Context
AI entered my work as an operations question, not a novelty tool. The goal was not to replace writing, reporting, or editorial judgment. The goal was to reduce friction in the parts of the workflow that were repetitive, scattered, or slow to structure.
In a complex B2B payments environment, weak AI use creates obvious risk: generic claims, unsupported language, product confusion, and work that sounds polished but does not hold up. I built workflows around the opposite principle: use AI to speed structure and synthesis, then keep the final standard with a human editor.
System designed
Editor review points
The point of the workflow was not to let AI make editorial decisions. It was to make better editorial decisions easier.
- Confirm source quality before drafting from it.
- Separate verified facts from useful but unverified direction.
- Check product claims, customer proof, and audience fit.
- Keep brand voice and final edit with the human owner.
- Use agents to speed repetitive work, not to decide what is true.
What improved
- Scattered inputs could become a usable brief faster.
- Writers and reviewers had clearer handoff materials.
- Optimization work became easier to repeat across pages.
- Image and creative requests had more specific guidance up front.
- Final QA stayed attached to the editor, not the tool.
Why it mattered
This is a leadership capability because the value is not in using a tool faster. The value is in designing a safer, more repeatable system for how content teams use AI without lowering the standard.
For complex organizations, AI only helps if the workflow protects accuracy, context, voice, source quality, and business judgment. My role was to build that structure: practical workflows, clear boundaries, and human ownership of the final work.
Tools and systems
Glean, ChatGPT, Claude, Perplexity, Microsoft Copilot, Gemini, NotebookLM, Asana, Notion, Contentful, SharePoint, Netlify prototype workflows, prompt packets, source checks, and editorial QA.