AI is everywhere. The infrastructure isn't.
An AI Operations function built from scratch turned scattered adoption into consistent output and measurable results.
The Challenge
AI tools arrived fast. Strategy didn't. Most companies jump straight to tools without building the systems or processes to make them compound. Adoption happens in pockets. Output is inconsistent. Nobody could answer: what are we actually getting from this? The capability existed. The infrastructure to scale it did not.
The Approach
Built an AI Operations function from scratch using Claude as the foundation — starting internally before replicating the model for clients. Embedded institutional knowledge through structured skill files and role-specific standards so output stays consistent at scale. Established a sprint-based process to surface what's worth automating, then built purpose-built agents to replace the repeatable, high-cost tasks across content, sales, and ops.
The infrastructure to scale it did not.
The Results
High impact AI agents built
Time saved per team member
2 FTEs avoided – $200K in payroll
Time to measurable results
