Inside the AI Race: 4 HR Leaders Reveal What Works

Artificial intelligence is changing how work gets done. As a result, HR has been tasked with equipping leaders to navigate new data-driven workflows, reskilling teams, and protecting culture even as algorithms take on tasks once reserved for humans. 

At HR Tech 2025 in Las Vegas, four leaders at the forefront of HR innovation shared a candid look at what it takes to guide organizations through AI-driven transformation. The panel included: 

• Ekta Vyas, Ph.D., CHRO of Keck Medicine at USC (University of Southern California).  

• Jenny Dearborn, CHRO of business consulting firm BTS.  

• Julie Develin, senior partner of human insights at technology firm UKG. 

• Teri Zipper, CEO and managing partner at research and advisory firm Sapient Insights. 

AI as Organizational Transformation, Not Just Technology 

AI’s impact lies in reimagining how work gets done, not just in automating old processes, Develin said.  

“Don’t just throw AI at the wall and hope something sticks,” she said. “Start with the business process. Identify where automation and augmentation will have the most impact and then bring in the tools.”  

For HR leaders, that might mean replacing static reports with dynamic workforce insights from dashboards or shifting from rigid job-based to skills-based structures. When AI is built into the design of work — not layered on top — it unlocks new ways for people and technology to create value together, said Develin. 

The Power of Partnerships 

Successful organizations don’t leave AI development solely to technologists, Dearborn said. Instead, they create deliberate partnerships between technical experts and business leaders driven by “relentless curiosity.” 

Technical teams understand what AI can do, while curious leaders explore what AI should do to create value for people and the business. When these perspectives meet, AI becomes more than a tool — it becomes a force for purposeful innovation, she said.  

In practice, this process might entail pairing data scientists with HR leaders to ensure every initiative aligns with strategy, ethics, and human impact.  

Data Integrity and Discipline as a Foundation 

AI tools rely on data, and organizations must have clean, reliable datasets to generate meaningful insights. “AI is only as good as the data you feed it,” Zipper said. “If you don’t trust your data, you won’t trust the insights, and neither will your leaders.” 

Before implementing AI tools at scale, HR teams should address data quality issues and governance processes, the speakers said. Once an organization establishes clear data standards, definitions, and stewardship roles, the foundation is set for more trustworthy AI. In other words, organizations that align on what their data means — and who owns its quality — transform information from a liability into an asset.  

Consistent, reliable data enables AI models to deliver insights that are not only accurate but also actionable, Zipper said. This enables leaders to make decisions with confidence, knowing that every recommendation is built on shared definitions and clean, well-governed data. In short, disciplined data practices don’t slow innovation — they make it real. 

Cultural Readiness and Change Management

Beyond technical considerations, the panelists agreed that an organization’s readiness for technological transformation was the biggest factor in AI success. Cultural barriers to AI adoption include resistance to change, fear of job loss, and lack of clarity, all of which can derail even the best technology strategy. 

“People are still figuring out where to start,” Develin said. “Transparency is key. Explain the ‘why’ behind AI adoption and show employees how it will make their work better.” 

When employees understand the reasons behind change and see how new tools connect to their own growth, adoption becomes much smoother.  

This kind of readiness goes beyond technical skills, Vyas added. “We have to build not just digital skills but leadership skills — the ability to ask better questions, challenge assumptions, and design change with intention.”