TL;DR: Adopting AI at work without breaking your business comes down to four moves. Use AI to amplify the boring, repetitive work that drains your people, not to replace the judgment they get paid for. Identify the edge cases before you deploy. Keep a human in the loop wherever a mistake costs money or trust. Retrain your existing people on the new tools instead of firing them. Here’s how I use AI without the crap. The companies that follow this pattern win the next decade.
The ones that confuse “AI can do this task” with “AI can replace this job” learn the hard way, publicly and expensively. Klarna and the chatbot at your doctor’s office are the same disease at different scales.
I called my doctor’s office the other day to leave a message. The new AI chatbot picked up and launched into a two-minute uninterruptable speech about how it was going to help me. When that finally ended, it started a one-minute one.
Then more after that. Five minutes every call, every time, before I could get to a human or even leave a message.
The old phone tree was clunky. This was worse. And somebody at that medical practice spent real money to make it worse.
That clinic isn’t the exception. It’s the pattern. Companies are rolling out AI everywhere, and a lot of those rollouts are quietly hurting the businesses paying for them.
The fintech giant Klarna laid off 700 customer service workers and replaced them with an OpenAI-powered chatbot, then started rehiring humans when the CEO publicly admitted the AI produced “lower quality” service. Same disease, different scale.
Not because AI is bad. Because nobody asked the boring question first: where does this actually belong in our work, and where does it ruin things if we put it there?
I’ve been around long enough to watch this happen before. Different technology, same mistake.
AI Is an Amplifier, Not a Fixer
The single most useful thing I can tell you about AI at work is that it doesn’t fix anything. It amplifies. Whatever you feed it, good or bad, comes back louder. Give it clean data and a clear question and it gives you something useful fast. Give it a mess and it gives you a bigger mess, delivered with total confidence.
This is why so many AI projects fail and why the failure gets blamed on the AI. The model did exactly what it was told. It took the garbage it was handed and produced more garbage, faster than any human could. The problem was never the tool. The problem was upstream, in the data, the process, or the thinking, and the AI just made that problem visible at scale.
Why Does AI Amplify Bad Data?
Most companies have worse data than they think. Records that contradict each other. Fields filled in three different ways by three different departments. Spreadsheets that a person can read with context but a machine reads literally. Point an AI at that and it doesn’t flag the mess. It averages it, guesses at it, and hands you an answer that looks authoritative and is quietly wrong.
A human doing the same task would stop and say this doesn’t add up. The AI doesn’t stop. It fills the gap with the most likely-sounding text and moves on. That confidence is the dangerous part. A wrong answer that sounds unsure gets checked. A wrong answer that sounds certain gets shipped.
Vague Process In, Automated Confusion Out
The second source of crap is process nobody can actually explain. Ask most teams to write down exactly how they make a decision and they cannot. They do it by feel, by experience, by a hundred small judgments they have never put into words. That works when a person does it. It falls apart the moment you try to hand it to a machine.
If you cannot describe the process clearly, the AI cannot follow it. It’ll pick a version, usually the simplest one, and automate that. Now you have a fast, consistent, wrong process running at volume. The AI didn’t break your workflow. It exposed that your workflow was never really defined.
Vague Questions In, Vague Answers Out
From the podcast
Diana Lee teaches a four-part structure for exactly this, and describes the missing piece as context you’d give any advisor, on Leaders and Their Stories:
The variables piece is giving it the context that it needs to know about you, as you’d if you were to work with an actual consultant or an advisor.Nobody expects a consultant to produce good work from a one-line brief. The machine gets held to a lower standard, and returns work to match.
The third source is the question itself. People ask AI broad, lazy questions and get broad, lazy answers, then conclude the AI isn’t smart enough. Ask it to make our marketing better and you get generic filler. Ask it a sharp, specific question with the real constraints included and the quality jumps.
This is a skill, and most people haven’t built it yet. The quality of what you get out is capped by the quality of what you put in. That’s the same rule that governs every tool ever made. A better saw doesn’t make you a better carpenter.
What Should You Fix Before Adopting AI?
Before you blame the model or shop for a different one, look at the three inputs. Is the data clean and consistent, or are you feeding it contradictions? Can you write down the process in plain steps, or does it live in someone’s head? Are you asking precise questions, or vague ones and hoping? Fix those and most of what people call an AI problem disappears.
I’ve watched companies spend months and real money trying to make an AI project work when the actual fix was a week of cleaning up their data and defining what they wanted. The AI was fine. The crap around it was the problem. Get the inputs right and the tool does exactly what it was supposed to do all along.