So you want to implement AI? Here’s what really happens
Alright. You’ve heard the buzz. Everyone’s using AI now. Your competitors. Your cousin’s startup. That guy on LinkedIn who writes 10 threads a day. It feels like if you’re not building something “with AI” right now, you’re already behind. So you decide it’s time. Let’s bring AI into the company. Should be simple, right?
Yeah. No.
Let me walk you through what usually happens—because I’ve seen this movie, and it’s a mix of comedy, horror, and a bit of documentary.
Phase 1: The Hype, The Decision, and the Digital Junk Drawer
You start off excited. The energy is high. Someone says, “We’ve got a ton of data, we’ll just plug it into a model and see what happens.” That someone has clearly never opened the data folder, which is basically a digital junk drawer. You’ve got CSVs from 2016, reports labeled “final_final_USE_THIS_ONE,” files full of typos, duplicates, blanks, and no one remembers what half the columns mean. And then you realize half the data you thought you had doesn’t even exist. So now, before you even touch AI, you’re cleaning spreadsheets and chasing down missing info like it’s detective work.
Phase 2: Enter the ‘AI Guru’
Next, you hit the “we need an expert” phase. Maybe you hire someone. Maybe it’s a consultant. Maybe it’s a developer on your team who took an online course and suddenly everyone’s looking at them like they’re the AI guru. That’s when people start saying stuff like “unsupervised clustering” and “reinforcement learning,” and everyone else just nods and pretends to understand. Someone asks what the model is actually going to do, and no one’s totally sure. But hey, you’re doing AI now.
Phase 3: The Model Works… Until It Doesn’t
Then the model gets built. Maybe it works in testing. You feel hopeful. You run some inputs, and it spits out answers that make some sense. People start to get excited. Someone suggests putting it into production. This is when things get weird. Because suddenly, in the real world, the model’s predictions go sideways. It’s recommending things no one asked for. Or it works… until it doesn’t. You ask why it gave a certain result, and the answer is basically “the model learned it,” which is another way of saying “we don’t know.”
Phase 4: The Human Element – Fear and Frustration
Meanwhile, your team’s getting uneasy. People start quietly asking, “Is this thing going to take my job?” Others are just frustrated because now they’re being told to trust a tool they don’t understand. And to be fair, you kind of get it—asking someone to hand part of their job over to a machine without knowing exactly how it works? That’s a big ask. So you try to smooth it over with presentations, maybe a little internal training. It helps. A little. But that trust takes time.
Phase 5: The Battle with the Tech Stack
And let’s not forget the tech stack. You’re trying to get this shiny new model to work with systems built in 2008. The software talks in old file formats. There’s a server somewhere no one’s dared to reboot in years. You spend hours trying to connect everything. Sometimes it works. Sometimes it breaks and no one knows why. IT is stressed. You’re stressed. Someone suggests rebuilding the whole system, and everyone laughs nervously and immediately changes the subject.
Phase 6: The Unexpected Visit from Legal
Also, the legal stuff. At some point—usually when you’re just starting to feel like things are under control—someone from legal shows up and asks where the data came from. You say something like “oh, we’ve had it forever,” and they go pale. Turns out “having” data doesn’t mean you’re allowed to use it for machine learning. Now you’re reading up on GDPR and data consent and wondering how many rules you’ve accidentally broken.
The Realistic Payoff: It’s Not Magic, It’s Just… Helpful
And through all of this, expectations are way too high. Some exec is imagining AI writing all your content, predicting the market, replacing customer service, and maybe making coffee. Meanwhile, your tiny model is just trying to recommend the right product to the right user and not recommend vacuum cleaner accessories to someone shopping for dog food.
Eventually—after some false starts, some weird outputs, and more than a few long nights—you get something working. These hurdles are common, and learning how to overcome the challenges of AI implementation is the real story behind any success. Maybe it’s not mind-blowing. Maybe it’s just tagging tickets automatically or predicting churn with 70% accuracy. But it helps. And that’s the key. AI isn’t always about big, flashy stuff. Sometimes it’s just a tool that saves your team a bit of time or makes things a little easier.