Adopting AI tools is easy. Building reliable, repeatable systems with them — and documenting those systems so others can use them confidently — is the harder and more valuable work. Over the past several years I’ve developed a suite of AI-augmented workflows that address specific operational gaps, each with a human validation layer and documentation written for team adoption.
Weekly Trend Reporting Pipeline — Pulls quantitative metrics via social tool APIs and direct Snowflake SQL queries, feeds structured data into an AI analysis layer, and produces a consistent weekly trend narrative combining qualitative and quantitative findings. Human review validates AI output against known campaign context before distribution. Dramatically reduced manual production time while improving week-over-week consistency.
Brand Voice Rewrite Modules — Built Custom GPTs and Gemini Gems configured with brand voice guidelines and structured rewrite instructions. Paired with user documentation covering inputs, output evaluation, and editing checkpoints — enabling team members to produce on-voice content independently and at speed.
Production AI Validation for Customer-Facing Care — A live AI advisory layer that reviews tone alignment and response-to-issue fit on customer-facing care responses before they send. In-suite AI is the default validator; Claude Projects serves as a secondary backstop for complex or ambiguous cases and for agents still building fluency. Advisory, not blocking — agent judgment stays in the loop. The same workflow underpins the brand voice translation layer described elsewhere on this site.
Built a standalone social data dashboard in Replit, connected to social listening tools via secure API, delivering live performance readouts to stakeholders without requiring platform access or BI tool expertise.
The common thread: AI accelerates and scales human judgment, it doesn’t replace it. Each workflow is designed with that principle explicit — and documented so the system survives beyond any single operator.
