The Activation Event Nobody Can Define in an AI Product
Your activation metric is almost certainly wrong.
Not wrong like “it’s tracking the wrong screen.” Wrong like “the entire concept you’re applying doesn’t fit the product you built.” If you’re working on an AI-native product and you’re currently trying to find your activation event the same way you’d find it for a SaaS tool, you’re solving the wrong problem.
Here’s the issue. Every activation framework you’ve ever read was built for products where value is delivered through actions. User creates a project. User sends their first message. User connects their first integration. You instrument the action, you run the cohort analysis, you find which action predicts retention, and you optimize onboarding toward that action. Clean. Measurable. Works great for a project management tool.
But in an AI-native product, the moment a user “gets it” isn’t an action. It’s an outcome. And that outcome emerges from a specific type of conversation, not a specific feature click. You can’t instrument it with a single event log. You have to find it in conversation patterns, which is a fundamentally different problem.
^ every PM trying to jam their AI product into an activation funnel built for clicking buttons
Why the classic framework breaks down
The standard playbook goes like this: take your 30-day retained users, take your churned users, find the action that one group did and the other didnt, and that action is your activation event. Twitter found it was following 30 accounts. Facebook found it was 10 friends in 14 days. Dropbox found it was putting at least one file in a folder.
These are all actions. Discrete, timestampable, instrumentable. And the framework works beautifully when your core value is delivered by doing a specific thing in the product.
Now try to apply this to an AI product.
What’s the “action” that predicts retention in an AI coding assistant? Starting a conversation? That happens with everyone, retained and churned. Typing more than 50 characters? Sending more than 3 messages in a session? You can run all of these correlations and you’ll find… weak signals. Noise. Nothing that cleanly separates the users who stayed from the users who left.
The reason is that the value in an AI product isn’t delivered by doing something. It’s delivered by receiving something, specifically, a response that is genuinely better than what the user expected. That’s a qualitative outcome. It lives in the conversation. And you can’t reduce it to an event log the way you can reduce “user clicked create project” to an event log.
This doesn’t mean activation is unmeasurable. It means you have to find it differently.
What activation actually feels like
Before you can find your activation conversation in the data, you need a mental model for what you’re looking for. Because “user felt good about the response” isn’t a metric.
Here’s how I’d describe the activation moment: the user asks something and gets an answer that’s more useful than they expected, in a way that changes what they do next. Not just correct. Useful in a way that opens up the next question they wouldn’t have thought to ask without that response.
The critical behavioral signal after a true activation moment: the user immediately sends a follow-up. Not because the first answer was incomplete. Because they want more. They’ve just realized this thing can actually help them.
There’s an important distinction between being impressed and being activated. Users can be impressed without being activated. “Wow that’s cool” does not predict retention. “This solved my actual problem” does.
An impressed user drops off after the wow moment. No current need, will mention it vaguely when someone asks about AI tools. An activated user immediately has a next question. They start thinking about what else this could do for them.
Shallow demos create impressment. Real problems create activation.
^ realizing your beautiful 6-step feature tour is creating “wow cool” and then 70% day-1 drop-off
How to find your activation conversation
This is the practical part. Here’s the method we’d use, and have used across AI products we’ve worked closely with at Agnost.
Step one: pull your 30-day retained users (users who were still active at day 30 after signup) and your churned users (users who didn’t return after day 7 or 14, depending on your product’s natural usage cadence).
Step two: go into the first three sessions of each group. Not the aggregate numbers. The actual conversations.
Step three: look for patterns that appear in the retained group’s early sessions but dont appear consistently in churned users’ early sessions. You’re looking across four dimensions:
Turn count. Retained users often hit a specific turn-count threshold in their first meaningful conversation. Not because more turns is always better, but because hitting that threshold means they were in a back-and-forth that went somewhere useful. For many AI products, something in the 5-9 turn range for the activation conversation is common. Churned users often have sessions that stop at 2-3 turns.
Topic or intent category. This is the big one. What topics were retained users exploring in their early sessions? What were churned users asking about? You’re looking for a category of use case that appears significantly more often in the retained cohort.
Response depth and follow-up behavior. After the AI gave a long, substantive response, did the user ask a follow-up? Or did they bounce? Retained users tend to follow up after deep responses. Churned users often get a thorough response and disappear, which usually means the answer was either wrong, too complicated, or they just didn’t have a real need to begin with.
Session-over-session return on the same topic. Did the user come back in session 2 and continue exploring the same topic they raised in session 1? This is one of the strongest signals of real activation we’ve seen. If a user returns to continue a conversation thread, they’re not just impressed. They’ve integrated the product into how they think about a problem.
Step four: you’re assembling a conversation signature, a combination of topic category, depth, turn pattern, and follow-up behavior that reliably shows up in the first few sessions of users who stuck around. That signature is your activation conversation.
Step five: now build toward it. Your onboarding flow should be redesigned around one goal, getting new users into their activation conversation as fast as possible.
What this looks like for different product types
The activation conversation is specific to your product, but there are patterns by category.
For an AI coding assistant, activation usually happens when the AI successfully debugs a real bug the user was actually stuck on. Not a toy example, not a made-up snippet. Their actual code. The moment the AI looks at their specific repo context and outputs something that genuinely fixes a thing that was blocking them is when users viscerally get it. “Wow this actually works on my code.” Before that moment, it’s just a smart autocomplete. After that moment, it’s something different.
For an AI companion, activation happens in a specific type of personal disclosure conversation where the AI demonstrates it understood the subtext, not just the literal words. Someone says “I’ve been having a weird week” and the AI doesn’t just say “I’m sorry to hear that, tell me more.” It actually picks up on what’s underneath the message and responds to that. The user who experiences that moment feels genuinely heard, and that’s functionally irreplaceable for them.
For an AI tutor, activation happens when the AI explains a concept in a way that makes the student say “oh I finally get it” and then immediately ask a follow-up they couldn’t have formulated before understanding the concept. The question changes. They go from “what is this” to “wait, so does that mean X?” That shift in question quality is the activation signal.
For a customer support bot, activation is often the simplest of all: the AI resolves something the user expected to wait 24 hours for in 30 seconds. The contrast between expectation and experience is the activation. And thats why deployment context matters a lot here, a support bot that handles only easy queries never creates this moment for users who needed real help.
The onboarding redesign that actually follows from this
Most AI product onboarding flows do one of two things: walk users through features step by step, or drop them into a blank chat and say “ask me anything.”
Neither is optimized for activation.
Once you know your activation conversation, the onboarding job is different. You’re not trying to show users around. You’re trying to create conditions for one specific conversation type as fast as possible.
Practical implication for setup: your onboarding should collect exactly the context required for the activation conversation and nothing more. Every extra question is friction between the user and their activation moment. If activation requires knowing what kind of code the user writes, ask that. Don’t ask three other things because they might be useful later.
The “try asking me…” prompts in your UI should steer users toward the activation conversation category, not showcase general capabilities. If activation happens when the AI debugs real code, the first-session suggestion should be “paste a function you’re stuck on” not “try generating some code.”
We track this across the products on Agnost’s platform. The difference in onboarding conversion between teams who’ve identified their activation conversation and built toward it versus teams running generic feature tours is real. In several cases, onboarding flows designed around the activation conversation convert to active day-30 users at 2-3x the rate of feature tours.
^ you, three weeks after redesigning your onboarding flow around your actual activation conversation
The measurement gap you’re probably dealing with right now
Here’s the real problem: your current analytics stack wasn’t built for this analysis.
Event logs for clicks and feature usage are easy to pull. But “what did users talk about in their first 3 sessions, and how did that differ between retained and churned cohorts” is not something standard product analytics tools give you. Not because they’re bad tools, they’re built for action-based products. The conversation is invisible to them.
So teams end up doing this manually: exporting raw logs, reading through samples, looking for patterns. Which works for a one-time analysis. But it doesnt scale, and it definitely doesn’t tell you in real time which of today’s new users are heading toward their activation conversation and which aren’t.
This is one of the core things Agnost was built to solve. Making conversation patterns searchable, comparable across cohorts, and trackable at the individual user level. If you want to run the retained-vs-churned activation analysis this post describes, or monitor in real time whether new users are hitting their activation conversation signature, that’s exactly the visibility we built toward.
Wrapping it up
Activation in AI products isn’t a click. It’s not a feature used or a setup step completed. It’s a conversation that ends with a user realizing this thing actually works on their real problem, and then wanting to keep going.
That’s harder to find than a button click. But it’s also far more durable when you do find it. A user who’s had their activation conversation isn’t just retained. They’re often evangelical about the product, because the experience of an AI genuinely surprising them with its usefulness is one of those things you want to tell people about.
Find the conversation. Build toward it. Measure whether new users are getting there.
Everything else in your onboarding is details.
^ you, finally watching new users hit their activation conversation in real time instead of guessing at feature clicks
TL;DR: SaaS activation is an action. AI product activation is a conversation outcome. Find the conversation pattern that separates your retained users from your churned users in their first 3 sessions, then redesign onboarding to get new users to that conversation as fast as possible.
Reading Time: ~10 min