AI marketing engineering at Pendo
Designing and implementing practical systems across AI search, content, data, and automation to make modern marketing work more connected, measurable, and useful.
The opportunity
Modern marketing teams have no shortage of tools, data, ideas, or AI experiments. The harder problem is connecting them into systems that make the work better.
My focus at Pendo is the connective layer: identifying where AI, search, content, analytics, and automation can reinforce one another, then turning those opportunities into practical workflows.
The approach
The work begins with the real process—not the tool.
That means understanding the people involved, the decisions they make, the information they need, the handoffs that slow them down, and the subject-matter expertise that must remain part of the system.
From there, the implementation can take many forms: a research workflow, an automation, a new way to structure content, an analytical model, or a lightweight internal tool.
What I am learning
The most durable AI systems are not the ones that remove people from the process. They are the ones that make human expertise easier to apply, preserve, and scale.
This public case study intentionally omits confidential details and internal results. The purpose is to document the method: connect the disciplines, build around the real work, measure what changes, and refine from evidence.