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
Everyone’s figuring out their AI strategy now. But a strategy has to be guided by a vision — how the world will be different, for better or for worse, due to AI, and how these changes will affect your target market. Armed with this vision you can choose your part in the future (mission) and
Developing a Transformational Vision for AI and Beyond Read More »
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
We all strive for certainty — if we do X, Y will happen. Certainty is comforting; it allows us to operate without fear, doubt, or having to think too much. Many of the processes your company employs, especially strategy, roadmap, and project planning, are meant to create certainty — Here’s what we need to do
Embracing Uncertainty: A Modern Take On Strategy, Goals, and Roadmaps Read More »
Objectives and Key Results (OKR) is one of the most common processes in the industry, and yet, despite 50 years of history, it’s the one companies struggle with the most. This problem is most evident when talking with product people and product leaders — the same challenges and frustrations come up again and again. So
Data is the lifeblood of product companies, but with access to lots of information come big challenges. Our brains were not designed to process large amounts of facts and figures and cognitive biases may kick in to create a simplified, and often wrong, narrative based on a few salient data points. We’re also tempted to
In part 1 of this article series I discussed the principles high-performing companies use to accelerate and improve the quality of their decisions. In this article I want to give you specific techniques to do the same, including the ACID model, not judging decisions by their results, and publicly killing HiPPOs. This is a long
Many organizations struggle with decisions-making: both speed and quality are well below where they need to be. Key contributing factors include: While these challenges are common, slow, bad decisions are not a universal problem. Some organizations are able to make good decisions fast and at scale. In this two-part article series I’ll cover the most
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,