Distilled conclusions.
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The Conversation This Book Is Really About
Over the past year, I’ve had the same conversation with at least thirty CTOs and VPs of Engineering. The setting changes: a conference hallway, a video call, a dinner after a workshop. The words change. But the conversation is always the same. It starts with a number. “Our developers are 40% more productive.” Or 30%.…
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I Wrote a Book: Reimagine, Don’t Retrofit
I’ve been building software since I was fourteen, when I sold my first software product. Nearly three decades later, having risen through consulting and architecture roles, co-founded companies, and eventually become a CTO responsible for the delivery strategy of hundreds of cloud projects across Latin America, one pattern has stayed constant. Every major platform shift…
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Why AI Fails on Your Existing Codebase: The Case for Code Elevation
A few weeks ago, a team I was advising tried something that sounded perfectly reasonable. They had an existing e-commerce platform, about five years old, built as a modular monolith with a few extracted microservices. The business wanted a recommendation engine integrated into the product catalog. The team had been using AI coding assistants for…
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The Developer’s New Role: What Happens When AI Drives the Conversation
Last month, I was facilitating a Mob Elaboration session with a development team at a company going through its first AI-DLC adoption cycle. The Product Owner had stated the intent: a new feature for their customer onboarding flow. Within minutes, the AI had generated an initial set of user stories, acceptance criteria, and a proposed…
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Reimagine, Don’t Retrofit: Why AI Needs a New Development Lifecycle
A few months ago, I sat in on a retrospective with a development team at a mid-sized financial services company. They had adopted AI coding assistants six months earlier. The mood was… complicated. On one hand, individual developers were writing code faster than ever. On the other, the team’s sprint velocity metric had become meaningless.…
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Multi-Cloud Reality Check: Beyond the Marketing Hype
Few topics in enterprise technology generate as much passionate debate as multi-cloud strategy. Vendors promote it as the path to freedom. Analysts present it as inevitable. CIO roundtables whisper about leverage and resilience. Conference speakers declare it the future of enterprise IT architectures. And yet, after years of supporting cloud transformations across regulated and non-regulated…
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The GenAI Governance Gap: Why Enterprise AI Initiatives Fail at Scale
Over the past two years, Generative AI has moved from experimentation to boardroom priority. Executives are no longer asking whether they should adopt GenAI, but how fast they can deploy it across the organization. Customer service copilots, internal knowledge assistants, automated document processing, developer productivity tools, and decision support systems are appearing everywhere. And yet,…
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How Financial Institutions Can Build Cloud Operational Readiness in 90 Days
In my previous post, The Hidden Cost of Unpreparedness, I described the challenge facing a financial services company migrating 180+ servers without the operational foundation to succeed. The risks are real: cost overruns, security gaps, compliance failures, and burned-out teams. But here’s the question I keep hearing: “If we need to build this foundation, how long will…
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The Hidden Cost of Unpreparedness: Cloud Migration Without a Foundation
Recently I was working with a financial services company on a cloud migration project. The scope was significant: over 180 servers, multiple business-critical applications, and tight regulatory requirements. On paper, it looks like a standard enterprise migration. But there was a problem, one I’ve seen too many times before. The customer hasn’t prepared for what…
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From Vibe Coding to Spec-Driven Development: The Next Leap in AI Software Engineering
In my last post, “The Mismatch Between AI Code Assistants and Enterprise Software Development,” I talked about how coding assistants (despite their brilliance) often fail to align with enterprise reality. They generate code fast, but without context, structure, or shared understanding. The result? More rework, more debugging, and less trust. Today, I want to explore where…
