You're probably using AI all wrong. I was too. Here's the painful story of how I learned to do it right.
A few years back, at one of my startups, we were on the verge of a massive breakthrough. We had a team of brilliant engineers, a product that was gaining traction, and a mountain of data we thought was our golden ticket. We decided to build an AI-powered recommendation engine. We spent months on it, poured a ton of money into it, and when we finally launched... crickets. It was a spectacular failure. The recommendations were garbage, and our users hated it. We had to scrap the whole thing.
That failure taught me a lesson I've carried with me through two exits and over 200 angel investments: AI is not a magic wand. And it turns out, I'm not the only one who's learned this the hard way. The data shows that a staggering 81% of companies fail to see a significant return on their AI investments.
I've managed over $6M in remote team payroll, and I've seen every AI performance review mistake in the book. I've seen companies burn through cash on AI projects that go nowhere, and I've seen others achieve incredible results with a fraction of the resources. The difference between the 19% who succeed and the 81% who fail isn't about having the most data or the smartest engineers. It's about how you think about AI.
The Allure of the AI Buzzword
The biggest trap I see founders fall into is chasing the AI buzzword. They hear that every company needs an "AI strategy," so they rush to implement AI without a clear understanding of what they're trying to achieve. They think that by simply sprinkling some AI on their product, they'll magically unlock new levels of growth and efficiency.
This is a recipe for disaster. At MovieLaLa, my first company, we didn't start with AI. We started with a problem: movie studios had no idea how to market to young audiences on social media. We built a platform that connected them with millions of moviegoers, and we used data to help them understand what was resonating. We didn't call it AI back then, but we were using data to make better decisions. It was only after we had a deep understanding of the problem that we started to explore how we could use more advanced techniques to automate and improve our processes.
The Data Delusion
Another common mistake is what I call the "data delusion." Companies think that if they just collect enough data, they'll eventually find some hidden insights that will transform their business. They spend millions on data infrastructure and hire teams of data scientists, but they never stop to ask themselves what they're actually trying to achieve.
More data is not always better. In fact, it can be a distraction. At RemoteTeam, which was acquired by Gusto, we had access to a ton of data about how remote teams work. But we didn't try to boil the ocean. We focused on a few key metrics that we knew were critical for success, like employee engagement and payroll accuracy. We built tools that helped companies improve those metrics, and that's what made our product so valuable.
The People Problem
The third and most important reason why companies fail at AI is the people problem. They think that they can just hire a few AI experts and let them work their magic. But AI is not a siloed function. It needs to be integrated into every aspect of your business, and that means you need to have the right talent and the right culture.
I've trained hundreds of engineers in my career, and I can tell you that the best ones are not the ones who know the most about machine learning algorithms. They're the ones who have a deep understanding of the business and are passionate about solving real-world problems. They're the ones who can work with product managers, designers, and marketers to build products that people love.
How to Be in the 19%
So, how do you avoid these traps and join the 19% of companies that are actually succeeding with AI? It's not as complicated as you might think. Here are a few principles that I've learned over the years:
- Start with a painful problem. Don't start with the technology. Start with a real-world problem that is costing you time, money, or customers. The more painful the problem, the more likely you are to succeed.
- Think of AI as a tool, not a solution. AI is not a magic wand. It's a tool that can help you solve problems more effectively. But it's not a substitute for critical thinking and good old-fashioned hard work.
- Focus on the human-in-the-loop. The most successful AI applications are the ones that augment human intelligence, not replace it. Think about how you can use AI to help your employees make better decisions and be more productive.
- Measure what matters. Don't get bogged down in vanity metrics. Focus on the metrics that are actually driving your business forward. And be prepared to iterate and experiment until you find what works.
AI is not a trend. It's a fundamental shift in how we work and live. But it's not going to be an easy road. It's a long-term investment. Be patient and be prepared for a lot of hard work.
The 19% of companies that succeed with AI will leave everyone else in the dust. The question is, which side will you be on?
Frequently Asked Questions
What's the most common pushback you get on this?
People often push back by citing exceptions or edge cases. And they're usually right that exceptions exist. But building a strategy around exceptions rather than patterns is a losing game for most founders.
How has this view evolved over time?
My thinking on most topics has changed significantly over the years. Early in my career, I held many conventional views that experience proved wrong. I try to update my beliefs when the evidence changes.
Do all experts agree with this view?
No, and that's fine. The best ideas in business are often contrarian. I share my perspective based on my experience and data, but I encourage you to seek out opposing viewpoints and form your own conclusions.