Your Product Just Got a Brain
AI Tricks That Make Growth Way More Fun (and Smarter)
Let’s be honest, product-led growth used to be kind of predictable.
Build a free plan. Add a tour. Wait for people to upgrade. Tweak onboarding again. Hope it sticks.
But now we’ve got AI. And it’s like the product grew a brain.
We’re not just making things faster, we’re making them smarter, more personalized, and in some cases, borderline magical. If you’ve ever wanted to say, “What if the product could just do that for the user?”, this is that moment.
Here are ways AI is quietly (and not-so-quietly) reshaping every stage of the PLG flywheel.
Acquisition: Let Users Try Before They Even Know They're Trying
Zero-click trials
Users can interact with your product right from docs, chat widgets, or even a Chrome sidebar, no signup, no friction. They’re using the product before they even visit the site.Example: Imagine someone is browsing your public knowledge base or reading a community thread. A small “Ask the product” widget powered by an LLM lets them type in a use case or query, and see the product’s response instantly.
"Can I automate follow-up emails based on status?"
"Yes, here's what it would look like inside the workflow builder..."Auto-personalized ICP pages
An LLM spots who’s visiting and spins up a landing page just for them, logo, tailored copy, and use-case examples. It’s the digital version of “Oh wow, this is exactly what I was looking for.”Example: Imagine a RevOps leader at Stripe visits your site and sees a page with Stripe’s logo, use cases relevant to RevOps, and dashboards prefilled with metrics. This is possible using Clearbit + a prompt-tuned LLM.
Activation: Tell the Product What You Want—It’ll Figure Out the Rest
Reverse demos
A user types, “Send me alerts when my MRR spikes,” and the agent configures the workflow. No tours. No long onboarding. Just goal → setup → value.Example: In Retool, a user types “Send a Slack alert when a new row is added to Airtable,” and the app auto-generates the entire workflow with the right blocks in place.
Self-optimizing onboarding
Instead of static walkthroughs, AI watches where users drop off, tests new copy or flows, and adapts on the fly. No PM involvement required.Example: Instead of locking everyone into the same onboarding flow, tools like Amplitude are experimenting with onboarding that adjusts on the fly. If the AI notices that users from design teams convert better when they see certain templates first, it just… changes the order. No PM required.
Engagement: The Product Feels Like It’s Paying Attention
AI usage coaches
Notice someone struggling? An agent steps in with exactly what they need—right where they are. Less "read this help doc" and more "want me to fix that?" Example: Duolingo’s Birdbrain engine watches how you learn. Struggling? It slows down. Flying through lessons? It ramps up. Now imagine your SaaS tool quietly doing the same, helping users right when and where they need it, without them ever asking.Voice and natural language commands
Users can ask things like, “Show me churn by cohort,” and skip menus entirely. The UI becomes a conversation, not a maze.Example: In Mixpanel, instead of building a report from scratch, a user types “Show me retention for users from India last quarter” and gets a chart instantly.
Monetization: Stop Charging for Seats. Start Charging for Results.
GPU-metered pricing
Align pricing to real-time resource usage, especially in AI-heavy features. It’s transparent, aligns with cost, and feels fair, especially for power users.Example: Runway charges based on video length + resolution + GPU render time + storage + model used.
Dynamic paywalls
Not every user should hit the same wall. AI scores user intent and adjusts trials, access, and nudges based on real behavior.Example: Instead of showing everyone the same trial or upgrade prompt, some media companies use AI to decide who sees a wall, when, and how hard. Someone loving the product might get a nudge to upgrade. Someone still exploring? Give them more time. Smart gating, not brute forcing.
Expansion: The Product Does the Selling (and Bragging)
LLM-curated add-ons
Instead of dumping users into a marketplace, the product subtly recommends the perfect next feature, based on what similar users do.Example: HubSpot has started showing “Recommended Add-Ons” based on what teams like yours usually do next. If you’ve just started with email automation, it might suggest Sales Playbooks right when you're ready.
Shareable win-stories
Imagine your product telling users: “You reduced resolution time by 34% last week.” And then asking, “Wanna share that with your boss on Slack?” One click, and your champion becomes your loudest cheerleader.Example: ClickUp’s AI sends a Slack-ready message: “Your team completed 137 tasks this week, up 22%. Want to share this win with your manager?”
Retention: Fix the Leaks Before They Show Up in a Dashboard
Churn prevention conversations
AI notices a drop in usage and kicks off a helpful check-in. “Want help setting up automations again?” It’s not a warning, it’s a save. This was done even before, but can be made more intelligent and contextual to increase the impact.Example: Platforms like Gainsight are using AI to flag subtle signs of trouble, like usage dips, fewer logins, or your main champion going quiet. Before anyone cancels, the product nudges the CSM with a tailored playbook to jump in and rescue.
Weekly value digests
Simple, auto-written summaries like “You saved 17 hours this week” keep the product’s ROI top of mind. Great for renewals. Great for reminding people you're doing your job.Example: Linear sends a Monday morning digest that says, “Here’s what your team shipped last week”, sometimes even as a quick audio summary. It’s a simple way to remind users, “Hey, look how far we’ve come.” That builds stickiness, not dashboards.
Product & Ops: Growth Hacks Meet Real Infrastructure
Prompt engineering = growth lever
Changing a prompt inside your AI feature can sometimes outperform an entirely new feature build. Treat them like you treat CTAs: test, track, optimize.Example: Intercom realized that tweaking the wording of their AI agent’s fallback prompt changed how many issues got resolved without escalation. No UI change. Just a better prompt. Sometimes, that’s all it takes.
Explainability as a feature
If your AI is making decisions, pricing, recommendations, or gating, users need to understand why. Transparency builds trust, and trust builds retention.Example: GitHub Copilot now shows why it made a suggestion by linking to public code sources. It’s not just helpful, it builds trust. Especially when your product is making decisions for the user, explaining “why” is half the game.
The Bigger Picture: Your Product Is Starting to Think for Itself
This isn’t just about automating a few steps.
It’s about building products that adapt to users, personalize themselves, and evolve in real time. The more data they see, the smarter they get. The less we hardcode, the more we can train.
We’ve moved beyond self-serve. We’re now in the era of self-evolving products.
Where to Start
Start with one friction point in your funnel. Activation, retention, wherever it hurts.
Add instrumentation. You can’t optimize what you can’t observe.
Run a small AI experiment. You don’t need an agent army. One smart intervention can change the flow.
If you’re experimenting with AI and PLG or have questions, drop me a note or hit reply. Would love to jam on it.
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