Most companies implementing AI are still figuring out how to explain the changes to employees. Zapier’s customer support team had a different problem: they knew exactly what needed to change, and their leader told them so.
Eighteen months ago, Lauren Franklin, head of support at Zapier, which makes a popular tech tool to automate repetitive tasks, presented her team with stark math. Customer volume was growing at the company but it would be too expensive and inefficient to scale support costs linearly with that growth. The traditional support model would break under the weight of the company’s success.
“There’s a seat on the bus for everyone on this team,” Franklin told them. “But the jobs we’re doing are going to change.”
The hands-on approach
Franklin didn’t delegate the AI transformation. Every Monday morning, she worked directly in the support ticket queue, handling customer issues alongside her team. This wasn’t symbolic leadership—it was intelligence gathering.
By working the same tools and processes as her reports, Franklin could identify which AI applications actually improved workflow versus those that created friction. She experienced customer interactions firsthand and could make informed decisions about process changes. “We will figure out how to do this together,” she told the team—and meant it literally.
The performance challenge and investments in change
Franklin was transparent about expectations. The key performance indicators (KPIs) for customer support needed dramatic improvement over 12 months—the kind of meaningful change that would fundamentally alter how the team operated. As an example of the scale required, she explained that the expectations for the average time spent resolving a customer’s issue might need to drop significantly within the year.
She made clear that these weren’t suggestions. The performance standards would change, and she would be held accountable for team results just as much as individual contributors.
Zapier’s transformation required more than standard customer service automation. The company built an in-house AI ticket system trained on their products and made AI-focused workflow redesign the primary focus of their support operations group.
Most significantly, they redesigned how support and engineering collaborated. Zapier created a residency-style on-call system where support agents could directly access escalations engineers for real-time help solving customer issues. This approach both improved collaboration and developed new technical skills for support staff.
These systemic investments, combined with Franklin’s hands-on leadership, are what enabled the team to exceed previous performance benchmarks.
The compensation bet
Zapier made a counterintuitive move: it raised support team salaries before seeing performance improvements. The company moved all salary bands to the 90th percentile, including customer support roles.
According to chief people officer Brandon Sammut, this served multiple strategic purposes. Higher compensation attracted and retained stronger performers. It also created budget pressure that discouraged unnecessary hiring and made underperformance more visible.
“If you have folks on your team who are okay but not great, that now stands out even more because of how highly they’re paid,” Sammut explained. The logic: if AI was going to enable elite-level performance, the company needed to pay for elite talent and set elite expectations.
The results, and three takeaways
Franklin’s approach delivered. The team achieved their ambitious targets for key support metrics. Employee engagement scores increased 20 to 30 percentage points across multiple areas.
The support organization now operates as what Sammut calls “truly elite”—highly skilled workers using AI tools to deliver superior customer service while earning top-tier compensation.
Here are three key takeaways from the Zapier experience:
- Be specific about performance expectations. Franklin didn’t speak in generalities about “embracing change.” She set concrete expectations and timelines, then held herself to the same standards.
- Lead from the front lines. Working alongside employees using new tools provides better strategic intelligence than reviewing reports about tool adoption.
- Invest in people before demanding results. Zapier’s decision to raise compensation upfront signaled commitment to employees while creating the right incentives for high performance.
Franklin’s approach offers a template for other leaders navigating AI implementation: combine radical transparency about business needs with hands-on leadership and strategic investment in talent. The result isn’t just operational efficiency—it’s higher skills and better pay for the people doing the work.