Why Most AI Projects Fail (And How to Avoid the Trap)

Hey, have you ever seen those headlines about companies spending millions on AI only to watch their projects crash and burn? Feels a little too common, right? It’s like buying a state-of-the-art espresso machine but not bothering to learn how to brew coffee. You end up with nothing but frustration—and no caffeine.

Here’s the thing: it’s not the technology’s fault. According to McKinsey, a whopping 75% of companies are ramping up their AI investments, but only 23% are actually getting their money’s worth. The culprit? Misaligned priorities and a lack of strategy.

So, let’s chat about a smarter way to think about AI: first-principles thinking. It’s the same mindset Amazon used to rethink logistics and create the gold standard for e-commerce efficiency. And guess what? It can help your business get AI right, too.

The AI Chaos Problem

Here’s a common scenario: a company hears about a shiny new AI tool. They buy it, throw it into their operations, and wait for magic to happen. Spoiler alert—it doesn’t. The result is a mess of disconnected systems, annoyed employees, and wasted budgets.

What’s going wrong? They’re treating AI like a plug-and-play solution, hoping it’ll fix deep-rooted inefficiencies. That’s like trying to patch a sinking ship with duct tape. Sure, it might hold for a minute, but it’s not going to keep you afloat.

First-Principles Thinking to the Rescue

Instead of asking, “How can we use AI?” flip the script.

Start with:

??????????? ??????????? What problem are we solving?

??????????? ??????????? Where are we bleeding time or money?

??????????? ??????????? What’s frustrating our employees or customers?

Think about it this way: if your team spends hours manually processing invoices, don’t jump straight to “AI can fix this!” Instead, ask why the process is slow. Maybe the root issue is clunky workflows or outdated systems. Solve those first, then see where AI fits.

A Real-World Win

One retailer I worked with had a chatbot that frustrated more customers than it helped. Instead of ditching it and starting over, we dug into the problem. Turns out, the training data didn’t match what customers were actually asking. We realigned the bot’s focus, added feedback loops, and boom—happier customers and a less stressed support team.

Where to Start? Ask Better Questions

?To get the most out of AI, you need to prep your organization like you’re training for a marathon. Here’s your checklist:

??????????? 1.???????? Is your data clean and accessible?

??????????? 2.???????? Are your workflows efficient?

??????????? 3.???????? Do employees understand how AI will impact their work?

??????????? 4.???????? Is leadership fully on board?

If any of these are lacking, slow down. Fix the foundations first. AI isn’t about throwing spaghetti at the wall to see what sticks—it’s about aligning tools with real needs.

Let’s Keep the Conversation Going

This is just the tip of the iceberg. If you’re nodding along (or furiously scribbling notes), you’ll love the full breakdown in my newsletter.

Subscribe here for more actionable strategies, practical examples, and a few stories from the trenches. It’s like having a coffee chat with a coworker who’s already been through the AI rollercoaster—minus the caffeine crash.