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Part 1 of 5 in a series on Lean Analytics in an AI era.
People keep asking us how Lean Analytics changes in an AI world.
Why? Because one of our co-founders, Ben Yoskovitz, co-authored Lean Analytics in 2013.
Here's the short version. The core Lean Analytics frameworks survive. Many of the specific metrics you were taught to obsess over need a rewrite. Some have been replaced entirely.
The hard part is telling them apart. So let us start with what doesn't change.
Lean Analytics has four ideas worth anchoring on. They all still hold.
1. Know your stage. We laid out five stages every business moves through (this includes startups, new corporate innovation ventures and even products): Empathy, Stickiness, Virality, Revenue, Scale. People still lie to themselves about which one they're at, desperate to hit hockey-stick growth before they've built a foundation. That's more true than ever in the rush to build the next AI darling.
2. Know your business model. How your business actually works determines which metrics matter. The six archetypes we wrote about in 2013 are dated. The principle of mapping your model is not. If anything it matters more now, because AI is quietly rewriting the cost structure underneath your model.
3. Pick the One Metric That Matters. At any stage, for any model, there's a single metric you should be focused on. You can't fix everything at once. The OMTM tells you what to work on now and how to measure it.
Here are some relevant posts:
OMTM vs. NSM: Why You Need Both to Drive Startup Growth
Build, Measure, Learn: The Expanded Edition
4. Draw lines in the sand. Benchmarks tell you when you've earned the right to move to the next stage. We called them lines in the sand because they're not set in stone. With AI products, those lines are moving faster than ever.
Here’s a post with more information on benchmarks: 4 Steps to Building Super Stick Products Leveraging Your Best Users
None of those four principles break in an AI era. The book was written in 2013 and the bones are still sound.
But the businesses being built today are different animals.
The cost of building has collapsed. Models drift under your feet. Pricing models, profit margins, user interfaces, the connectivity between platforms, all of it has shifted. And your "users" might not even be human anymore.
So the five stages don't disappear. They get a question mark stapled to each one.
What does activation mean when one prompt produces an expert-level output? What does stickiness mean when an agent, not a person, decides whether your product gets used? What does scale mean when every power user costs you money in tokens?
The framework holds. The metrics underneath it shift.
That's what this series is about. Over the next four editions we’ll go deep on what's actually changing:
The lens has shifted. The discipline hasn't.
If you're building or advising AI products right now, we want to hear it: which Lean Analytics metric has already stopped telling you the truth?
