How HR Can Turn Executive AI Ambitions into Real, Measurable Change
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Across industries, CEOs are making bold statements about “becoming an AI-driven company.” For HR and People leaders, though, the view from the ground looks very different.
While executive enthusiasm has skyrocketed, adoption on the front lines feels uneven. Some teams are experimenting confidently. Others are unsure where to start. And HR is often caught in the middle, expected to accelerate adoption while also managing the cultural, ethical, and skill implications that come with it.
That tension came through clearly in Ethena’s recent AI Readiness at Work webinar, where hundreds of HR and compliance professionals shared how their organizations are navigating the shift. When asked if “their CEO has been pushing hard for company-wide AI adoption,” responses revealed a story that’s still unfolding.
When surveyed in June, most respondents described their CEOs as strongly encouraging AI adoption across the company. By late September, those responses had shifted. While enthusiasm remained, there was a noticeable rise in neutral and cautiously optimistic sentiment.
That evolution may sound minor, but it signals something important, a movement from hype toward realism. As Roxanne Petraeus, CEO of Ethena, noted during the webinar:
“This is a new thing. There’s no playbook. CEOs didn’t go through this five years ago and get a preview; we’re all sort of going through this change in real time.”
For HR leaders, that shift represents an opportunity. It’s a chance to reshape the AI conversation from speed to sustainability, ensuring adoption isn’t just happening quickly, but happening well.
If the last decade made HR the steward of employee experience, the next will make it the steward of AI enablement.
Because while IT and Operations teams may drive the technical rollout, HR owns what really determines success: people’s understanding, trust, and willingness to use new tools.
Melanie Naranjo, Chief People Officer at Ethena, framed it clearly:
“That framing can be both incredibly freeing and incredibly terrifying, because there’s this sense of, like, what do you mean it’s not HR’s job to own AI adoption? But I think HR’s job is to enable and facilitate.”
When People teams take the lead in creating the conditions for success, with clarity, safety, and communication, AI adoption becomes less about pressure and more about empowerment.
When goals around AI adoption are vague or inconsistent, teams struggle to engage meaningfully. Momentum stalls, and uncertainty grows.
One of Ethena’s biggest friction points early on came from a lack of clarity about the “finish line.” Was success measured by usage, skill fluency, or experimentation?
Melanie described the turning point:
“Experimentation such that at the end of the quarter, every employee has three tools they’ve experimented with, that feels realistic and motivational to me.”
That reframed success from mastery to momentum, an approach that built curiosity without fear of failure.
How to put this into use:
AI maturity doesn’t scale evenly across functions. The same prompt that delights a marketing team might alarm an engineering one.
Melanie explained:
“You’ve got to start with the leadership team, find out what concerns they have, figure out what strategy’s going to work best for them. I don’t think it’s a one-size-fits-all across the company.”
Roxanne agreed:
“A centralized approach just doesn’t work, how a sales team adopts AI is really different from how customer success or engineering will.”
The solution is decentralization. Each department lead defines what realistic adoption looks like for their function. Engineering can focus on security and compliance. Marketing can lean into experimentation. The result is broader participation with less friction.
How to put this into use:
Adoption success has less to do with policy and more to do with psychology. Employees’ confidence or skepticism about AI can make or break rollout efforts.
Roxanne shared that one of Ethena’s breakthroughs came after pausing to really listen:
“We realized the transformational thing wasn’t getting everyone to use the same tool. It was figuring out how to use AI in our product in a way that would truly change how we work.”
That shift, from chasing metrics to understanding motivation, turned adoption into a collaborative process.
How to put this into use:
AI adoption rarely fits neatly into dashboards. Early metrics often fail to capture cultural progress, and over-measuring too soon can discourage experimentation.
As Roxanne explained:
“I built a custom GPT pretty early, because I was like, look, how can I tell the company to start using these tools if I don’t understand what they’re capable of?”
That mindset, modeling curiosity instead of compliance, allowed Ethena’s leaders to focus on engagement and exploration first, before tightening measurement.
How to put this into use:
The most effective AI learning often happens informally. Creating space for low-stakes, peer-driven learning builds confidence faster than top-down mandates.
Melanie shared how Ethena’s AI Show-and-Tell became a turning point:
“People got really excited, and then they felt more comfortable asking questions, because there wasn’t that fear of, like, if I ask something, I’ll look dumb.”
By normalizing curiosity rather than perfection, teams created psychological safety and momentum.
How to put this into use:
Roxanne added that this people-first approach is often where HR shines:
“Building psychological safety might not sound flashy, but it’s exactly the kind of thing CEOs overlook, and it’s where HR can make the biggest impact.”
When AI becomes a business-wide priority, it’s tempting for HR to take full ownership. But sustainable adoption depends on shared accountability.
Roxanne summed it up well:
“It’s not realistic to think HR is going to become an expert in AI for sales teams or customer success. But finding those experts and helping each function learn, that’s incredibly impactful.”
By equipping functional leaders with guidance, frameworks, and training resources, HR becomes a force multiplier rather than a bottleneck.
How to put this into use:
As adoption matures, the right metrics shift from participation to performance. HR can lead this evolution by defining what good looks like, not just who’s using AI, but how it’s improving work quality, engagement, and inclusion.
Some indicators worth tracking:
The key is connecting data back to culture. AI should amplify people, not replace them.
AI adoption isn’t a one-time decision; it’s an ongoing cultural evolution. And HR is the department best positioned to ensure it unfolds responsibly.
As Melanie reminded the audience:
“The new HR isn’t about just mitigating risk, it’s about innovating responsibly. We can’t stick our heads in the sand and pretend AI doesn’t exist.”
By leading with empathy, structure, and clarity, People leaders can transform AI from a source of anxiety into a company-wide advantage.
The most successful organizations won’t just teach employees how to use AI; they’ll help them understand why it matters and where it fits.
When HR reframes AI adoption as a journey of empowerment rather than enforcement, teams build not only fluency but also trust.
And that’s what turns AI from a buzzword into
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