How Zapier's HR Team Built an AI Stack Without Engineers — and Got 97% of the Company Using It
How Zapier's HR Team Built an AI Stack Without Engineers — and Got 97% of the Company Using It
In March 2023, three days after OpenAI released GPT-4, Zapier CEO Wade Foster declared a company-wide "Code Red." The message was blunt: AI was about to reshape every function in the business, and the People team was no exception. Within two years, 97% of Zapier employees were actively using AI in their daily work — up from 65% in late 2023 — and much of the HR tooling driving that adoption was built by HR professionals who never wrote a line of code.
Here is how they did it.
The "Code Red" catalyst
Foster's urgency was strategic, not theatrical. Zapier, a 900-person fully remote automation company, saw GPT-4 as both an existential threat and a once-in-a-decade opportunity. The company mobilized through internal hackathons and an AI champions program, encouraging every team — including People Operations — to build AI tools on Zapier's own no-code platform. The People team took that mandate literally.
Four AI tools, zero engineering tickets
Rather than submitting requests to an engineering backlog, Zapier's People team built four AI-powered tools on their own using the company's no-code automation platform.
1. Goal-setting chatbot (the flagship build)
Emily Mabie, Zapier's manager enablement lead, built the most ambitious tool: an AI-powered goal-setting chatbot that coached employees through the company's AMP (Actionable, Measurable, Purposeful) framework. Using five native Zapier tools, Mabie assembled the system in under two weeks — no developer support required.
The chatbot guided employees through crafting quarterly goals, flagging vague language and prompting specificity. A backend AI layer analyzed patterns across conversations, identifying drop-off points and weak goal areas so HR could intervene proactively.
The results were immediate: 91% participation in the first AI-coached goal-setting cycle, with more than 800 unique chatbot conversations. For context, most companies struggle to push goal-setting completion above 70%.
2. Onboarding automation
The People team automated portions of new-hire onboarding, using AI to personalize welcome sequences, surface role-relevant documentation, and trigger check-in workflows at key milestones. The system reduced manual coordination overhead while ensuring consistency across a fully distributed workforce.
3. Feedback coaching
An AI feedback coach helped managers draft and refine performance feedback before delivery. The tool applied Zapier's internal feedback standards, nudging managers toward specific, behavior-based language — an area where even experienced leaders often fall short.
4. Pulse survey analysis
Rather than waiting weeks for a vendor to process engagement survey results, the People team built an AI layer that summarized open-text responses from pulse surveys in near-real-time. The tool flagged sentiment shifts and recurring themes, giving HR leaders actionable intelligence within days instead of weeks.
From 65% to 97%: what drove adoption
Zapier's AI adoption numbers are company-wide, not limited to HR tools. The jump from 65% in late 2023 to 97% by end of 2024 reflects a deliberate, multi-pronged strategy: hackathons to generate momentum, an internal champions network to sustain it, and — critically — embedding AI adoption metrics into the company's regular engagement surveys. By measuring AI usage alongside traditional engagement indicators, Zapier signaled that AI fluency was not optional.
The People team's own tools contributed to this flywheel. When employees encountered AI in their goal-setting workflow, onboarding experience, and feedback cycles, AI stopped being a separate initiative and became part of how work got done.
The caveat: Zapier is a tech company
It would be disingenuous to ignore context. Zapier is a technology company whose core product is automation — its People team had native access to tools purpose-built for no-code AI workflows. Not every HR team will have that advantage.
The source material for this case study also originates from Zapier's own blog, which carries an inherent product-marketing lens. The outcome metrics (97% adoption, 91% goal-setting participation, 800+ conversations) are self-reported and should be treated as directionally informative rather than independently audited.
Transferable lessons for enterprise HR
That said, the underlying principles are portable:
Start with existing workflows, not greenfield projects. Zapier's People team did not build a custom AI platform. They applied AI to processes that already existed — goal-setting, onboarding, feedback, surveys — and improved them incrementally.
Empower domain experts to build. Emily Mabie is not a software engineer. She is an HR professional who understood the goal-setting workflow better than any developer could. No-code platforms (Zapier, Make, Power Automate, or others) make this possible at scale.
Measure what you want to see. Adding AI adoption to engagement surveys was a subtle but powerful signal. If your organization tracks it, people pay attention.
Ship fast, iterate faster. Mabie built the goal-setting system in under two weeks. Perfection was not the standard; usefulness was. HR teams that wait for a fully polished tool will lose to teams that ship a functional one.
The Zapier case is not a playbook that every HR team can copy verbatim. But it is proof that when People teams are given the tools and the mandate, they can build AI solutions that drive measurable adoption — without filing a single engineering ticket.