How I approach practical AI workflows, MCPs, skills, agentic systems, and operating memory for marketing and analytics work.
AI operations are not about adding a chatbot to a process.
The real leverage comes when workflows become repeatable, tool-connected, governed, and improved over time. That means the system needs memory, permissions, evaluation criteria, and a clear boundary between what the model can do and what a human should decide.
How I approach it
The useful unit is the workflow:
- what decision or task repeats
- what context the model needs
- which tools it can call safely
- what instructions and examples preserve judgment
- what outputs need review
- what should be logged, evaluated, and improved
MCPs, skills, agents, and cloud runtimes matter because they can make AI work operational instead of conversational.
Where this connects
AI operations connect to analytics, SEO, tracking, reporting, and internal enablement. The goal is not to look automated. The goal is to make high-quality work easier to repeat.