I work at the intersection of business strategy, technology, AI, data, and transformation.
Most of my work has been about taking something ambiguous, complex, or underperforming and turning it into something that works better — a product, a system, a capability, a team, or a way of operating.
I’m particularly interested in the space between strategy and execution, because that is where most good ideas either become real or quietly die.
Martin Cervantes
I like problems that are still messy. The kind where the technology is only part of the answer, the operating model is unclear, the data is imperfect, and nobody has a ready-made playbook. That is usually where I add the most value: creating structure, forcing the right decisions, and moving from an idea or business need into something that can actually be built, adopted, and scaled.
I have very little interest in strategies that only work on a slide.
Technology matters when it changes how a business performs. That can mean better decisions, faster workflows, stronger capabilities, better customer experiences, or entirely new ways of operating. I’m less interested in what a technology can do in theory and more interested in what changes once people actually have to use it every day.
What changes once people actually have to use it every day.
A large part of my career has been spent in consulting and transformation, but I’ve always gravitated toward the build side of the work. Products. Analytics engines. AI-enabled tools. Digital capabilities. Operating models. Teams. There is a big difference between recommending something and being close enough to the implementation to see where the recommendation breaks.
I prefer the second one.
Built and scaled a digital transformation capability from the ground up, combining methodology, delivery, commercial development, and team building. Over time, it grew from a new capability into a meaningful part of the business.
Designed a decision-support system that turns fragmented ERP and inventory data into practical replenishment recommendations. The hard part was not forecasting demand. It was deciding what the forecast should actually make someone do on Monday morning.
Worked on AI-enabled products designed to improve how teams access information, structure decisions, and execute recurring workflows. I’m skeptical of AI products that are impressive for five minutes and useless after two weeks. The model matters, but the harder work is usually everything around it.
I’m particularly interested in the gap between things that sound right and things that actually work.
That gap is usually where the interesting lessons are.
I write about:
I started my career as an engineer and eventually moved into consulting, digital transformation, technology, and product work.
That progression was less planned than it may look in hindsight. The common thread has always been the same: I like understanding how complex systems work, finding where they break down, and figuring out how to make them better.
Technology, software, AI-enabled products, and transformation. Outside day-to-day work, I spend time exploring how AI changes product development, how teams operate, software building, and technology leadership.
I like testing ideas by building things, not just reading about them.
I’m always interested in meeting people working on difficult and interesting problems — particularly around technology, AI, software, analytics, transformation, and building new capabilities.