Why HR leaders need to rethink their tech stack
Why Zapier made AI fluency a requirement for every new hire
How HR can balance AI experimentation with psychological safety
Why HR should lead AI transformation
First steps for HR teams starting their AI journey
How to encourage AI adoption across the workforce
The future of HR leadership in an AI-first era

Resources

Brandon's Linkedin
Claire's LinkedIn
Jeffrey's Linkedin
AI for HR Use Cases Catalogue (Zapier, open-source)
Zapier’s AI Automation Engineer JD + Interview Guide
Zapier’s AI Fluency Framework
RSVP for ZapConnect — Zapier’s annual virtual conference on Sept 25 (free)

As always, check out AllVoices the industry-leading employee relations tool, today!

Why HR leaders need to rethink their tech stack

The webinar opened with a deceptively simple question: how many HR tech tools are you using right now?

Answers ranged from a handful to dozens, and almost everyone admitted their stack was more cluttered than they wanted. That’s the state of HR technology today: piecemeal solutions cobbled together to solve individual needs, but rarely connected in ways that make life easier.

Brandon Sammut, Chief People Officer at Zapier, acknowledged this reality: “The goal is always as few as possible to deliver the impact the organization is counting on from us. But practically speaking, it’s somewhat messy for some organizations.”

Growth makes stacks messy by design

Startups often begin with lightweight tools. As headcount grows, so do compliance needs, payroll complexity, and demands for learning or performance systems. HR is left with a toolkit that looks more like a patchwork than a strategy.

Claire Schmidt, Founder of AllVoices, noted that the challenge isn’t just tool sprawl, it’s the lack of a roadmap. “The stack that works at 10 employees doesn’t work at 100, and what works at 100 won’t scale to 1,000. Leaders need to plan for adaptability, not perfection.”

Why AI offers a chance to rethink stacks

Zapier’s whole value proposition is connecting tools and building workflows that bridge silos. That same philosophy is starting to be applied inside HR. Automation can sync data, reduce duplicate entries, and surface insights without requiring yet another platform.

This matters because the tech stack conversation is also a strategy conversation. A messy system leaves HR bogged down in reconciliation. A connected, AI-driven stack frees them to think strategically.

Why Zapier made AI fluency a requirement for every new hire

One of the boldest moves Zapier has made is requiring baseline AI fluency for all new hires.

Brandon explained the reasoning clearly: “We believe that a team that is collectively fluent in how to build and rethink work with AI is going to find answers a lot faster than organizations that have AI experts in one corner and everyone else waiting for them to figure it out.”

What fluency really looks like

At Zapier, fluency doesn’t mean coding or data science. It’s about:

Framing clear problems AI can solve
Writing effective prompts
Knowing when human oversight is essential
Recognizing ethical boundaries and risks

Brandon added, “With AI, you can delegate some of the work, but you can’t delegate the accountability.” That mindset shapes how employees experiment: bold enough to test, disciplined enough to own outcomes.

Different philosophies on adoption

Claire contrasted Zapier’s approach with AllVoices. For her company, AI fluency isn’t mandated, but openness is. “They need to be comfortable speaking about it. They need to be knowledgeable about it. But if they don’t want to use it every single day, that’s okay — as long as it’s not keeping them from doing their best work.”

This highlights an important nuance: enterprise-scale companies may require fluency to maintain speed, while smaller orgs may emphasize curiosity and awareness.

How HR can balance AI experimentation with psychological safety

When ChatGPT 3.5 launched in late 2022, Zapier jumped in quickly. By March 2023, Brandon’s team was already exploring ways to embed AI into daily work. It was high ambiguity, high potential — and experimentation was the only way forward.

“Anytime an organization is trying to do something high in ambiguity, the rate of learning has more to do with success than how good you are at something today,” Brandon said.

Why safety matters in experimentation

AI only works if people are willing to try, fail, and share what they learn. That requires psychological safety. Claire underscored this: “People have to be comfortable that if something goes wrong, they’re not going to get fired. When there’s that safe feeling, they will be more likely to take risks, try new things, and grow.”

Building the right conditions

Allocate explicit time for AI practice
Celebrate both successful use cases and lessons from failure
Provide anonymous reporting for concerns about misuse
Reinforce that AI is an accelerant, not a replacement

Without these cultural guardrails, employees will either ignore AI entirely or use it in ways that stay hidden from leadership — both outcomes that stall adoption.

Why HR should lead AI transformation

Perhaps the most surprising takeaway was Zapier’s decision to make HR the orchestrator of AI transformation.

Brandon explained why: “We talk about this as AI transformation, but it’s at least as much a people transformation as it is a technology transformation. That’s why our people team is the orchestrator.”

Why HR is positioned to lead

HR touches every role, every process, every department
Culture-building is HR’s core competency
Adoption requires trust as much as technical skill

Claire noted that this reframing is crucial: employees embrace AI more readily when it’s integrated into people strategy instead of imposed by IT. Making HR the driver signals that AI is for everyone, not just for technical teams.

First steps for HR teams starting their AI journey

For leaders who haven’t yet taken the plunge, the advice from both Brandon and Claire was clear: don’t start with technology, start with problems.

Claire put it plainly: “The more successful pilots will be the ones that actually have a clear problem defined first. What’s wasting a lot of your time at work? Where are you getting bogged down? Define the problem, then explore automation.”

Where to start

Candidate communication templates
Onboarding checklists derived from job descriptions
Survey comment synthesis
Automatic tagging and linking of ER cases

Some of these can be built internally. More sensitive workflows, like employee relations investigations, may require specialized tools like AllVoices to ensure security and fairness.

Aligning with company priorities

Brandon added that the best early projects sit at the intersection of business impact and employee motivation. Pick tasks that improve the employee experience while also creating measurable efficiency gains. That dual benefit builds momentum quickly.

How to encourage AI adoption across the workforce

Even with strong pilots, adoption isn’t automatic. Confidence is often the missing ingredient.

Zapier addressed this by appointing AI fluency captains within the people team. These employees dedicate part of their role to helping peers learn. They run live workshops where the group chooses a use case and builds a solution together.

Brandon described the impact: “The amount of confidence someone comes away with after that type of experience just jumps off the page.”

Peer-led learning as a multiplier

Learning from colleagues adds safety. It normalizes trying, asking questions, and making mistakes. Claire responded enthusiastically: “I’m now spinning with things we could do in our own company. I’m sure everyone else is too.”

Other tactics include:

Making resources easy to find and use
Recognizing employees who share learnings
Encouraging diverse comfort levels, from novice to advanced

The goal isn’t uniform expertise, but broad confidence. Adoption sticks when people see AI as an enabler, not a mandate.

The future of HR leadership in an AI-first era

The session ended with a forward-looking message. AI will soon be as expected in job candidates as Excel once was. Brandon noted: “Right now it’s still fairly distinctive in the job market, but before we know it, it will be a requirement.”

Claire emphasized that the path doesn’t look the same for every company, but the responsibility is universal: equip employees to thrive in an AI-first environment.

The throughline across the conversation was clear. For HR to move from firefighting to influence, leaders need to:

Require or encourage AI fluency depending on scale
Create safe spaces for experimentation
Pilot projects tied to clear problems
Empower employees to learn from each other

That combination doesn’t just make HR more efficient. It positions HR as the function that leads organizations through cultural and technological transformation.