Architected Intelligence

December 15, 2025

Over the next several posts, I want to get much more practical. I'll walk through how organizations can start using AI in meaningful ways, beginning with the quick wins: low effort, low risk, and immediate value. From there, we'll move into more integrated uses of AI that require deeper changes to processes and workflows but deliver far greater long-term payoff.

There's a lot to cover, and not every approach will make sense for every organization. My goal is to provide a clear, realistic path for leaders who want to move beyond AI hype and toward real, measurable impact. Today, I'll start with the simplest place to begin: AI chatbots.

When most people think about "using AI," what they're really thinking about is chatbots. They're the easiest place to start, require minimal technical setup, and can deliver immediate value. There are many options available, and all offer free tiers alongside paid plans. From a practical standpoint, the differences matter less than many people think; the real value comes from how you use them.

Used well, chatbots function like a tireless assistant. High-value use cases include drafting emails, reports, policies, and presentations; summarizing documents, meetings, and email threads; organizing and prioritizing work; explaining complex topics; and critiquing ideas or proposals.

These are low-risk, low-effort ways to save time immediately. Depending on your workflow, you may be able to reclaim hours each week simply by using one or more chatbots effectively.

There are also simple techniques for getting better results. One of my favorites is using two chatbots to critique and refine the other's output. This often produces stronger results and helps surface blind spots. Don't overlook conversation history either; keeping related work in the same chat thread preserves context.

AI tools like chatbots are undeniably powerful. Used thoughtfully, they can save time, improve clarity, and support better decision-making. But over-reliance (or a lack of awareness of the risks they introduce!) can just as easily create new problems.

The real challenge isn't whether AI can help, but where it makes sense, how it should be applied, and what guardrails need to be in place. That's where many organizations struggle.

In my next post, I'll focus on the other side of chatbots: the common pitfalls, risks, and mistakes leaders need to understand before scaling their use across a business.

December 8, 2025

After 30+ years in technology, I've seen waves of innovation come and go, but I've never seen anything move this quickly or with this much transformative potential.

In 2017, Google researchers published "Attention Is All You Need," the breakthrough that made modern large-language models possible. Fast forward to November 2022, when ChatGPT introduced the first broadly accessible LLM. Today, just three short years later, the landscape is crowded with capable models from dozens of providers, with each release bringing new capabilities that until recently felt like science fiction.

Modern AI can engage in interactive text and voice conversations, answer questions based on your private knowledge bases, generate high-quality video and music, and even assist in advanced areas such as drug and materials discovery. The recent emergence of AI agents has pushed things further, enabling systems to orchestrate complex, multi-step work.

The real value often comes from taking a thoughtful look at your processes to see where AI can integrate into and streamline—or even completely replace—existing workflows.

December 1, 2025

And to be fair, AI is powerful. It can write better emails, summarize meetings, support customer service, and streamline countless small tasks. But none of that automatically solves the real challenges organizations face.

Most companies have complex workflows, homegrown processes, layers of culture and subcultures, and years of operational habits wrapped around them. Some are helpful. Some aren't. But they're all deeply intertwined, and you can't simply drop AI on top of that and expect transformation.

Real improvement starts with understanding your core issues, rethinking processes, managing change well, and shaping culture. Only after that do you decide where AI fits. And when it's added in the right places, it can amplify everything else. But when AI is treated as a magic fix, expectations get inflated, trust erodes, and the real problems remain unsolved.

I believe AI is transformative, but only as part of a larger, thoughtful strategy.