'''# Why Your AI Product Needs a
I’ve seen it a dozen times. A brilliant team, a genuinely innovative AI model, and a product that’s going… nowhere. They have users, sure. They have activity. But they have no idea what truly matters. They’re drowning in data but starved for wisdom.
When I was at RemoteTeam, we had this exact problem. We had all these dashboards. User activity, engagement metrics, you name it. But we were just chasing our own tail. It wasn't until we defined our North Star metric that everything clicked into place. For us, it was the number of successfully completed automated payroll runs. That single number told us if our customers were getting the core value of our product. Everything we did, from product development to marketing, was then focused on moving that one metric. And it worked. We were eventually acquired by Gusto.
This isn't just a nice-to-have. For AI products, it's a matter of survival. Your model might be 99% accurate, but if you're not measuring what your users actually care about, you're flying blind.
The Problem with Vanity Metrics
Most AI teams I see are obsessed with the wrong things. They track:
- Daily Active Users (DAU): So what? People can be opening your app and getting zero value from it.
- Model Accuracy: A classic one. Your model can be technically perfect but solve a problem nobody has.
- Number of API Calls: This is just a measure of activity, not progress.
These are vanity metrics. They look good on a slide deck, but they don't tell you if you're building a sustainable business. They don't tell you if your users are happy.
Finding Your North Star
So how do you find your North Star? It's not about picking a metric out of a hat. It's about understanding the core value you provide to your users.
Here’s the framework we used at MovieLaLa, which was later acquired by Gfycat.
What is your product's "magic moment"? This is the moment when a user truly understands the value of your product. For Instagram, it was seeing their photo instantly look better with a filter. For our movie recommendation app, it was discovering a hidden gem they loved. For an AI-powered code completion tool, it might be the first time it suggests a complex block of code that just works.
What is the one metric that best captures that magic moment? This is your North Star. It should be a measure of value, not activity. It should be something you can directly influence with your product. For MovieLaLa, our North Star was the number of movies a user rated 4 or 5 stars. This told us that we were successfully recommending movies they enjoyed.
How can you instrument your product to track this metric? This is where the technical side comes in. You need to have the right analytics in place to track your North Star metric accurately. Don't just rely on off-the-shelf tools. You might need to build your own instrumentation to get the data you need.
Examples of North Star Metrics for AI Products
This is not a one-size-fits-all formula. Your North Star will depend on your specific product and users. But here are some examples to get you thinking:
- For an AI-powered writing assistant: Number of accepted suggestions. This shows that your suggestions are actually helpful.
- For a sales forecasting tool: Percentage of forecasts that are within 5% of the actual sales numbers. This demonstrates the accuracy and reliability of your tool.
- For a personalized e-commerce recommendation engine: Revenue per visitor. This is a direct measure of the value your recommendations are creating.
- For a customer support chatbot: Number of issues resolved without human intervention. This shows that your chatbot is effectively handling customer queries.
I've invested in over 200 companies, including some of the biggest names in AI like Anthropic, OpenAI, Scale AI, and Hugging Face. The ones that succeed are the ones that have a crystal clear understanding of their North Star. They don't get distracted by the latest fads or technologies. They are relentlessly focused on delivering value to their users.
Your North Star is Your Compass
Once you have your North Star, it should guide every decision you make. When you're thinking about a new feature, ask yourself: "Will this move our North Star metric?" If the answer is no, then you should seriously question whether it's worth building.
Your North Star is also a powerful communication tool. It aligns your entire team around a common goal. Everyone, from the engineers to the marketers, should know what the North Star is and how their work contributes to it.
At RemoteTeam, we had our North Star metric displayed on a giant screen in the office. Every morning, we would look at that number. It was a constant reminder of what we were trying to achieve. It kept us focused and motivated.
Don't Be Afraid to Be Wrong
Finding your North Star is a process of discovery. You might not get it right the first time. And that's okay. The important thing is to start with a hypothesis, test it, and be willing to iterate.
Don't be afraid to have a strong opinion. The worst thing you can do is to have a vague or wishy-washy North Star. It's better to be opinionated and wrong than to be vaguely right.
I wrote about this in my book, "Becoming Top 1%". The top performers in any field are the ones who have a clear vision and are not afraid to take a stand. They don't try to please everyone. They have a point of view.
So, what's your North Star? If you don't have one, now is the time to find it. It's the most important thing you can do for your AI product. It's the difference between building a product that's just a cool piece of technology and building a product that actually changes people's lives.
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.