There’s no shortage of AI hype at the moment. An interesting part of the speculation is devoted to the future (or lack there of) of product management. In this article I’ll present the three scenarios of the future PM that are circulating in the product-sphere and what I think of them. Then I’ll discuss a […]
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.
We are regularly told that AI has caused a deep shift in product development. The old patterns are no longer relevant, and you have to either adapt or die. In this article I’ll try to assess if that’s indeed the case. Have we fundamentally changed the way we develop and sell tech products? Is this
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
All tech companies have to choose what to build, but approaches vary — from top-down projects to bottom-up research and experimentation. Your company is probably somewhere on this gamut, but it’s not always a deliberate choice. It’s important to realize that these are not the only two possible models, and that there isn’t necessarily just
Impact is a central concept in product development, but it’s often unerdused or misused. In this article I want to suggest a thinking tool, The Total Impact Matrix, to help you use it better. The impact of an idea is the sum of value it creates for our target audience — customers/users/partners — and for
By now you must have heard an awful lot about how gen-AI will “10X” tech workers’ productivity and massively cut development costs. This promise is music to the ears of executives and tech-eutopians, but currently is not backed by any rigorous research (most of the evidence comes from coding competitions and reported usage — mostly
In the first article in this series I reviewed common prioritization approaches, including opinion/consensus/HiPPO, rules-of-thumb, and Cost of Delay. I also explained why prioritization as part of product discovery — prioritizing what to test — is far more effective than prioritizing what to build. You greatly reduce the cost of prioritization errors, and at the
Prioritization is the process of choosing which ideas/bets to invest in and which to postpone or park. It entails two cognitively-hard tasks: 1) evaluating the merit of each idea, and 2) comparing multiple ideas and choosing an order of precedence. If that’s not difficult enough, prioritization decisions are often a flash point of debate and
Building great products — ones that deliver high value to the customers and to the company — is extremely hard. This is evident from discussions I hold with product people, who regularly report not being able to create impact. It’s also clear from data: A/B experiment statistics, feature usage data, and startup failure rates, all