AI Transformation

Positioned Best Friends Animal Society for AI adoption before most of the organization knew it was coming — building the pilot, the governance framework, the education program, and the measurement infrastructure that now form the foundation of org-wide AI strategy. Secured nonprofit pricing with multiple AI vendors, launched org-wide AI Essentials training, and stood up the organizational AI Council — all in the span of a year.

The Setup

AI transformation doesn't start with AI. It starts with data. Before any of the organization-facing work began, I was making the case internally that our data governance and quality efforts weren't just operational hygiene — they were the prerequisite for everything AI would eventually promise. If the data isn't governed, complete, and reliable, AI doesn't make it better. It makes it faster and wrong.                                                                 

That framing shaped everything that followed.                                                                                                                                                                                       

The Pilot

I advocated for a small set of Claude licenses so my team could start learning — not through a formal program, but through structured experimentation. We defined the types of prompts we'd explore, tracked time saved and response quality, and shared findings with each other weekly. What worked, what didn't, what we'd want to try next.

The pilot grew. Ten people became twenty, then forty — spanning beyond my team as interest spread across the organization. That growth wasn't accidental. The weekly sharing sessions created visibility, and visibility created demand.                                                                                                               

AI projects from across the organization started finding their way into our Data Friends Showcases, which became an informal but powerful venue for surfacing what people were building and learning.

The pilot also produced something concrete: a documented case for nonprofit pricing. I led the vendor evaluation —  tracking time-to-insight, response quality, and use-case fit — and used those results to secure nonprofit pricing with multiple AI vendors. Claude access has since been extended to anyone in the organization who wants it. That single change has accelerated organizational AI maturity faster than any program we've run, because people started building their own tools rather than waiting for centrally engineered solutions.

The Execution Group

 As organizational interest grew, I was asked to be a founding member of the AI Execution Group — a cross-functional team bringing together innovation, marketing, IT, and my team to figure out how to scale AI adoption responsibly. The innovation and marketing leads were focused on culture. My focus was structure: governance, policy, and measurement.

We were building an AI education program at the same time the organization was launching a new LMS. I led the content curation process and ultimately made the case that generic AI content wasn't what the organization needed. We needed content specific to Best Friends, our culture, and our policies. That meant building it.                              

It also meant discovering where policies didn't exist. I helped draft AI use policies and guidance documents, filling gaps that the education program surfaced as we went.

In March 2026, AI Essentials Training launched org-wide — the first structured AI literacy program in the organization's history.

The Measurement

 One of my consistent contributions throughout has been insisting on measurement. Before the education program launched, I established baseline data on AI literacy and comfort across the organization. That baseline has since been used in multiple contexts — to track program impact, to make the case for continued investment, and to identify where resistance or confusion remains highest.                                                                             

I've also taken on the role of external research — tracking how other organizations and industries are approaching AI transformation and bringing those insights back. What works elsewhere, what the failure modes are, and what we should be anticipating before we get there.                                                                                  

Where It's Going

 In April 2026, the organization stood up its AI Council — a formal governing body for AI policy, security strategy, and cultural integration. I joined as a founding member.      

On the infrastructure side, we're building toward a fully AI-native data environment. That includes Snowflake Cortex for running models directly in the data warehouse, a transition of external visualizations to a JavaScript-based library (improving user behavior tracking, performance, and professionalism while saving approximately $150K/year), and a migration of internal dashboards to Sigma — a platform chosen specifically for its AI capabilities and deeper Snowflake integration.

The OpenSesame licensed content that we didn't deploy at launch is now being incorporated into a multi-tiered AI literacy program — broadening access and depth of learning across the organization. I'm supporting the learning and development team as a subject matter expert for that next phase, while continuing to expand the governance framework to address what responsible AI adoption looks like at organizational scale.