After three decades in IT, I’m now watching the fifth major innovation cycle of my career: I’ve seen PC networking, internet/dotcom, enterprise integration and automation, cloud and mobile, and now AI. As this next era takes shape, becoming an analyst with Forrester felt like the logical next step. I’m moving from building products to helping others navigate complex technology decisions. My research will focus on cloud-native development, AI-enhanced development, edge platforms, and application modernization.
Bringing A Diverse Skill Set To The Forrester Team
I bring both product management and engineering leadership experience across commercial software and mission-critical enterprise applications.
In my most recent role, I was a director of engineering at a major fintech company, working on workflow automation, low-code configuration of complex business logic, cloud technology selection, global operations, and enterprise integrations.
Before that, I spent nine years at a commercial software company focused on contract management, procurement, and source-to-pay workflows. As the company changed ownership and products were acquired, my team replatformed the product for SaaS/cloud and mobile-first portal architectures.
From 2006 to 2013, my career focused on business process management, now more commonly called digital process automation. My team built a platform that combined complex event processing, rules engines, and dynamic workflows to route tasks based on changing business conditions. Many of those concepts now show up in agentic and adaptive workflow patterns.
My Back Story: Looking For Architectures Before They Become Obvious
I visited a data center with my uncle when I was a kid. I watched him connect to computers around the world. In the time before cellphones and PCs, that felt like magic. That early experience led to a lifelong curiosity about technology.
My career began in the US Air Force repairing complex electronic systems. After leaving the military, my hobby of building PCs evolved into network administration and eventually software engineering. Before long, I was building B2B and B2C websites during the height of the dot-com boom.
I’ve had the privilege of seeing much of the IT industry develop, one layer at a time. Each innovation cycle starts as hype, then either fades or becomes part of the foundation for whatever comes next. That same curiosity from my younger days keeps me looking for the architecture choices that matter before they become obvious. My role now is to bring that long-term view to the current transition to help clients separate hype from the decisions that create real ROI.
Cloud-Native: Less Hype, More Foundational For The Age Of AI
The scope of cloud-native has expanded well beyond its original roots in microservices and serverless. What began as a way to build distributed systems is now a development approach for building software that is modular, resilient, observable, and easier to evolve. Today, the deployment model may be cloud, hybrid, or on-premises, but cloud-native development is about designing software as portable, connected services that can run consistently at scale.
We are entering the next phase of the AI supercycle, in which cloud-native development and AI start to converge. Use cases are moving away from AI bolted on to existing apps and toward an agentic world where workflows span applications, agents, models, data, and infrastructure.
Cloud-native development gives AI systems a better path to production because it brings discipline around deployment, scaling, monitoring, and recovery. Those capabilities matter when AI workloads are dynamic, expensive, and tightly connected to business workflows. Without that discipline, AI integrations can become fragile layers in an already complex application stack.
AI workloads will not live in one place. Larger models may handle general-purpose reasoning and knowledge tasks, while smaller, tuned models can run closer to specific workflows, data, users, or devices at the edge. That split can improve performance, support data sovereignty, and reduce exposure of sensitive IP to external model providers. As small language models move closer to the edge, cloud-native development patterns become more important for packaging, deployment, monitoring, and control.
Cloud-native development may get less attention as the AI story gets louder, but that is usually what happens when a technology becomes foundational. It moves into the background and becomes part of how teams build systems that can survive the next wave. Less hype, more foundation.
I’m Not Always Nerdy
Outside of work, I enjoy a wide range of activities. I have been a private pilot for 20 years. Recently I’ve been shifting toward more down-to-earth hobbies like skiing, kayaking, and riding motorcycles. And of course, spending time with my family is always important.
Let’s Connect
I’m looking forward to connecting with you and discussing how to build modern software that not only integrates with AI but will stand the test of time. If you’re a Forrester client, feel free to schedule an inquiry or guidance session with me. If you’re a vendor or service provider for the cloud or edge, please schedule a briefing with me. Looking forward to the conversation.
