Technology Strategist. AI Advocate. Mentor. Builder. Lifelong Learner.

From Talking About AI to Actually Using It

Today, much of my focus is on Artificial Intelligence—particularly AI adoption, Local and On-Device AI, AI agents, workplace AI, automation, and the rapidly evolving relationship between AI software and the hardware that powers it.

For many organizations, the challenge is no longer simply understanding what ChatGPT, Copilot, Claude, Gemini, or the latest AI model can do.

That means understanding the models, but it also means understanding the infrastructure, devices, data, security, workflows, people, and business processes surrounding them.

I spend a considerable amount of my own time experimenting with these technologies hands-on. I build and containerize Local AI environments, test models across different hardware configurations, explore memory and compute requirements, experiment with local inference and open-source models, and build AI agents, automation workflows, and multi-agent architectures.

Sometimes the experiment works beautifully. Sometimes it doesn’t. Both are valuable. Understanding AI requires more than reading about what is possible—you have to build with it, test its limitations, and understand where it actually provides value.

The Next Frontier: AI Moves to the Device

One area that particularly interests me is the movement of AI from the cloud onto the devices we use every day.

PCs, workstations, edge devices, servers, and other computing platforms are evolving rapidly. CPUs, GPUs, NPUs, memory architectures, and increasingly powerful AI-capable systems are making it possible to run models and intelligent applications locally.

Local AI can provide greater privacy, lower latency, offline capabilities, more control over data, and entirely new ways of integrating AI into everyday workflows.

I believe the next generation of technology leaders will do more than provide AI-capable devices. They will help customers understand why those capabilities matter, where they create value, and how to put them to work.

That intersection—between hardware, AI, people, adoption, and business outcomes—is an area where my experience and interests naturally come together.

Beyond the Chatbot

We are moving beyond the idea that AI is simply a chatbot sitting in a browser waiting for a prompt. The next generation of AI will increasingly involve personal assistants, workplace copilots, AI agents, agentic workflows, multi-agent systems, and local or hybrid AI architectures.

Instead of simply answering questions, these systems can increasingly understand context, use tools, interact with applications, access approved information, perform tasks, and participate in workflows.

That changes the conversation. Organizations must think about architecture, security, governance, data, hardware, integration, user experience, and perhaps most importantly—adoption.

Technology Is Only Valuable When People Use It

After decades in technology, I’ve learned that deploying technology is often the easy part. Getting people to understand it, trust it, adopt it, and use it effectively is much harder.

People approach AI from very different starting points. Some are excited. Some are skeptical. Some are concerned about privacy, security, ethics, or their jobs. Others simply don’t know where to begin.

That’s why I believe AI enablement needs to go far beyond teaching people how to write better prompts. People need to understand when AI is useful, when it isn’t, how to evaluate its answers, how to recognize hallucinations, how to protect sensitive information, and how to integrate AI responsibly into the way they work.

My role has often been to sit between technology teams, business leaders, sales organizations, partners, and customers and translate what is happening into something understandable, practical, and actionable.

The Art of the Possible

One phrase I frequently come back to is “the art of the possible.”

Emerging technologies often arrive before most organizations understand what to do with them. Someone needs to experiment. Someone needs to connect the pieces. Someone needs to translate technical capability into business value. And someone needs to show people what is possible.

Throughout my career, I’ve played many different roles—developer, consultant, strategist, technical pre-sales leader, mentor, educator, program leader, solution seller, and advisor.

That variety is one of my greatest strengths. It allows me to move comfortably between technical conversations and business conversations while keeping the focus on the people who ultimately need to use the technology.

Responsible AI Matters

I’m excited about AI, but I’m not interested in technology simply because it is new. AI needs to be implemented thoughtfully.

Organizations also need people willing to ask “Should we use AI here?” and not simply “Can we?”

I believe the best AI strategies balance innovation with responsibility while keeping humans firmly at the center. AI should help people make better decisions, learn faster, gain access to expertise, solve problems, and accomplish things that previously required significantly more time or resources.