Why Solid Decisions Made Late Lead to Better Products

How the Rapid Learning Cycles Framework Prevents Disappointing Results in Climate Tech

All too often, I still encounter the myth that the way to deliver a better product is to investigate customer needs and define the product’s requirements at the beginning, and then lock them down. We’ve known for a long time that doesn’t work, because it forces decisions to be made too early.

This problem is even worse in areas like climate tech where so much of the landscape is undergoing rapid change. We make decisions based upon the best available information today — and then things shift. Elections lead to new public policy directions; major climate events lead to different consumer behaviors; technical breakthroughs create new opportunities.

All of this means that making decisions too early practically guarantees that those decisions will be revisited when new information comes in.

Decisions Made Too Early Must Be Revisited

When a team makes a decision without the knowledge they need to make the right one, they’re dependent upon making a lucky guess. Usually, that means the decision will be revisited when the problems with the decision pop to the surface, usually at the worst possible time: right before a major customer demonstration, or during the production pilot run with launch just weeks away.

Even if one of these decisions is isolated with minimum impact on the rest of the product design, the team has to find time and absorb the expense to fix the problem. But often, these major decisions trigger a big wave of rework: a redesign of a handle on a new medical instrument to respond to customer complaints about comfort requires everything inside the handle to be laid out differently.

It would seem like the way to avoid these loopbacks is to make the right decisions in the first place, and there has been a lot written about better methods for customer research and better requirements engineering to ensure that these early decisions are good decisions. But all of that only goes so far to prevent loopbacks, because new information comes in all the time.

Rapid Learning Cycles delivers results by eliminating these loopbacks at the source.