I see it every day. Founders, bright-eyed and bushy-tailed, telling me they’re “implementing an AI strategy.” They’ve got the budget, they’ve hired the data scientists, and they’re ready to “leverage AI to unlock unprecedented growth.”
I’ll be blunt: most of them are wasting their time and money.
They’re chasing a fantasy, a buzzword that they think will magically solve all their problems. But AI isn’t a magic wand. It’s a tool, and like any tool, it’s only as good as the person wielding it.
I’ve been in the trenches of Silicon Valley for over a decade. I’ve built and sold two companies, RemoteTeam to Gusto and MovieLaLa to Gfycat. I’ve also been an angel investor in over 200 companies, including some of the biggest names in AI like Anthropic, OpenAI, Scale AI, and Hugging Face. I’ve seen firsthand what works and what doesn’t when it comes to AI.
And I can tell you this: most founders are getting it completely wrong.
The ROI Shell Game
Vendors love to flash impressive ROI numbers. They’ll show you case studies of companies that have implemented their AI solution and seen a 10x return on investment. It’s a shell game. They’re preying on your FOMO, your fear of being left behind in the AI race.
But here’s the dirty little secret: the entire concept of AI ROI is flawed. It’s a lagging indicator, a vanity metric that tells you nothing about the true health of your business.
Think about it. If your company is a mess, if your data is a disaster, if your team is dysfunctional, what do you think will happen when you throw AI into the mix? You’ll just have a more expensive, more complicated mess.
AI is a powerful amplifier. It will amplify your successes, but it will also amplify your failures. If you have a solid foundation, AI can help you build a skyscraper. But if you have a shaky foundation, AI will just help you dig your own grave faster.
The Real Problem: You’re Not Data-Driven
You say you’re data-driven, but are you? If your team isn’t making decisions based on data before AI, the new tools will just help you make bad decisions faster. Let’s talk about the real problem.
I remember when we were building MovieLaLa. We were a small team, just a handful of us in a tiny office. We didn’t have a fancy AI-powered recommendation engine. We had a spreadsheet. We manually tracked every movie, every user, every interaction. We spent hours poring over the data, trying to understand what our users wanted.
It was tedious, painstaking work. But it was also the most valuable thing we did. Because it forced us to be honest with ourselves. We couldn’t hide behind a black box algorithm. We had to confront the brutal facts of our business every single day.
And that’s what’s missing in so many companies today. They’re so eager to jump on the AI bandwagon that they skip the most important step: building a data-driven culture.
The Three Pillars of a True AI Strategy
So, what should you be measuring instead of ROI? What’s the secret to a successful AI strategy? It’s not about the technology. It’s about the people, the process, and the culture.
Here are the three pillars of a true AI strategy:
1. Data-Driven Culture
Before you even think about implementing AI, you need to build a data-driven culture. This means that everyone in your organization, from the CEO to the intern, needs to be comfortable with data. They need to know how to collect it, how to analyze it, and how to use it to make decisions.
This doesn’t mean that everyone needs to be a data scientist. But it does mean that everyone needs to have a basic understanding of data literacy. They need to be able to ask the right questions, to challenge assumptions, and to think critically about the data they’re seeing.
2. Clean and Accessible Data
Once you have a data-driven culture, you need to make sure that your data is clean and accessible. This is the unsexy, unglamorous work that nobody wants to do. But it’s also the most important.
I’ve seen so many companies that have petabytes of data, but it’s all a mess. It’s siloed in different departments, it’s in different formats, it’s full of errors. It’s completely useless.
If you want to be successful with AI, you need to invest in data infrastructure. You need to have a single source of truth for your data. You need to have a process for cleaning and validating your data. You need to make it easy for your team to access and analyze the data.
3. Start Small and Iterate
Finally, you need to start small and iterate. Don’t try to boil the ocean. Don’t try to build a massive, all-encompassing AI solution from day one.
Instead, pick one specific problem that you want to solve. It could be something as simple as automating a manual process or improving the accuracy of your sales forecasts. Start with a small, manageable project that you can complete in a few weeks or months.
And then, once you’ve had some success, you can build on that momentum. You can tackle bigger and more complex problems. You can start to build a true AI-powered organization.
The Bottom Line
Don’t be fooled by the AI hype. Don’t get caught up in the ROI shell game. The secret to a successful AI strategy has nothing to do with the technology. It has everything to do with the people, the process, and the culture.
Build a data-driven culture. Clean and accessible data. Start small and iterate.
That’s it. That’s the secret. It’s not sexy, it’s not glamorous, but it’s the truth.
And it’s the only way you’re going to win in the age of AI.
Frequently Asked Questions
What experience informs this perspective?
This perspective comes from over a decade of building companies in Silicon Valley, two successful exits (RemoteTeam to Gusto, MovieLaLa to Gfycat), and investing in 200+ startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.
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.
How can I apply this thinking to my own situation?
Start by identifying the core principle behind the opinion, not the specific example. Then ask yourself: does this principle apply to my context? If yes, test it in a small, low-risk way before going all in.