experience through recruiter experience.” How we use AI should be focused on making real improvements—helping the team work smarter, hire better talent, and drive lasting results.

5. Prioritize Tools That Enable Proactive Talent Journeys

It's easy to get stuck in a reactive mode, focused on filling vacancies quickly, but this often overlooks the potential to build long-term relationships with candidates. 

Jess highlighted how recruiting tends to be “very transactional,” with workflows designed to screen out those who don’t fit a specific role at a given moment.

“What might be better when this stuff gets truly intelligent is to design things that pull threads through different talent journeys.” - Jess Von Bank, Mercer

The real opportunity lies in using AI to shift away from this short-sighted approach, allowing recruiters to understand candidates on a deeper level and assess their potential for future roles.

This should be how we ensure inclusivity and democratize talent access. As Jess put it well:

“If we could do a better job for talent, we’d stop screaming about the talent shortage. If we truly understood people beyond their work experience, we could recommend what they could do and where they’d be most productive.” - Jess Von Bank, Mercer

José echoed this, suggesting that AI can help gather insights on candidates who might not meet today’s needs but could be a great fit down the line. Instead of moving on, AI enables recruiters to maintain relationships and revisit these candidates for future opportunities.

5 Prominent Use Cases of AI in Recruiting

The focused group discussion ignited vibrant exchanges among executives, revealing exciting AI use cases that can truly elevate the hiring process to the next level:

1. Job Architecture 

If you put AI in the intake process, it can aid in structured, bias-free job descriptions. 

Eikon used the Radford leveling framework and job role inputs to generate unbiased job descriptions with ChatGPT, including an "About You" section that outlines the ideal candidate profile for around 400 JDs.

2. Candidate Feedback

Matt from Jobnet takes on the point of the proactive talent journey and suggests that AI and data can be powerful tools for guiding both candidates and recruiters toward a potential match, even if that candidate isn’t an immediate fit. 

AI could track a candidate's progress after an interview, allowing them to signal their growth and readiness for future opportunities. 

That’s what many talent intelligence and internal talent marketplace platforms are trying to deliver. 

3. Enhance Internal Collaboration

Monika from Bytedance brought up the potential issues among hiring teams—where different teams compete for the same candidates, leading to disjointed and fragmented experiences for both the company and the talent. 

AI helps mitigate this by creating a unified candidate tracking system across teams, ensuring a seamless and consistent recruitment process.

4. Conversational AI for Candidate Engagement

Roundtable participants gave an example of them using conversational AI tools like Humanly and Harriet for a more humane, frictionless, seamless candidate experience. 

Chatbot helps you screen candidates, answer their initial questions, and follow up with them throughout. This is a great example of how AI can streamline day-to-day recruitment activities while improving the overall candidate journey.

5. Interview Assistant

There’s a common adoption of AI notetakers during interviews, in which AI automatically records and organizes interview content for you. 

But a step further is how AI not only captures notes but analyzes the data across multiple interview stages so that hiring teams can seamlessly collaborate, draw meaningful conclusions, and gain deeper insights into candidates' performance throughout. 

Final Thoughts: The State of AI in Recruitng

AI has the potential to revolutionize HR and recruitment, but it’s not a set-it-and-forget-it solution. To make it work, hiring teams need to approach it with clear goals and a well-thought-out strategy. 

That’s how you ensure AI helps you achieve better outcomes, serves your purpose, and takes your team to the next level—not the other way around.

And we’re just at the beginning. AI will continue to evolve, bringing even more breakthroughs to the market. 

That’s why it’s crucial to build a solid foundation now—upskill your team, keep experimenting, and refine your approach as the technology grows.