Now I build AI products while keeping my day job as VP—and I write about the journey. The wins, the failures, and everything in between.
Two production platforms and a professional course, built solo while maintaining my day job. Real users, real metrics, real learning.
From Workflow to Operating Model
Research-Driven Podcasts, Your Control
Intelligence Over Execution
Thoughts on AI, learning, and building
DIALØGUE supports 7 languages, but the real multilingual work was not translating strings. It was fixing audience-local dates, TTS consistency, UI language drift, and deciding where quality mattered enough to slow down.
I spent years in advertising watching teams confuse motion with progress. Then I started building AI marketing tools and realized the problem was getting worse: faster execution, weaker judgment.
AI can now produce media plans, performance summaries, measurement frameworks, and campaign setups at impressive speed. The problem is not that the output is obviously bad. The problem is that it is often good enough to pass a casual review while missing the business context that actually matters.
Most teams still ask which model to use. From my experience, that is no longer the main question. If your AI system forgets the client or the brand, the category, and what good looks like, the smartest model in the world still starts every conversation from zero.
In 2023, I thought generative AI would flood search with cheap content and reduce the return on SEO. Three years later, that happened. But the bigger shift is that content production is no longer the moat. Structure, trust, QA, localization quality, and answer-engine visibility are.
One person. Seven modules. Three hours of video. Sixteen templates. A custom slide pipeline with 18 layout types. Professional voice clone. All while keeping my day job as VP. This is what the AI-first operating model looks like when you apply it to yourself.