How Knowing Every Job Helped Me Build Better AI Agents
Why Understanding Every Role Made Me Better at AI
Most people run from complexity. I ran toward it—especially in companies.
I wasn’t one of those founders who escaped corporate life to become “free.” I actually wanted to be inside the machine. Not to do every job, but to understand every role—how they moved, collided, and relied on each other. That obsession with cross-functional clarity turned out to be my unfair advantage in the world of AI agents.
The Core Problem: Fragmentation Kills Flow
Companies don’t break because of a lack of talent. They break because teams don’t talk. Or worse, they talk without understanding each other.
Sales pushes one thing, product builds another, marketing spins a third. I’ve seen it happen in enterprise orgs, scrappy startups, and everything in between.
But here’s the thing: AI agents must understand context. They’re only as good as the workflows they power—and workflows cross departments. If your AI assistant doesn’t know how sales and ops actually interact, it won’t save time. It’ll create confusion.
The Backstory: My Love Affair with Roles
Early in my career, I chased roles like a collector. From sales to enablement, operations to customer success, I didn’t care about the title—I cared about the vantage point. I wanted to see how the pieces moved.
I needed to feel the ecosystem.
This made me annoying to some leaders, useful to others. But over time, it built a mental map of how systems really work—how marketing goals affect CS tickets, or how onboarding flows impact renewal rates.
This wasn’t resume padding. It was system intelligence. It became the quiet skill I didn’t know would one day help me build AI tools people could actually use.
The Shift: Enter AI, and the Lightbulb Moment
By the time I hit an AI startup, I already knew how to speak multiple department dialects. That’s what AI models needed too: not just a prompt, but context.
I got certified in AI, joined a startup for a short but potent stint, and started building. Agent after agent, I kept hearing the same feedback: “This actually works for my workflow.”
That’s when it clicked: building great AI agents isn’t about technical firepower alone. It’s about human systems. You can’t automate what you don’t understand.
Enablement—my first love—wasn’t a side note. It was the secret weapon.
The Solution: Why Interdependencies Make or Break AI Adoption
What most AI consultants miss? The soft tissue. The interdependencies between teams.
An agent that handles support tickets but ignores billing workflows will always fall short. A sales automation that skips CS input breaks trust downstream.
The best agents I’ve built:
Map to how departments actually function
Respect the nuance of human handoffs
Align with real incentives (not just automation for automation’s sake)
And that only works when you’ve sat in those chairs. Or at least, cared enough to understand them deeply.
Your Turn: Use Your Career Like a Compass
If you’re trying to break into AI or make sense of agent-building—start where I did.
Inventory your past roles. What cross-functional experiences do you have?
Map interdependencies. Ask: What team did my work impact most? Who followed my tasks?
Build from workflows, not features. The best agents start with pain points, not tools.
This mindset is what separates shiny demos from real ops breakthroughs.
CTA: Want to Build an Agent That Actually Works?
If you’re a person tired of AI fluff and ready for systems that save hours, let’s build yours together. Book a 1-hour workshop—we’ll build your first real agent, mapped to your workflows.
Let your experience be your edge. You don’t need a PhD in AI—you need pattern recognition, empathy, and the guts to zoom out.
That’s what makes great agents. That’s what made mine work.