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These are the principles I use before I build anything with AI.
Not because they sound good in a framework.
Because they keep me from over-engineering something that should have been solved by looking at the basics.
Before I touch a tool, I want to know where the process actually lives.
Is it documented? Is it in a spreadsheet? Is it in a dashboard? Is it in someone's head? Is it buried in Slack messages from six months ago?
If nobody can point to the brain, that is not a tooling problem yet. That is a context problem.
AI needs context to work well. So do teams.
When someone says a process is broken, I do not start with the tool.
I ask: What are you using it for? Why does it exist? Who depends on it? What breaks if it changes? What decision is it supposed to support?
Most operational messes get worse because someone fixes the symptom without understanding the job the process was doing.
In 2026, it is very easy to reach for the big solution.
AI agent. Workflow. Automation. Dashboard. New platform. New integration.
Sometimes the answer is much smaller.
A missing owner. A bad definition. A field nobody uses correctly. A process nobody ever wrote down. A handoff that was assumed but never agreed to.
The basics are boring. They are also usually where the answer is hiding.
More fields do not mean better data. More workflows do not mean better automation. More dashboards do not mean better visibility.
More often means more places for the truth to split.
Good systems remove friction. They do not create five new places to check.
Before I build, I want a clear answer to one question:
What does done look like?
Not "what else could this eventually do?" Not "what would be cool?" Not "what if someday we also…"
Done means the original problem is solved.
You can iterate later. But if you do not define done before you start, you will keep building forever because building feels productive.
Every operational change has a blast radius.
If I change this field, what workflows depend on it? If I update this process, what reports move? If I automate this handoff, who loses visibility? If I clean this data, what system overwrites it tomorrow?
The work is not just making the change. The work is understanding what the change touches.
A tool can tell you a lifecycle stage is blank. It cannot always tell you whether that is wrong.
A tool can flag a workflow as inactive. It cannot always tell you whether that workflow is intentionally evergreen.
A tool can show you the mess. Someone still has to understand the business well enough to decide what matters.
A process is not stable because the person running it is good at their job.
It is stable when someone else can understand it, run it, and know when the answer is wrong.
If the process disappears when one person is out, the company does not have a process. It has a dependency.
The best operational work usually becomes invisible.
The handoff happens. The data is clean. The report makes sense. The right person knows what to do next.
Nobody celebrates it because nobody had to think about it.
That is the point.
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