Better AI Prompts Start With Better Processes

"If I talked to you the way I talk to my AI, it would be domineering. Brutal. Constantly dissatisfied. You would never be good enough." That is not how most people prompt. Most people bring the same conversational style to AI that they bring to a direct report: leave context unstated, assume it will read between the lines, and get frustrated when it does not. They talk to it like a colleague. Intentionally or not, they expect it to fill in the gaps. AI will not fill in those gaps. It is a machine. A different kind of machine than most people realize, but a machine nonetheless. The leaders who get real, repeatable results from AI are not using better tools. They are working from a different mental model.

AI Is a Machine. Here’s What That Actually Means

A machine converts input to output, that part is simple enough. What most people miss is how many inputs they are actually sending. When you type a message, you are not just sending words. You are sending tone, assumptions, emotional register, and every contextual signal embedded in how you framed the request. AI processes all of it. "They don't think of it like a machine," says Collin Russell, who builds AI processes at Cortado Group. "They don't recognize all of its inputs." Thinking of AI as a T-2000 or a microwave is a mistake, but on the right track. AI doesn’t feel or intuit its way through communication as people do, but it does its best to appear as such. It’s a machine that emulates social norms, reads your mood, mirrors your tone. Prompt with frustration and you tend to get output that reflects that register. Prompt with precision and you get precision back. This happens because tone is an input, and this machine is highly responsive to its inputs. Problems arise, not when people treat AI like a person - but when they do so unconsciously, without realizing that tone, framing, and unstated assumptions are shaping every response. Think of two people navigating a bog. The one who has never been through it swings from vine to vine, staying safe but slow. The one who knows the terrain takes larger leaps faster because they can see where they are going. An experienced prompter states the method, names the risks, and asks for a draft before the final output. A novice says "can you make this thing for me?" and waits. The tool is the same - but the inputs differ tremendously, and the mileage reflects that.

Give It One Bounded Job

The most common AI implementation mistake is giving AI too much latitude. "The more choices you let AI make, the more chances it makes the choice you wanted least." When AI has to fill gaps, it will do so with variable quality. The choices it makes are not random, but they are not yours either - they are the statistically likely ones, which is not the same as the correct ones for your context, your standards, or your clients. Tighten the constraints before AI acts, rather than adding oversight after it has already gone wrong. AI should make what Collin calls "soft leaps of fuzzy logic that a normal machine can't." It should handle decisions that require contextual judgment. Everything else should be deterministic: one script, all fields assigned, no open-ended choices. When you are not getting results you can work with, and you think you are already being specific enough, ask AI why it made the choices it did. Use it as a diagnostic: why did you add this section? Why did you format it this way? AI can usually surface its assumptions, and when it does, you get the language you need to constrain it more precisely next time. Extracting specificity from the AI is part of being specific with the AI.

What This Looks Like in Practice

Publishing blog posts to our previous WordPress site was taking one hour per post. The interface was labyrinthine: too many fields and switches, too much to look at, a massive exertion of mental energy just to get text published. It was the kind of work that wears people down slowly. The solution was not asking AI to write the content or manage the publishing workflow. The solution was giving AI one bounded job: take raw text from a Google Doc, segment it by field, and run a script that posted it to WordPress with every field assigned. AI ran one script. All fields were mapped. It did not browse, guess, or make open-ended decisions. The map told it where everything went. AI did not decide. One hour became six minutes, reliable and repeatable every time. "It worked as I expected it to, which is a simple answer, but that’s the basis of every good tool: it works as expected.." The bar was not “impress me”. The bar was: works as expected, every time. That is the template. Find the process that is broken and slow. Define all the fields. Give AI one bounded job within a structure you control. One additional principle is worth naming: AI is not the best, nor only, solution for broken processes. Sometimes AI can help you identify what is broken before you decide how to address it. The rule is to avoid letting AI introduce more points of failure than it removes.

What Breaks When You Scale

Our same WordPress build worked for the people who designed it. Adding a second person to any AI-assisted process surfaces the same problem almost every time: "We don't have it written down." A new team member joins and nobody has documented the merge procedures for the codebase. Two days pass in confusion, and three weeks go by before the workflow fully normalizes. A consultant is asked to use "our format" for a client deck. She finds something in a shared folder, assumes it is correct, and submits the wrong deliverable. The process that worked fine for the person who built it breaks for the next person because half the rules existed only in one person's head. When a process fails at the handoff, the instinct is to blame the skill of the person who failed to execute. The correct diagnosis is usually adherence: resources were available, someone else had done it cleanly… the new person failed anyway. That is an adherence gap, and it has two causes. The process is too hard to follow, so people skip steps: make it easier. Or the person does not understand the terminology or the standard: find out what they do not understand and explain it. For load-bearing outputs, a third consideration applies. A statement of work, a client deliverable, a document that represents your professional judgment: these do not get handed to AI alone. "There are a lot of things an AI can miss that a human will intuitively look for." The person who says "AI drafted it" when a client finds a mistake loses credibility in that moment. The 20% of expert judgment that AI cannot replace is what makes the work look like you know what you are doing.

The First Move

The implementation failures costing your team the most hours are documentation failures. The operations leader who thinks their AI process is underperforming usually has a process that was never fully written down. Writing down what good looks like before automating it is the actual work, not obsolete overhead. For the marketing leader whose AI tools produce inconsistent output: the problem is almost never the tool. The decision space is too wide. Narrow what AI can choose, and quality becomes predictable. Resist starting with the most exciting AI opportunity: find where an expert wastes an hour doing something that should take six minutes. Define what good looks like, and write it down. Give AI one bounded job within that definition. Congratulations, you’ve successfully implemented a practical AI solution!