Ep 308 – Amy Reichanadter (Chief People Officer, Databricks)

1. The best People leaders are still builders

One of Amy’s most important points was that her career wasn’t built around a master plan. Instead, she consistently leaned into the white space around her — the gaps, problems, and opportunities that nobody else was tackling.

That same mindset is even more important now. AI is changing how work gets done so quickly that there isn’t a playbook to follow. Leaders can’t rely solely on frameworks or best practices anymore. They need to get hands-on, experiment, and learn in real time.

The leaders who thrive won’t be the ones who know all the answers. They’ll be the ones willing to build before they feel completely ready.

“Even if I didn’t know how to do it, I would just lean in and try to build something.”

Amy also emphasized that leadership today requires a balance between performance and learning. You still need to drive results, but you also need to model curiosity and experimentation for your team.

2. Don’t start with AI experts. Start by raising everyone’s baseline.

A lot of organizations are looking for their AI superstars. Amy thinks that’s the wrong place to begin.

At Databricks, the first priority was making sure everyone had access to learning and hands-on experience with the tools. The goal wasn’t to identify the most advanced users immediately. The goal was to create a level playing field.

What surprised her was who emerged as the strongest AI adopters.

“If somebody had asked me three months or six months ago who the people on my team would be that would emerge as top talent using AI, I would never have picked the people that have risen to the top.”

Once everyone reaches a baseline level of understanding, patterns start to emerge. You’ll see who has the curiosity, creativity, and builder mindset needed to push the work forward.

The lesson: don’t assume you know who your future AI champions are. Give everyone the opportunity to learn first.

3. Let the people closest to the work identify the opportunities

One of the smartest parts of Databricks’ approach was how they identified AI use cases.

Instead of creating a top-down list of automation projects, Amy’s team asked every People team to surface the work they believed could be improved, automated, or redesigned.

The result? More than 100 use cases across the People function.

“They’re so close to the work. They know better than I know what needs to be automated.”

After gathering ideas from across the organization, the leadership team prioritized them based on business value, complexity, legal considerations, and learning potential.

It’s a reminder that transformation efforts often fail when leaders assume they already know where the biggest opportunities are. The people doing the work every day usually have the clearest view of what needs to change.

4. Move fast by treating AI transformation like a product team

Many HR teams are still approaching AI as a collection of experiments.

Amy’s team is approaching it more like product development.

Their process is intentionally simple:

• Process map
• Prototype
• Test
• Deploy
• Capture learnings
• Move to the next use case

The goal isn’t perfection. The goal is speed and learning.

“We are very much running it from the standpoint of speed and learning.”

Every project creates new knowledge that gets applied to the next one. That creates compounding progress instead of isolated wins.

For HR leaders wondering where to start, this is a useful reminder that you don’t need a multi-year transformation roadmap. You need a repeatable learning loop.

5. Your North Star matters more than ever

One challenge of leading through AI is that nobody knows exactly where things are headed.

Amy believes that’s precisely why leaders need a clear vision.

At Databricks, one of her guiding principles is creating consumer-grade AI experiences for employees. In other words, workplace experiences should feel as simple and intuitive as the products people use every day.

“We should expect that that’s the way we service our employees — just like we would service customers in the real world.”

That vision helps teams make decisions.

If an experience requires employees to navigate multiple systems, submit tickets, or hunt down information, it probably isn’t meeting the standard.

Amy shared a challenge she often gives her team:

“If it’s more than three steps or somebody has to go look something up, we’re probably in the wrong place.”

The specific North Star may be different at your company, but the principle is universal. During times of uncertainty, people need a clear picture of what success looks like.

6. AI shouldn’t replace critical thinking. It should expand it.

One concern we hear constantly is that AI is making people less thoughtful.

Amy sees it differently.

The issue isn’t whether people are using AI. It’s how they’re using it.

If AI is doing all your writing, thinking, and decision-making, then yes — you’re probably outsourcing valuable mental work. But when AI is used to challenge assumptions, explore ideas, analyze data, and test thinking, it can dramatically accelerate learning.

“People are learning more than ever when they’re actually using AI to help them with critical thinking.”

This is especially important for HR leaders.

The administrative work that consumes so much of our time is becoming increasingly automatable. That creates an opportunity to spend more time on the work that makes People teams valuable in the first place: leadership, culture, organizational effectiveness, transformation, and human judgment.

As Amy put it, this isn’t a threat to the People function.

It’s an opportunity to re-architect it.

“Our core expertise around people, organizations, leadership, culture — this is our time to figure out how this new tool can make us even more valuable.”

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