AI as a Sensemaking Partner

There’s a way of working with AI that I don’t hear talked about very often. It doesn’t feel like prompt engineering, and it doesn’t feel like outsourcing thinking. It feels quieter than that. Slower, even when it’s fast. More like sitting with an idea until it reveals what it actually is.

When I use AI this way, I’m not looking for answers so much as surfaces to think against. I’m trying to understand what I believe, what I’m assuming, and what I might be missing. The tool helps, but it doesn’t lead. And that distinction has started to feel important.

What I’m Experiencing

The most valuable work I’ve done with AI rarely happens in one exchange. It starts with something unfinished. A half-formed thought. A sense that there’s something true here, but I don’t yet know how to say it. I ask the AI to help me articulate it, and then I read what comes back carefully. I notice where it assumes too much, where the tone is wrong, where something sounds impressive but doesn’t actually serve the point I’m trying to make.

So I push back. I ask it to elaborate. I ask it to remove parts. I ask it to aim at a different audience. Sometimes I realize the problem isn’t the response at all, but my own lack of clarity. The process becomes one of refinement rather than generation. The AI helps me externalize my thinking, but I remain responsible for deciding what stays and what goes.

What surprises me is how human this feels. The value isn’t in the speed of output. It’s in the way the dialogue forces me to pay attention. I can’t disengage without the work getting worse. If I abdicate judgment, the artifact becomes hollow very quickly.

What This Means for Software Development

If this way of working becomes more widespread, it has implications beyond writing or planning. It points to a shift in what we actually value in software development. Tools are getting remarkably good at producing code, tests, and documentation. They can process more context than any individual ever could. But they do not understand consequences. They don’t feel the weight of tradeoffs or the cost of being wrong in the real world.

That means the center of gravity moves. The scarce skill is no longer raw production. It’s sensemaking. It’s the ability to hold complexity, challenge confident outputs, and decide what not to ship. AI increases the volume of possibilities. Humans still decide which of those possibilities deserve to exist.

Organizations that confuse acceleration with understanding will move quickly and accumulate invisible risk. Organizations that pair powerful tools with disciplined human judgment have a chance to build software that is not just fast, but trustworthy. In that environment, the question isn’t whether AI can do the work. It’s who is willing to take responsibility for what the AI produces.

What This Means for Software Testers

For testers, this moment feels strangely familiar. Testing has always required sitting in uncertainty, asking inconvenient questions, and resisting the urge to accept artifacts at face value. Using AI as a sensemaking partner doesn’t change that. It intensifies it.

When AI generates tests, or explores code paths, or summarizes system behavior, someone still has to understand what happened. Someone still has to say, “I know what this tested, and I know what it didn’t.” That knowledge doesn’t come from the artifact alone. It comes from learning the system deeply enough to notice what’s missing.

Testers who see their role as primarily producing artifacts may feel displaced. Testers who see their role as cultivating understanding will find new leverage. The work becomes less about writing things and more about interpreting them. Less about volume and more about discernment.

A Closing Thought

I don’t think the most important question about AI is whether it can think. It’s whether we’re willing to stay engaged when it thinks quickly and confidently on our behalf.

Used well, AI gives us better drafts, better surfaces, better starting points. Used poorly, it gives us plausible nonsense at scale. The difference isn’t technical. It’s human.

I’m trying to use AI in a way that sharpens my responsibility rather than dulls it. As a developer, as a tester, and as someone who still believes that clarity, care, and judgment matter.

—

Beau Brown

Testing in the real world: messy, human, worth it.