About The Context Window

Enterprise AI transformation is not a tool rollout.

Companies can buy models, deploy copilots, and launch agent pilots without changing much about how work actually gets done. The harder question is whether AI can understand the business well enough to be useful inside real operations.

That is what The Context Window is about.

I am JD Fetterly. At Klarity, I work on AI Transformation: helping enterprises understand how work actually happens today so they can redesign it around a tighter partnership between humans and agents.

That work sits at the intersection of AI, enterprise transformation, process intelligence, and operating-model change. It has also given me a close look at the gap most companies have to cross. High-level process maps are useful, but AI needs a much more practical understanding of how work moves: who is involved, which systems matter, where decisions happen, and where the unofficial workarounds keep the business running.

I have helped some of the world's largest companies pull that kind of detail out of their operations, from L1 process views down to the task-level reality of how work actually gets done.

I'm a builder. A lot of what I write about comes from making things myself: local AI workflows, review loops, context graphs, content tools, and small operating surfaces that turn an idea into something usable. That builder instinct matters because enterprise AI transformation is not only a strategy problem. It is a systems problem.

The Context Window is where I write through that work.

Why follow this work

Most AI coverage stops at the product announcement, the model benchmark, or the demo. I am interested in what happens next: whether a company can use AI inside the way work actually moves.

I write about the questions leaders have to answer before AI can matter. Where does work really happen? What does an agent need to know before it can be trusted with part of a business process? Which product shifts matter for enterprise adoption, and which are just noise? How should accountability change when software starts acting more like a teammate?

The goal is to help serious readers see past the demo layer and understand what it takes to make AI useful inside real organizations.

Start here

If you are new here, start with two paths: Transformation & Context, for how enterprise work has to change for AI to matter, and Agents, Tools & Industry, for which agent, model, platform, and vendor shifts actually matter for enterprise adoption.

The premise

AI transformation will not be won by better tools alone. It will be won by organizations that can make the real structure of work visible enough for humans and agents to operate together.

That is the lens I bring here.

All views are my own.