From Nike to Wildfire Safety: Building AI With Zero Code Experience

A 14-year Nike veteran pivots to nonprofit wildfire safety and builds an AI advisor herself, starting from “I didn’t understand what a prompt was.”

Kelli Klein owns 40 acres of rural forested land in Oregon. That detail matters because it means wildfire safety isn’t an abstraction for her. It’s the view from her kitchen window.

After 14 years at Nike in operations and technology management, Kelli moved to the nonprofit world. She joined the Firebrand Resiliency Collective, a small Oregon-based organization focused on helping rural communities prepare their homes for wildfire. The mission was urgent. The resources were thin. And the technology tools everyone kept talking about? She hadn’t touched them.

“I had never done a prompt before in my life… it took me two or three times to figure out.”

What she built

Kelli wanted a wildfire home assessment advisor. Something a homeowner could walk through to understand their risk and get specific recommendations. The kind of tool that a big insurer might spend six figures building, except nobody was building it for a small nonprofit in rural Oregon.

So she built it herself.

She started in ChatGPT, testing how the tool responded to real wildfire scenarios, feeding it assessment frameworks, watching where it got things right and where it fell apart. And it did fall apart. She hit the limitations personally because she was the one testing it, not reviewing someone else’s summary of what worked.

That distinction is the whole story. Kelli wasn’t reading about AI tools. She was breaking them with her own hands, then deciding what to do next.

When ChatGPT couldn’t handle what she needed, she evaluated Voiceflow and Typeflow as alternatives. She tested both. She made the platform decision based on what she’d seen, not what a vendor told her.

“The more I got into AI the more especially with the no code tools it was like I can build out prototypes by myself.”

Why this matters beyond wildfire

Kelli’s learning arc is worth naming explicitly. She went from “I didn’t understand what a prompt was” to evaluating AI platforms for a mission-critical safety application. That progression happened because she chose to do the work herself, not because someone handed her a recommendation.

This is the AI player-coach instinct applied to a context where the stakes have nothing to do with career advancement. Nobody was going to build this tool for a small nonprofit. No consulting firm was going to scope it. No engineering team was waiting for her requirements doc. If Kelli didn’t build it, it wasn’t going to exist.

“Nonprofits feel like oh this AI stuff is only for the big players when it’s actually this is the places that can make some of the biggest difference.”

She’s right. The organizations with the fewest resources are often sitting on the most urgent problems. And AI’s accessibility curve has shifted enough that a leader with zero code experience, a clear mission, and the willingness to test and fail can build something real.

The hands-on advantage

Kelli’s story lands differently from the typical “leader learns AI” narrative because her motivation strips away every corporate incentive. No promotion. No board presentation. No competitive pressure from a rival team shipping faster. She built because rural homeowners needed a tool that didn’t exist yet, and she was the person closest to the problem.

That clarity of purpose actually made her a better builder. When you know exactly who the tool is for and what failure means in the real world, you test harder. You don’t accept a demo that looks good. You push until it works for the person standing in front of their house wondering if they need to clear brush from the south-facing slope.

“No matter your level, you will gain by being hands-on.”

Kelli said that as advice to other leaders. Coming from someone who went from zero prompts to evaluating AI platforms for wildfire safety, it carries weight.

Listen to the full conversation: Episode 32, The AI Product Leader

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Polly Allen is the founder of AI Career Boost and host of The AI Product Leader podcast. She spent years leading AI at Amazon Alexa before building the AI Career Boost Blueprint, an 8-week program for Director+ product leaders becoming indispensable AI player-coaches. Subscribe to The AI Player-Coach newsletter →