I see a lot of my peers trying to figure out how to use AI—without understanding it very well.

They're treating it as absolution—hand over the mess, hope to be forgiven.

But confession without behavioral change means you keep sinning and keep paying.

Given any manual process, the sequence has always been:

First, ask: can I eliminate this process entirely?

Second, ask: can I structure the inputs and write if-then-else rules to automate it?

If neither, do it manually.

That's it. That's been the discipline for decades.

People have now inserted a new option: "Can I use AI to manage this and remove manual people?"

And they're jumping straight to it—skipping steps one and two entirely.

They haven't asked if the process can be eliminated. They haven't tried to structure inputs and write rules. They've just seen a manual process and thought: AI.

This isn't innovation. It's abdication dressed up as innovation.

Not doing that is like adding rocket pack to the existing inefficiencies and complexities.

Only after this does the process qualify for AI based automation.

So when is AI actually valid?

Run it through the If-Then-Else Test.

If you can write the logic as if-then-else rules—you don't need AI. Just automate it. Done.

AI enters the picture when linguistic variation in your data is too numerous to bucket manually—when you have free-form text, not dropdowns.

AI structures unstructured data. Rules still define what happens next.

(A caveat: AI-assisted coding is different. There, AI writes code that an engineer would have written—the logic still needs to exist, AI just generates the implementation. That's a different kind of leverage, and not what this post is about.)

If you do have genuinely unstructured data:

First, ask: if I had to bucket this data manually, what rules would I apply? What would I ignore or flag for review?

Write those precise rules down.

Let AI aggregate and structure at scale using linguistic probabilities. That's what LLMs do—they make meaning from language, bucketing variant free-text into usable categories.

Then feed the structured output back into your rule-based automation.

AI does the aggregation. You do the judgment.

The business rules and decisions that follow from AI's structured output—those must be defined by you. Beforehand. Not figured out by AI.

When you throw AI at unstructured data without clear boundaries, you're asking it to exercise judgment.

That's where hallucinations start. That's where it goes off track. That's where people say "AI doesn't work."

You've abdicated thinking to a probabilistic model. And then you're surprised when it doesn't think like you.

And even when inference costs drop, you're still carrying hidden costs: security, infrastructure, rollback when it fails because the primary work wasn't done.

The mess regenerates. The bills continue. The thinking remains undone.

I evaluated multiple AI-based LMS platforms recently.

Most were ChatGPT wrappers—they knew AI, not learning.

One vendor was different. They said: "I can give you the tool, but you need to structure your process first."

The restructuring wasn't on the AI bit. It was on learning objectives, course materials, evaluation methods. Then AI handled the genuinely unstructured residue—aggregating feedback, reducing cognitive fatigue.

The others would have failed without their AI layer. This one would have been excellent even without it.

Before you turn on AI for any process, ask:

Do I have clear buckets? Do I know what business decisions follow from each bucket—before I turn it on?

If yes—AI's job is simply to sort unstructured data into those buckets probabilistically. The rules you've defined take over from there. You're using AI as a tool.

If no—you're asking AI to figure out both the structure and the judgment. That's not a tool. That's confession.

~Discovering Turiya@work@life.