The inherent correlation between scope of impact, and level of uncertainty.

In large scale organizations with a healthy design function, ‘discovery’ is often described as the practice of informing the business through exploratory (usually research) work — a concept popularized by folks like the great Marty Cagan. Unlike its counterpart, delivery, which focuses on executing and bringing designs to market, discovery involves a preparatory phase. It’s akin to target shooting: assessing the environment, calculating distances, and making adjustments before taking the shot.

Discovery encompasses a wide array of activities, making it a nebulous term, especially in medium to large enterprises. From exploring new markets like generative AI or sustainability, or even reviewing if you should be restructuring swathes of your product portfolio, to refining specific feature requirements best ‘bang for the buck’, the scope of what people consider ‘discovery’ is often so vast and varied that usage of the term might not actually give any of us a better idea of what you’re actually doing.

To truly excel in discovery, organizations must develop a rubric for classifying and managing them. I’m here to argue that the most effective way to do this is to use the type of questions discovery is trying to answer as the guiding principle for this categorization and subsequent project design.