Dopt

Join me tomorrow for the Ultimate Product-led Onboarding Playbook live event with Reforge! I'll be sharing the playbook from my time working with 100s of companies while at Dopt and leading Growth Design at Dropbox. Almost 3,000 people have registered already. Sign up link ↓

Latest customer using Dopt! Here's ZenML's onboarding hub built with Dopt. A few reasons I like this: 🔹 This pattern makes sense for ZenML: onboarding hubs are most successful in products that have longer setup experiences where users need more assistance, like dev tools. 🔹 The checklists give structure to the journey toward aha moment and the support, invite, and resource entry points help users get unstuck. 🔹 There are two separate checklists in the hub: a "Starter Setup" and "Production Setup". The truth is onboarding is more like a set of aha moments where the user realizes deeper levels of value. I like how these checklists work together to get a user all the way to production. We've considered something like this at Dopt -- it's cool to see our customers go live with it! 🔹 The hub may look like a lot, but it's all tucked into an entry point in the navigation. The user can use the rest of the app and easily return here when needed. I wrote a full breakdown of the pattern on our blog, link in the comments!

When building AI features you never have full confidence “it will work”. There’s an inherent risk: You can't know if an AI result is good without building the feature to see the result. But you don't want to build the feature only to discover the AI response isn't good. So how do you determine if it's worth building? Yesterday we launched AI Assist to the world. Over the past few months, we went from low-confidence early AI ideas to launching AI assist, our new tools to build AI-powered in-product assistance into your product. Building AI Assist was unlike any other feature I've built in my career. It felt much riskier than non-AI features. By navigating the challenges of building AI assist, we learned 3 product lessons for mitigating risk when building AI features: Lesson 1️⃣ : It's too easy to dream. Get grounded with technical research. Make time for designers and developers to do research to learn what’s possible. It may end up being a sunk cost (you’ll have to be comfortable with that risk), but research is the best input to creating AI solutions that are grounded in reality. Lesson 2️⃣: Quantify risk to select opportunities Unlike the traditional value vs effort prioritization of non-AI product work, AI requires a sense of the probability of success across a set of bets (kind of like a Growth team!). Quantifying the risk helps navigate where to invest time. Lesson 3️⃣: Building is the only way to know what to build When building non-AI products, you have high certainty a feature will work after discovery and definition. Not so with AI features. With AI features, you must iteratively build to increase the probability of success and continue or decide to cut your losses and move on. I wrote a case study about building AI Assist and dug deeper into these lessons on our blog, link in the comments!

It's been so awesome seeing AI Assist starting to get used out in the wild. Here it is explaining the concept of 'Temperature' in Autoblocks 🌡️