Journey

Why Now

I’d worked on plenty of “agile” projects. Standups, sprints, retrospectives—the ceremonies were familiar.

But I realized I was doing agile without understanding agile. Following rituals without grasping the principles behind them.

The Learning

The certification forced me to slow down and understand:

• Why sprints work (timeboxing creates focus and feedback loops)
• When agile fits vs. when it doesn’t (uncertainty vs. predictability)
• How to adapt frameworks to context (not dogmatic about any single approach)

What Stuck

The biggest insight: agile isn’t about the process. It’s about feedback loops and adaptation.

That principle applies everywhere—not just software development. AI projects. Content strategy. Career decisions.

Connection to AI Work

AI implementation is inherently uncertain. Requirements shift as capabilities become clear. What seemed impossible last month is suddenly feasible.

Agile thinking—small iterations, fast feedback, continuous adaptation—is how you navigate that uncertainty.

The certification gave me language and frameworks to think about this more rigorously.

The Realization

I’d spent a decade building marketing systems—CRMs, data pipelines, automation workflows. The work was good, but the ceiling was visible.

Then I started experimenting with AI tools. Not just ChatGPT for writing emails, but actually building things. Automations. Integrations. Systems that did real work.

The pattern was familiar. This was systems building—just with different tools.

The Pivot

I didn’t abandon marketing operations. I reframed it.

Before: “I build marketing systems” After: “I build AI-powered business systems—marketing was just where I started”

Same skills. Same systems thinking. Different application and trajectory.

What Transferred

• Data pipelines: Understanding how data flows between systems
• Automation logic: Knowing when to automate and when to keep humans in the loop
• Integration experience: APIs, webhooks, making different tools talk to each other
• Business context: Knowing what matters beyond the technical implementation

What I Had to Learn

• AI-specific patterns: Prompt engineering, context windows, model selection
• New technical skills: Python for ML, cloud functions, different deployment models
• The AI landscape: What’s hype, what’s real, where the opportunities are

The Ongoing Journey

This isn’t a destination—it’s a direction. AI capabilities are changing monthly. The goal isn’t to master a fixed skill set but to develop judgment that adapts.

The systems thinking I built over 10 years is the foundation. AI is the new layer.