Why Generative AI Will Make Most Product Analytics Tools Obsolete
I remember the exact moment I realized we were flying blind. It was 2014, and my company, MovieLaLa, was on a rocket ship. We had millions of downloads, a massive social media following, and what I thought was a deep understanding of our users. We were tracking everything—every click, every swipe, every share. We had dashboards coming out of our ears. And yet, our growth was starting to flatline.
We brought in a team of expensive consultants, and they spent weeks buried in our analytics. They came back with a hundred-page report full of charts and graphs, but no real answers. They told us our user engagement was down, but they couldn't tell us why. They told us our retention was suffering, but they couldn't tell us what to do about it. It was a classic case of being data-rich and insight-poor. We eventually sold MovieLaLa to Gfycat, but that experience stuck with me. It was a painful lesson in the limitations of traditional product analytics.
Fast forward to today, and I see the same pattern repeating itself. Companies are spending a fortune on sophisticated analytics tools, but they're still struggling to answer the most basic questions about their business. Why? Because the tools are the problem. They were designed for a different era, a pre-AI era. And they are about to become obsolete.
The Dashboard is Dead
For the last decade, the dashboard has been the primary interface for product analytics. We’ve been conditioned to believe that if we just stare at enough charts and graphs, the insights will magically appear. But it’s a lie. The dashboard is a passive, one-way medium. It can show you the “what,” but it can’t tell you the “why.”
I saw this again at my second company, RemoteTeam. We were building a platform to help companies manage their remote teams, and we were obsessed with data. We had a beautiful dashboard that tracked everything from employee onboarding to payroll processing. But when the pandemic hit and our business exploded, the dashboard became useless. We were so overwhelmed with new data that we couldn't see the signal for the noise. We were drowning in metrics, but we had no idea what was really going on with our customers.
This isn't just my experience. A recent study by Forrester found that less than 0.5% of all data is ever analyzed and used. Think about that. We're collecting all this data, paying to store it, and paying for tools to visualize it, and we're not even using it. It's the definition of insanity.
The problem is that data, on its own, is meaningless. It's just a collection of numbers. To turn data into insights, you need context. You need to understand the user, the product, and the market. And you need to be able to ask the right questions. Traditional analytics tools can't do that. They're just glorified calculators.
The Coming AI Wave
This is where generative AI changes everything. For the first time, we have a technology that can not only process vast amounts of data but can also understand context and intent. As an angel investor in over 200 companies, including foundational AI players like Anthropic, OpenAI, Scale AI, and Hugging Face, I’ve had a front-row seat to this revolution. The pace of innovation is unlike anything I’ve ever seen. What was science fiction a year ago is now a reality.
These new AI models can do things that were previously unimaginable. They can analyze unstructured data like user feedback, support tickets, and social media comments. They can identify patterns and correlations that a human would never spot. And they can communicate their findings in plain English, not in a confusing mess of charts and graphs.
Imagine this: instead of spending weeks digging through dashboards, you simply ask a question. “Why is our user churn higher for customers in Europe?” And within seconds, you get a detailed answer, complete with a summary of the likely causes, the underlying data, and even suggestions for how to fix the problem. This is not a futuristic vision. This is what the next generation of product analytics tools will look like.
The Anatomy of an AI-Native Analytics Platform
So what separates these new AI-native platforms from the legacy tools? It’s not just about slapping a chatbot on top of an existing dashboard. It’s a fundamental rethinking of how we interact with data. Here are a few of the key characteristics:
Conversational Interface: The primary interface is not a dashboard, but a conversation. Users can ask questions in natural language and get immediate answers. This democratizes data and makes it accessible to everyone in the organization, not just the data scientists. At one of my portfolio companies, they replaced their weekly analytics meeting with a Slack channel where anyone can ask questions of their data bot. The time to insight went from a week to a few seconds.
Proactive Insights: Instead of waiting for you to find the problem, the platform finds it for you. It constantly monitors your data for anomalies and outliers, and alerts you to potential issues before they become full-blown crises. It’s like having a team of data analysts working for you 24/7. I know a founder who was alerted by an AI analytics tool about a bug in their checkout flow that was costing them thousands of dollars a day. They had no idea the bug even existed.
Root Cause Analysis: It doesn’t just tell you what happened, it tells you why it happened. It can automatically sift through all the possible factors and identify the root cause of a problem. This is the holy grail of analytics, and it’s finally within reach. For example, an e-commerce company I advise was struggling with a high cart abandonment rate. Their AI analytics platform was able to pinpoint the cause to a specific shipping option that was confusing users.
Automated Hypothesis Testing: Coming up with a hypothesis, designing an experiment, and analyzing the results can take weeks. With an AI-native platform, you can do it in minutes. The platform can help you formulate a hypothesis, suggest the best way to test it, and then automatically analyze the results. This is a massive accelerator for product development.
How to Prepare for the Inevitable
If you’re a product leader, you can’t afford to ignore this shift. The way you’re doing analytics today will be obsolete in 18-24 months. So what can you do to prepare?
First, you need to change your mindset. Stop thinking about analytics as a reporting function and start thinking about it as a strategic partner. Your goal is not to create more dashboards, but to make better decisions, faster. This means empowering your team to ask their own questions and find their own answers. It means moving from a culture of reporting to a culture of inquiry.
Second, you need to re-evaluate your tool stack. The all-in-one analytics suites that you’re paying a fortune for are dinosaurs. They’re not built for the AI era. Look for the new breed of AI-native tools that are emerging. Start with small pilot projects and see the impact for yourself. Don’t be afraid to experiment. The cost of inaction is far greater than the cost of a failed experiment.
Third, you need to invest in data literacy for your team. The tools are getting smarter, but they’re not a replacement for human judgment. Your team needs to know how to ask the right questions, how to interpret the results, and how to make data-informed decisions. The AI can be the copilot, but you are still the pilot. This means training your team on the basics of data analysis, but also on the art of critical thinking. It means teaching them to be skeptical of the data and to always look for the “why” behind the “what.”
The Future is Not a Dashboard
I’ve built and sold two companies, and I’ve invested in over 200 more. I’ve seen what it takes to win. And I can tell you with certainty that the companies that embrace this new era of AI-powered analytics will have an insurmountable advantage. They will be able to move faster, make smarter decisions, and build better products.
Those that cling to the old way of doing things will be left behind. They will be stuck in a reactive loop, constantly trying to catch up, while their competitors are already on to the next thing. The dashboard is dead. The future of product analytics is a conversation. And it’s a conversation you need to be a part of. The time to start is now. Don't wait until you're the one being disrupted.
I’ve seen this movie before, and I know how it ends. The companies that adapt, that embrace the new technology, that are willing to rethink everything they thought they knew—those are the ones that survive and thrive. The rest become footnotes in history. Which side of history do you want to be on?
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 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.
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.