As AI shapes the world of work, HR and other business leaders are being forced to answer a difficult question: Are we using AI to simply do things faster or to fundamentally do things better?
This episode of HiBob’s People Proud podcast cuts through the hype and zeroes in on what actually matters when it comes to AI in HR.
The conclusion was clear: AI’s role isn’t to replace people. It’s to strengthen them.
Kyle Lagunas, founder and principal analyst at Kyle & Co, joined HiBob’s Tali Sachs and Ken Matos to unpack why the familiar “do more with less” mindset is a trap—and how HR leaders can move beyond reactive AI adoption and use the technology as a force for resilience, credibility, and long-term talent advantage.
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The pressure to perform (and the trap of reactivity)
For many organizations, the rush to adopt AI has more to do with pressure than strategy.
Ken Matos pointed to one striking data point: 74 percent of CEOs worry they could lose their jobs if they don’t show tangible AI returns within two years. That expectation doesn’t stay in the C-suite. It quickly cascades through the organization.
More often than not, it lands squarely on HR.
“One thing that concerns me is that pressure resulting in doing something with AI,” Lagunas explained. “I don’t want to call it purely performative. It is reactive.”
Suddenly, the company expects teams to “do something” with AI, even though the destination isn’t clear. And when the goal becomes being able to say, “This is what we’re doing with AI,” there’s often no space left to cultivate a thoughtful response.
Tali Sachs sees this pattern play out again and again, where knee-jerk reactions lead companies to rush into disconnected tools that look innovative on paper but don’t align with real business needs.
In the process, the human side of the equation often becomes an afterthought, as leaders frequently skip the essential step of upskilling their people.
“They’re like, ‘Maybe we’ll just lay off X amount of people because AI can do that [instead],’” Sachs said. But, in cases like this, leadership realizes—sometimes as much as a year later—that what the organization really needs are people who are proficient in AI, not just AI tools. The key is investing in developing those skills across the workforce.
To put it simply: Reactivity is the biggest risk factor here. AI readiness is the advantage.
Building AI readiness, not reactivity
For Kyle Lagunas, the answer to reactive AI adoption isn’t slowing innovation. It’s shifting the focus to AI readiness instead of chasing “the next big thing” and grounding change in fundamentals.
He compared today’s AI moment to the rise of social and mobile tech 15 years ago: waves of innovation that ultimately required changes in behavior, not just new software.
Instead of chasing every new tool, Lagunas encouraged HR leaders to focus on building people’s ability to continuously adapt and use technology to unlock better outcomes, not just faster results.
“I would rather us come into the boardroom and say, we are getting change resilient,” Lagunas said. We are getting … transformation-ready … That’s the capability that we’re building … It’s more intentional.” This demonstrates doing the foundational work without saying, “Hey, we’ve turned on 50 new AI use cases.”
Matos reinforced this distinction, grounding the conversation in a simple principle: When it comes to doing AI right, purpose must lead adoption.
“If you’re aiming to help make your workforce better, more productive, and more effective,” Matos said, “that’s a very different outcome than just wanting to have as few [people] as possible.”
You can see that mindset shift most clearly in how organizations approach learning.
Upskilling for an AI-ready workforce
When it comes to turning AI readiness into reality, learning is often where organizations struggle the most, Sachs noted. Too often, organizations expect their people to simply know how to use AI tools without investing in meaningful training.
Lagunas agrees and pointed out that this isn’t anything new. “We have been here before,” he said, recalling the rise of social and mobile technologies in the early 2000s. But back then, he noted, organizations policed social media use instead of teaching best practices.
This moment offers an opportunity to do it differently—by teaching AI best practices that elevate people’s potential and the organization’s success.
Rather than assuming fluency, Lagunas emphasized the importance of treating AI literacy as a core capability across the entire business—not just for technical teams—and building it intentionally through learning, experimentation, and shared standards.
This approach creates the conditions for a workforce that’s genuinely transformation-ready.
Building that kind of workforce doesn’t happen in isolation. It depends on trust, data, and the partners HR chooses to work with.
Data, credibility, and trusted partnerships
For Lagunas, credibility in the age of AI is shaped as much by who HR chooses to partner with as it is by the data itself.
“Ethos matters,” he said. But it’s not just about delivering innovation. It’s about helping HR leaders maintain credibility internally while navigating change.
As AI becomes more embedded in business decisions, trust begins to carry real weight—not just in the tools organizations adopt, but in the values and intentions behind them.
Sachs and Matos echoed this sentiment, noting that the most successful organizations treat their tech providers as collaborators rather than transactional vendors. Cultural alignment, Lagunas emphasized, has become as critical as innovation itself.
AI maturity is about momentum, not perfection
Based on his research across global HR teams, Lagunas has come to a counterintuitive conclusion: True AI maturity doesn’t exist yet.
What sets leading organizations apart isn’t perfection but momentum: the ability to keep moving forward through experimentation, learning, and collaboration.
That momentum, Lagunas explained, depends on cross-functional coalitions. “We’re talking about … what’s moving the needle in AI for HR,” he said. “It’s not more AI.” It’s the foundational capabilities that have long differentiated HR, especially it’s ability to create deep and meaningful partnerships with leaders across the business.
“If I want to have a challenger dynamic with all of my stakeholders, all of my partners across the ELT, I need to have partnership with them,” he said. The HR teams making the most headway are the ones working hand-in-hand with IT, Legal, and other business leaders.
That kind of partnership builds accountability and safety—fundamental conditions that make sustained AI progress possible.
Balancing innovation, intention, and people
The conversation landed on a simple but powerful philosophy for the future of work: AI in HR isn’t about cost-cutting. It’s about augmentation, expanding what people are able to do, and giving them the capacity to do higher-quality work.
For leaders, adopting this philosophy means rethinking how value, growth, and people connect.
Sachs pointed to a concept frequently championed by Ronni Zehavi, HiBob’s CEO and co-founder: “Do more with more.” The idea isn’t to cut people out of the equation but to unlock more value using the talent you already have.
Lagunas strongly echoed this perspective, underscoring the risk of using AI solely to chase efficiency without improving the quality of the work itself.
“It’s not time to scale mediocrity, but that is the risk that you run,” Lagunas said. It’s not just doing more with less. It’s doing more and doing it better than you ever have before while under pressure to cut costs. That added dimension is the opportunity.
Getting there requires HR to keep doing what it does best: bringing the business together. Partnering with IT, Legal, and line-of-business leaders helps ensure people use AI to solve real problems, not just impressive use cases.
As Matos put it, the metric for success is simple: “AI success creates business success, and if it doesn’t, why bother?”
Key takeaways: How to use AI in HR to drive impact
- AI in HR is about augmentation, not cost-cutting. The most effective AI strategies focus on expanding what people can do and improving the quality of work, rather than simply reducing headcount or chasing short-term efficiency.
- How to use AI in HR starts with readiness, not reactivity. Organizations that move beyond pressure-driven adoption and focus on intention, learning, and alignment are better positioned to realize long-term value.
- AI in HR examples work best when skills come first. Building AI literacy across the workforce—through learning, experimentation, and shared standards—creates the foundation for sustainable adoption and stronger outcomes.
- Credibility in AI adoption depends on trusted partnerships. The partners HR chooses to work with across the organization play a critical role in maintaining internal trust, aligning values, and ensuring AI supports real business and people needs.
- Momentum matters more than maturity. There is no finish line for AI maturity. Progress comes from continued collaboration, cross-functional coalitions, and the willingness to learn and adapt over time.
- Do more with more to unlock higher-quality outcomes. The real opportunity with AI is enabling people to do better work, helping organizations grow through capability, connection, and shared purpose.
