AI is everywhere in HR right now, but clarity isn’t.
So we asked HR leaders, consultants, and executives a simple question: what did you get wrong about AI at first?
What came back wasn’t a list of tools or tactics. It was a series of mindset shifts: moments when leaders realized AI wasn’t just changing workflows but was quietly challenging long-held assumptions about control, trust, decision-making, and value.
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Below, HR experts share the biggest before-and-after moments they experienced once AI moved from theory to day-to-day work—and what other HR leaders can learn from them.
What many leaders got wrong: AI was just about efficiency
Most leaders started with a familiar goal: saving time.
But Francesca Ranieri, CEO of The Frank Strategy, quickly realized that speed alone wasn’t the unlock. What mattered was what leaders chose to do with the capacity AI created.
Instead of treating AI as a way to squeeze productivity out of the same work, she made a deliberate leadership decision:
If you find an AI that takes something off your plate, you get that time back. Every hour or dollar saved [with AI] went into what we called our ‘abundance bucket,’ funding the strategic projects we never had time for. The real shift wasn’t in the tech, it was in the mindset.
Ashutosh Garg, CEO of Eightfold.ai, described the same shift from another angle: AI value isn’t just theoretical. It has to show up inside real workflows and deliver results.
“AI projects are first launched as experiments, not embedded into core business workflows. They look impressive on a slide, but they often don’t connect to measurable outcomes.”
And when it does work, it’s because the cost and benefit are tangible:
“Every interview has a real, quantifiable cost in recruiter and hiring manager time…the value isn't theoretical; it’s measurable.”
Pro tip: AI impact isn’t about doing the same work faster. It’s about freeing up capacity for the work that actually moves the organization forward.
What surprised them: AI doesn’t fix broken systems
Another early assumption quickly fell apart: that AI would fix messy foundations like unclear processes, inconsistent data, and fuzzy ownership.
Several HR leaders compared the current AI wave to past HR tech rollouts: big promises layered onto shaky foundations.
As Francesca Ranieri put it honestly:
“We’ve seen this before, CRM and HCM rollouts that promised transformation but couldn’t fix bad data hygiene, broken or non-existent processes, unhealthy culture, or unclear strategy.”
AI Strategic Advisor Aman Bandvi framed this as a leadership responsibility, not a technical limitation:
"We are not just adopting a new technology. This requires a fundamental shift in mindset. The heroic CEO making all the calls is an obsolete model. The new leadership is collective and augmented. It's about fostering a symbiotic relationship between human teams and AI systems. My role is to help the leader cultivate this new ecosystem, where their success is measured by the collective intelligence and ethical integrity of their human-AI organization."
Aman's Tip
The AI-first world is fundamentally recasting the role of a leader. It’s a shift from being a decision maker to a sense maker and system architect.
The message across interviews was consistent. AI doesn't solve foundational problems; it scales whatever already exists. And the next step is clear—before expanding AI, get clear on the basics: clean inputs, real ownership, and workflows people actually follow.
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What shifted in leadership style: from having answers to creating conditions
One of the biggest changes leaders described wasn’t technical; it was personal.
Jennifer McClure, CEO of Unbridled Talent, noticed that AI challenged the idea that leadership means being the smartest person in the room:
Leadership in an AI-first world is less about commanding from the front and more about creating the conditions where people—and technology—can do their best thinking together.
She emphasized that transformation wasn’t tool adoption:
“Transformation doesn’t come from adopting new tools—it comes from adopting a new mindset.”
And Mel Plett, Founder of Cordelia Consulting, echoed the same shift toward more human leadership:
Leadership isn't about having titles or having all the right answers. It's about listening, asking better questions, and shaping workplaces where people see their impact.
"As more technical and operational work gets automated, leadership will shift to being almost entirely about forming deep relationships, providing transparency, building trust, and helping others connect to their purpose."
The takeaway: AI in the workplace raises the floor on execution. But leadership still sets the bar on judgment, relationships, and clarity.
What blocked adoption: permission mattered more than tools
Many leaders assumed that once AI tools were available, people would naturally start using them.
“The real gap is between aspiration and permission.”
Organizations talked about AI in HR as the future of work, but employees weren’t sure what was acceptable right now:
“AI promises the future of work, but people still need someone to redesign the present so they can actually use it.”
Stop waiting for perfect clarity. The tech is moving too fast for that. Instead, pick a few areas where your teams can experiment safely and start building muscle.
Mel Plett reinforced the emotional layer underneath adoption:
“People need to feel safe, confident, and valued in an AI-augmented workplace.”
Pro tip: If you want adoption, publish permission. Clear guardrails beat vague encouragement every time.
What stayed true: AI didn’t replace HR judgment
Some HR leaders worried AI would undermine professional judgment.
What they found was the opposite: AI became most useful when it supported thinking, not replaced it.
Mel Plett described the turning point:
“AI was not about replacing our judgment; it was a tool to help us see our blind spots and provide thought partnership to make smarter decisions.”
She was also clear about what would matter most going forward:
“The leaders who will thrive… won’t be the ones who know the most about AI, but the ones who know the most about people.”
Katie Burke, Chief People Officer at Harvey, described how her team uses multiple models not for novelty, but for discernment:
"We are in the process of adding a few additional AI-specific tools to our stack as we speak. And because we are a multi-model company, we use Gemini, Claude, and ChatGPT — both on their own and to test out Harvey outputs via our Legal Research team."
Pro tip: Strong HR leaders don’t outsource judgment. They use AI to pressure-test it.
What didn’t scale: pilots without systems
A recurring lesson across interviews was that experimentation alone doesn’t create change.
Aman Bandvi cautioned against treating AI like a shortcut:
“Stop selling ‘AI solutions’ and start guiding leaders through… asking the right questions of the future.”
Reyhaneh Khalilpour grounded the same idea in day-to-day operational reality, emphasizing the importance of documenting workflows and building systems that last beyond a single power user.
Reyhaneh's Tip
I ensure that every AI initiative starts with a clear understanding of the process it’s meant to improve. We map pain points, define outcomes, and only then select or design AI pilot solutions. This prevents tools from being used as a band-aid and shifts focus to capability-building rather than just automation.
AI doesn’t become capability through enthusiasm, it becomes capability through repeatability.
Pro tip: If an AI workflow disappears when one champion leaves, it isn’t a capability yet.
What leaders eventually realized: AI has to live inside the operating model
At scale, AI stopped feeling experimental.
Ashutosh Garg described the maturity shift clearly:
“AI only creates value when it’s deeply integrated into how work actually happens, not when it’s layered on top.”
This is where many HR teams get stuck: AI is added as a layer rather than embedded as organizational infrastructure.
The leaders seeing results weren’t running more pilots. They were redesigning how work moves.
Takeaway: AI maturity isn’t about launching more experiments. It’s about embedding AI into the workflows that matter.
What HR’s role is becoming: designing decision systems
Finally, several leaders described stepping back from making every decision themselves and focusing instead on how decisions get made.
For HR, this is significant. The work isn’t just adoption, it’s shaping the guardrails that allow speed without losing trust.
What all the experts had to unlearn
Across every conversation, HR leaders didn’t just adopt AI. They let go of old assumptions.
This means the end of equating output with progress, loosening control, and focusing less on tools but more on systems, permission, and judgment.
As Francesca Ranieri summed it up:
Francesca's Tip
When you get the sequence right, AI stops being a shiny distraction and starts becoming a real force multiplier.
AI doesn’t replace leadership. It reveals where leadership needs to evolve, and gives HR the opportunity to shape what comes next.
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More expert interviews to come on People Managing People!
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