In our industry, we love to invent new technology that will be innovative, interesting, and valuable. What we don’t always do is figure out if what we are building will actually solve our users’ problems, or if we’re just trying to move our product roadmap down the line. For example, if you work at a software company as a designer or researcher, the company likely is familiar with the direction they want to take the product that you work on. They say, “We think we need to build this feature so that our users won’t have this problem anymore.” The first thing to ask yourself is:
“Will more or improved technology solve this problem?”
Of course, you won’t be able to answer this question off the bat without doing some user research to validate or invalidate your hypothesis. People typically think they know what will solve their problems, but that’s why we do research, to see if they’re right!
Working in a technology organization, while planning or conducting generative research, it can be common to narrow the focus on a specific technology, usually whatever the company is already working on. However, it can be beneficial to remove the specific technology constrain altogether, in order to widen the scope of possibilities for insights.
When conducting generative research, where the team develops a deeper understanding of users in order to find opportunities for solutions and innovation, it’s especially important that we maintain a technology-agnostic approach. By technology-agnostic, I mean to take the specific technology used for the solution out of the equation, at least at the beginning. Of course, you need to have general guardrails for your research, but focusing on the problem during the discovery phase of product development allows the team to be more flexible and focus their time on the right things instead of “solutioning” before they’ve done their due diligence.
Keeping with the technology-agnostic thought pattern, there are many examples in the real world of applying new technology to solve an existing problem, and solving a problem without “new” technology. One such example of applying new technology to solve an existing problem is the self-driving car. The problem that the self-driving car sets out to solve is that people who drive cause a lot of accidents, so naturally if we remove people from the equation and just leave driving to the cars, there will be fewer accidents. Self-driving cars are a difficult thing to do successfully, however, and many people today would feel more comfortable driving the car themselves, rather than have the car's computer system take them to work in the morning.
Take a moment and think about how you may begin to learn about reducing car accidents. Do we start by looking at the kinds of cars people drive? How far and how often people drive on average? Locations where the most accidents occur and why? If generative research is on the docket, while it may seem counterintuitive to take the technology (cars) out of the equation, it can indeed help put a stake in the ground of where to go digging.
What causes car accidents? There’s a lot of factors here, but we can assume that the vehicles themselves are probably lower on the totem pole of concerns behind the driver’s impairments (alcohol, sleep deprivation), poorly designed intersections, and distracted driving using a smartphone. Now we can see that there’s a lot of plausible starting points to solving the car accident problem, rather than just saying, “It must be the car that needs fixing.”
One possible solution to reduce excessive amounts of car accidents that doesn’t require new or yet-to-be-developed technology is increased, better public transit. At least in cities, if most people could take reliable, clean, and affordable public transit to get around instead of driving, naturally there would be much fewer accidents. In America, there are only a handful of cities today that do public transportation really well, and only a handful are investing heavily in improving their infrastructure
This article is not about selling you the idea of public transit, but to illustrate there are many different ways to solve problems, and not all of them require new technology all the time. The benefit of doing generative research is not being tied to a specific technology, meaning that you may discover a new direction or concept that you wouldn’t have previously considered if you were investigating a problem through the lens of a specific technology. When using technology as a hammer to solve all problems from the beginning, everything looks like a nail.
Zach is a Research and Design Associate at the User Experience Center. Prior to joining the UXC, he worked as a Product Designer at Integrate, a marketing technology company based in Phoenix. He contributed design, user research, and product-informed strategy for several product development teams.
Zach holds a Bachelor of Arts in Interdisciplinary Studies with emphases in Psychology and Interior Architecture from Woodbury University and a certificate in User Experience Design from Designlab. He is currently pursuing a Master of Science in Human Factors in Information Design from Bentley University.