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. More on how I apply this to books. 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 has plenty of company. Businesses are rolling out AI everywhere, and a lot of those rollouts are hurting the companies paying for them. The software doesn’t deserve most of the blame. A company that uses AI like any other tool does fine. A company that hands it a whole job and walks away ends up where that clinic did, with patients who dread calling.
The fintech giant Klarna said its OpenAI-powered chatbot was doing the work of 700 customer service agents. Then it started rehiring humans, after the CEO publicly admitted the AI produced “lower quality” service. Same disease, different scale.
AI didn’t cause that. Nobody asked the boring question first: where does this belong in our work, and where does it ruin things if we put it there? Skipping that question is lazy, and customers pay for the laziness long before the executives do. I’ve been around long enough to watch this happen before. Different technology, same mistake.
The short answer
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. For more, see six places AI will break your work. AI handles the routine. It breaks on exceptions.
- ► Keep a human in the loop wherever a mistake costs money or trust.
- ► Retrain your people on the new tools instead of replacing them. For more, see AI can write a business book. It can’t write yours. The augmented human beats the replaced one every time.
That’s the structure. The rest of this piece is forty years of receipts on why those four moves are the difference between AI saving your business and AI killing it.
Watch: 5 Strategies to Drive AI Adoption in Your Organization, AI at Work
What do most AI rollouts get wrong?
The doctor’s chatbot failed because somebody assumed a tool that handles routine questions at scale also knows when to get out of the way and let a human take over. AI is good at the first and bad at the second. The clinic didn’t think about the edge case, which in this instance was me. A patient who already knew what he wanted and just needed to leave a message. The system had no off-ramp, so every patient calling in got the long-form AI experience whether it served them or not.
The whole disease fits in one phone call. Smart tool, deployed without thinking about where it belongs, ends up making things worse than what it replaced.
The same mistake plays out at the top of the Fortune 500. Klarna, the buy-now-pay-later fintech, announced that its OpenAI-powered chatbot was doing the work of roughly 700 customer service agents. The CEO publicly claimed AI could do every job humans do.
The company projected a $40 million profit improvement for 2024 and a market story that fueled their IPO. Then customer satisfaction collapsed on anything complicated, and the same CEO was on Bloomberg admitting the AI produced “lower quality” service and that Klarna was hiring humans back. Same problem as the clinic chatbot, with two more zeros on the budget. Smart tool, deployed in a job it couldn’t do, with no humans in the loop where the customers needed them most.
I see the same mistake in writing. Someone hands me chapters their AI wrote and they want to know what I think. The chapters are technically fine. Clean grammar, no spelling errors, every paragraph sits politely next to the last one. And they say nothing. Nobody is home. The author published the machine’s first try because the surface looked clean, and now they have a book that reads dead. Same disease as the chatbot. Tool used in the wrong place.
Where does AI work at work?
Back in the eighties I led a project to automate the control system for a water district in a region that gets buried under snow every winter. The conditions were brutal.
Tanks and pumping stations scattered across hundreds of square miles. During heavy weather the remote sites were physically cut off from central command for a month at a time.
Roads closed. Lines down. Crews couldn’t reach the equipment.
The water had to keep flowing anyway.
What we built, well before anybody used the term AI, was a distributed system of remote controllers that could think for themselves.
Each remote station learned the patterns from years of weather and flow data. It knew what to do when the central system went dark. And it knew its own limits well enough to fail safely when conditions went outside what it had ever seen. The system was written in Pascal because Pascal solved the problem.
That project worked for one reason. We didn’t ask the algorithm to replace the people who understood water. We asked it to handle the routine when the people couldn’t physically get there. The humans stayed in charge of the system. The system handled the grunt work and bought the humans time to deal with the parts only humans could deal with.
Forty years later, the right uses of AI follow the same rule. I worked with a client whose warehouse staff wear augmented reality glasses on the floor. A picker walks into the aisle and sees a glowing arrow that nobody else can see, pointing at the exact box they need. They can wave a hand and “open” the box to see what’s inside without touching it. If they grab the wrong one, the system tells them in real time. Their pick rate doubled.
Nobody lost a job. The same number of people now do far more interesting work because the system handles the boring search-and-verify part.
That’s what good AI adoption looks like. The humans got more capable.
The work got less tedious. The system handled what it’s good at and stayed out of what it isn’t.
I’d make every executive shopping for a chatbot spend a day on that warehouse floor first. The system does the searching, the people still own the work, and the work got more interesting for them. Then I’d have them sit through five minutes of my doctor’s chatbot and decide which one they want their name on.
Where does AI break at work?
Now the warning side. I’ve watched smart people get hurt because they didn’t understand where the machine fails.
I’d make every executive shopping for a chatbot spend a day on that warehouse floor first.
AI fails on edge cases. Anything outside the pattern it learned. The chatbot in the medical office is a tiny version of this. A self-driving car is the big version. The bicycle wobbles unexpectedly. The pedestrian steps off the curb early. The traffic backs up for a reason the model has never seen. Now the system has to make a call it was never trained to make.
Most of the time it gets it right. Some of the time it doesn’t, and the consequences of that small percentage are enormous.
AI hallucinates. It invents facts confidently. For anyone publishing in their own name, this is brutal.
The machine will cite a study that doesn’t exist.
Attribute a quote to the wrong person. Get a date wrong by two decades. And present all of it in clean, authoritative prose. If you’re not checking every claim, you’re going to put your name on a lie. That happens to writers daily right now, and most of them haven’t realized it yet.
I don’t accept “the machine said it” as an excuse. Your name’s on the page, so the invented study belongs to you now, and so does the embarrassment when a reader catches it. AI drifts. Over a long task it loses the thread.
Whatever you asked it to do at the start bends slightly with every response, the way a tree bends in wind, and twenty pages later the document has wandered off the original ask. The longer the project, the worse the drift.
AI is shallow. It writes what most people would write about the subject. The surface looks intelligent. The depth isn’t there. It hits the common facts, makes the common arguments, reaches the common conclusion, and never says the one risky thing only someone who lived the work would know to say.
It can fake the sound of expertise. It can’t produce expertise.
And AI has no lived experience. It’s read about love and grief and combat and divorce. It hasn’t felt any of them.
For fiction or memoir, anything that asks a reader to feel real emotion, this is the fatal limit. The machine can describe the shape of an emotion. It can’t write from inside one.
| Failure mode | What happens | Example from this article |
|---|---|---|
| Edge cases | Fails on anything outside the pattern it learned | The clinic chatbot with no off-ramp for a patient leaving a message |
| Hallucination | Invents facts confidently in clean, authoritative prose | A cited study that doesn’t exist, a misattributed quote, a date two decades off |
| Drift | Loses the thread over a long task | Twenty pages later the document has wandered off the original ask |
| Shallowness | Writes what most people would write about the subject | Fakes the sound of expertise without producing it |
| No lived experience | Has read about grief and love but felt neither | The fatal limit for fiction and memoir |
Why edge cases are the game
Run those failure modes at scale across a whole industry and you get the long-term version of the problem. I wrote a book on it called The Death of Thinking. The short version is what happens when we trust machines to do the parts of our work that used to make us sharper, and end up dumber on the other side.
Every example above comes back to the same problem. The routine case almost never breaks an AI rollout. The exception does, and the exception is what ends up in the complaint, the one-star review, or the news story. The blind pedestrian. The patient who needs to leave a message.
The chapter that needs a moment of real grief on the page. The interview transcript that contains the one detail that should change the story.
The work of adopting AI well is the work of mapping the edge cases before you deploy. Where does this system meet a human who doesn’t fit the pattern? What does it do then?
Who catches it? Where does the cost of a single failure land?
If you can’t answer those questions, you don’t have an AI strategy. You have an AI deployment, and you’re going to find out the hard way.
The retraining piece
The other half of this is what AI does to the people doing the work. This is part of my Technology of Writing Hub, where I collect everything on the topic. Some jobs are going away. Not all of them.
Not most of them. But the parts of jobs that were always boring and repetitive will get automated, and the people who only did those parts will need new work.
This has happened before. It’s the industrial revolution again, faster, and leaders who act surprised by it after years of warning are kidding themselves. Blacksmiths didn’t all become unemployed when factories arrived.
They retrained and moved into the parts of metalwork that machines couldn’t do. The ones who refused to adapt had the hardest time. The ones who learned the new tools came out ahead.
I have a client in his seventies who runs AI on his own manuscript drafts. He asks the machine to flag weak chapters and find inconsistencies. He doesn’t let it write his book.
He uses it to make himself a sharper reviewer of his own work. He’s better at writing now than he was five years ago, partly because of the tool.
Fear of being replaced never seems to cross his mind. He’s too busy getting better at the work, and I admire the hell out of that.
That’s the model. Whatever your age, whatever your industry, the move is the same.
Find the boring repetitive parts of your work and put AI on them. Free up the time those parts used to take. Put the time into the work only you can do.
Get better at the human part. That’s the augmented human, and that person is going to be more valuable in five years.
How should you start adopting AI at work this week?
If you run a business or own an AI rollout, here’s the short list. Pick one boring task that drains your people. One. A repetitive, time-consuming, emotionally numbing task. That’s your first AI use case. Not the customer-facing thing. Not the public-facing thing. The internal grunt work nobody likes doing. Get a win there before you touch anything risky.
Before deploying anything that touches a customer, write down every edge case you can think of and ask what the system does in each one. If you can’t answer for the top ten, you’re not ready to deploy.
Wherever a mistake costs you a customer or could trigger a lawsuit, keep a human in the loop with veto power in real time. Someone reviewing logs after the fact doesn’t count. I think cutting that person to save a salary is one of the worst trades a business can make. One bad automated promise to the wrong customer can eat a year of savings, and you’ll end up hiring the human back anyway.
Train your people on the tools instead of around them. Show them what AI is good at, what it isn’t, and how to be the boss of it instead of letting it intimidate them. The companies that win the next decade are the ones with augmented humans. The ones that fired everyone and trusted the chatbot will spend it rehiring.
Start small and internal, map the edge cases, keep a human with veto power, and train the people you already have.
That covers the structure. The rest is showing up every day and doing the work of integrating it carefully, one process at a time, with your eyes open. (See also why redesigning the work matters more than buying the tool)
My take
Start with one boring internal task and keep your people. Most rollouts go wrong because someone mistakes a task AI can do for a job it can replace. Write down your edge cases before anything touches a customer. Put a human with veto power wherever a mistake costs money or trust, and I’d never cut that seat to save a salary. Train your staff to boss the tool. Companies that fire everyone and trust the chatbot will spend the next decade hiring back.
The takeaway
Adopting AI at work isn’t about whether to use it. That argument is over.
The companies that figure out where AI amplifies their people and where it doesn’t belong are going to outrun the ones still arguing whether to allow it at all. And the people who learn to let it augment them are going to outrun the people who treat it as the enemy.
The doctor’s chatbot will get fixed eventually, when enough patients complain and the practice realizes that making it harder to leave a message wasn’t the upgrade they thought they were buying. The warehouse with the AR glasses already figured this out. The water district figured it out forty years ago in Pascal.
Rollouts that punish customers so a budget line looks better for one quarter are short-sighted, and nobody has an excuse for them anymore.
Everybody’s seen the Klarna reversal, and most of us have sat through a chatbot like my doctor’s. Put the tool on the drudgery and keep a human wherever the stakes are real. The companies that won’t do that deserve every customer they’re about to lose.
Amplify your people instead of replacing them. The Birth of the Augmented Human shows what goes right when people use AI properly, with better output and better decisions across almost every domain.
Frequently Asked Questions
The book on this: The Birth of the Augmented Human is 212 pages on what goes right when a person works with the tools instead of against them. That’s the whole difference between adoption and replacement.
- Klarna Slows AI-Driven Job Cuts With Call for Real People, Bloomberg
- Klarna Is Hiring Customer Service Agents After AI Couldn’t Cut It on Calls, According to the Company’s CEO, Entrepreneur
- Europe’s largest fintech firm froze recruitment because its AI assistant is so good: Klarna’s AI bot does the work of 700 people, TechRadar
