This series of articles is based on a video for Space Garage. Click here to view it on Youtube.

The Observation

James Dyson observed that traditional vacuum cleaners lost suction when their bags became full. Applying the scientific method he questioned whether there was a better way to maintain suction power.

The Hypothesis & Experiment

Dyson ideated and developed a hypothesis around Cyclonic separation a bagless vacuum technology. After creating numerous prototypes, he iterated on the design, refining it through testing and user feedback.

The Result

Even after creating his first model of a bagless vacuum, he spent several years figuring out how to sell it. As of today Dyson has knocked it out of the park with a range of products for various industries while applying similar learnings. He also happens to be the wealthiest industrial designer ever.

The Role of Automation

So this is a clear formula.

Shouldn't we be able to automate it? Not really. As I mentioned, it's easy to look backwards and think the outcome was inevitable. No matter how sophisticated your automation tools get the adage "garbage in garbage out" stays true. There are aspects of this process where you can use generative tools like LLMs but trying to apply it like a formula is usually when it goes wrong fastest.

The Role of Design Sprints

I've had clients ask me, verbatim, "Can we make our products look like Apple?" They think we're going to go into a room, run a Google style design sprint and come out with something magical. It takes years, an enormous resources and relationships with a massive network of experts to yield a product that can match the level of an Apple product. Sometimes, that isn't even the right approach or aesthetic for their specific market.

The point of running a guided process like this is to start a cultural shift in an organization. It's great if we have deliverables at the end of a session but the goal is really to give people these tools and a tangible example of how to use them in the future.

When things don't go as planned

As I mentioned, failure is inherent in this process. Additionally the process can be very counterintuitive. This is especially important in the earlier more divergent phases. A lot of the most groundbreaking solutions come from observations using a very disparate set of data points.

In Part III we'll learn about how failure can lead to unexpected wins.

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