👋 Hey friends,
Happy Friday! I’ve been thinking a lot about growth. Not growth in the old sense (hire more, spend more, push harder), but growth in the AI-native world — where the distance between idea and execution has basically collapsed.
I’ve been talking to a lot of founders and PMs lately, and I keep hearing the same thing: “We feel like AI should be giving us an edge, but where do we actually start?”
So today, I want to dig into four places where I’ve seen AI make the biggest difference:
Scaling smarter (without adding headcount)
Growing revenue by going deeper with customers
Rethinking marketing and sales around precision
Building products where AI isn’t just a feature — it’s the product
Let’s jump in.
Scaling Smarter with AI
I’ll be honest — the old way of scaling feels a little ridiculous now. Hiring armies of reps, spinning up new offices, waiting quarters for insights. AI flips that on its head.
These days, I think about scaling as removing bottlenecks instead of adding bodies.
Automate operations. I’ve seen teams use Zapier AI or LangChain to shave off thousands of repetitive support or reporting tasks. One PM told me their support queue basically went from “soul-crushing” to “manageable” overnight.
Decide faster. Tools like Obviously AI let you run churn or pricing models without a data science team. Imagine not waiting weeks for an analyst to confirm what you already suspected.
Go global instantly. Startups are using DeepL and Unbabel to launch in new markets literally overnight. Translation isn’t the blocker anymore.
One story I love: A SaaS team added an AI onboarding assistant with Intercom Fin. It proactively flagged new users at risk of churning and nudged them with personalized guidance. Result: churn dropped 18% in the first month.
Scaling today isn’t about doing more. It’s about doing the right things faster.
Revenue with AI: Depth, Not Just Reach
Here’s a trap I see a lot of people fall into: using AI just to cut costs. Sure, that’s nice. But the real magic is how AI helps you make more money from the customers you already have.
Think about it:
Personalization at scale. Netflix’s recommendations drive ~80% of views. You can get 80% of the benefit with tools like Dynamic Yield or Monetate. I tried one of these on a client project — we swapped generic product carousels for personalized ones, and AOV jumped in a week.
Predictive upselling. Amazon makes ~35% of sales from “Frequently Bought Together.” If you’re on Shopify, tools like Clerk.io or Rebuy let you do the same.
Dynamic pricing. Airlines nailed this decades ago. Now, with BlackCurve or Pricemoov, even smaller businesses can price based on demand or inventory.
Instead of asking “How do I get more customers?”, ask “How do I double the value of the ones I already have?” That mindset shift alone changes your roadmap.
Marketing & Sales in the AI Era
This is where I think AI feels the most visible. Marketing used to be spray-and-pray: push campaigns out, hope something sticks. Now? It’s like running a laser pointer.
Lead scoring that actually works. Tools like HubSpot AI and Apollo.io sort leads by likelihood to convert. I know a sales lead who said, “We literally stopped wasting time on dead-end calls.”
Hyper-personalized campaigns. Jasper and Mutiny spin up landing pages and ads tailored to each segment. It used to take weeks of creative work; now it’s hours.
Conversational selling. I’m fascinated by how Drift and Intercom AI act as SDRs that never sleep. They handle the FAQs, qualify prospects, and pass the warm ones to humans.
Content at scale. Instead of testing 1 headline, you test 100 with AdCreative.ai or Copy.ai. AI lets the market tell you what works, not your gut.
A B2B SaaS startup swapped their generic demo emails for AI-personalized ones (down to the industry and current tech stack). Demo bookings tripled in 90 days. That’s not incremental — that’s game-changing.
Building AI-Powered Products
This part gets me really excited — because AI isn’t just something you bolt onto your product. Increasingly, AI is the product.
The way you build has to change. Forget year-long roadmaps. The rhythm is: idea → thin slice → test → iterate.
Here’s how I think about it:
Ideation. Start with real user friction. Then spin up prototypes in v0 or Bolt. If you need integrations with GitHub or Supabase, Lovable is fantastic.
Development. Build on top of foundation models (GPT, Claude, Gemini, Llama). Capture user corrections as training data. Always show the “why” to build trust.
Deployment. Roll out to a small slice of users. Pick one KPI (conversion, churn, latency) and track it obsessively.
Integration. Keep everything modular. APIs make it easy to swap models when the tech evolves. Don’t forget compliance — it’s cheaper to get it right early.
Choosing your tools:
Chatbots (Claude, ChatGPT) → best for simple, one-off prototypes. Claude’s Artifact is neat, but you can only edit via prompts.
v0 → beautiful designs, fast.
Bolt → throwaway prototypes, flexible.
Replit → internal tools or data-heavy builds.
Lovable → production apps with integrations (though I usually debug in Cursor).
One HR startup I talked to didn’t rebuild their product. They just started with AI resume ranking (via Replit Agent). Once recruiters trusted it, they layered on interview scheduling and onboarding. Thin slices, fast feedback, measurable ROI.
Your Turn
Growth with AI isn’t about doing more — it’s about collapsing the gap between idea → test → iteration.
Scaling = velocity, not volume.
Revenue = depth, not reach.
Marketing = precision, not noise.
Product = orchestration, not ownership.
So here’s my question for you:
If shipping an idea was basically free, what bold experiment would you run tomorrow?
I’ll leave you with this: the companies pulling ahead aren’t the ones dabbling with AI on the side. They’re the ones weaving it into how they scale, sell, and build — turning AI from a tool into a growth engine.
Thanks for reading. I’d love to know: how are you using AI to grow right now? Reply and share — I always learn the most from your stories.
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