Why is product discovery — the practice of identifying the right things to build before switching to delivery — so hard to implement? In my previous article I described two systemic problems: must-have features and weak goals. In this article I’ll focus on three key challenges that the are down to the product org itself. […]
Many companies recognize it’s better to develop products through research, experimentation, and evidence-guided decisions, yet few are able to make product discovery truly work. While the concept has been around for a long time, implementation is often half-hearted, partial and ineffective. AI dev hype is making things worse. In this and the following artile I’ll
Whenever I teach product discovery, someone will raise their hand and ask a variant of this question: “This feels slow. Wouldn’t it be faster to just launch it and see what happens?” The answer is No. If you skip product discovery you may get bits into production faster, but you’ll create value far slower. This
In his latest book, Transformed, Marty Cagan introduces the term The Product Operating Model (POM) which he defines as “How the best tech-powered companies work …— principles, practices, and competencies ”. I feel that, like Product Discovery, which Cagan also coined, the Product Operating Model is going to be an important and useful concept, but
I regularly ask product people how they choose what to develop. Today, an increasingly popular answer is “we focus on the most important user problems”, or its more modern cousin “we prioritize opportunities”. Underlying these answers is a classic model that asserts that product development has to start with mapping out and prioritizing customer problems,
Product companies tend to manage the lifecycle of projects (AKA ideas) — from the moment the project/initiative is approved until it is shipped. But in a reality where most ideas fail to create value, it is far more important to manage the lifecycle of goals (what we’re attempting to achieve)— from the moment the goal
The Lifecycle of Goals: Research, Discover, Deliver, Monitor Read More »
Observing tech companies you may notice a broad, disturbing phenomenon: everyone’s hard at work “selling” their product ideas — stakeholders and engineers are selling to product managers, product managers are selling to management, and management is selling to everyone (just listen attentively in the next all-hands meeting). As a product manager I too had to
Idea prioritization exercises are often hard and unsatisfying. The discussions run long and the decisions feel suboptimal. Using numeric methods like ICE or RICE adds more process, but not necessarily more clarity, as the numbers feel “made up”. I’ve written in the past about how to make better use of ICE, but I have to
I recently met a product team, let’s call it Team Red, who is developing a new product in an established, traditional industry. On the surface Team Red has it all: it is cross-functional, fully-staffed, well-funded, working towards clear business goals, and is empowered to make decisions. And yet as we sat down to talk, I
False Predictability Is Killing Product/Market Fit Read More »
There’s a popular notion that we should copy the playbooks of the most successful tech companies, aka Big Tech. The most obvious sources of inspiration are the FAANG companies: Facebook, Apple, Amazon, Netflix, and Google. So you may be told to Work Backwards like Amazon, use OKRs and 20% projects like Google, or organize according to