AI Workflow Systems

One of the more significant shifts in how I work over the past few years has been building AI into the production process. Not as a novelty, but as actual infrastructure that changes what a small team can output.

This page walks through three of the core workflows I've built and use daily.

Sprint Setup and Dependency Mapping:
Setting up a 50-task sprint manually, mapping dependencies, linking tasks, and setting due dates relative to a ship date used to take over an hour. I built a workflow using Claude and Jira automation that does it in about 15 minutes. The prompt structure accounts for task type, dependencies, review time, and hard delivery deadlines. The output is a fully configured sprint ready to execute.

Campaign Execution Pipeline:
When a product update or release comes in, there's a standard set of things that need to happen. News post, social copy for each platform, image direction, posting schedule. I built a templatized prompt workflow that takes a changelog or brief and produces a complete campaign package. What used to take a team member the better part of a day now takes a fraction of that.

Executive Reporting Dashboard:
Leadership visibility into what marketing is doing on any given day was a constant challenge. I built an AI-powered reporting system that pulls live data from our Jira sprints twice daily and generates a structured dashboard showing active marketing beats, task completion by team member, upcoming deadlines, dependencies, and risk flags. Leadership can check it any time without pulling me away from the work.