What does automation mean for designers?

It's rare to find a product team that hasn’t automated some part of their workflow in the pursuit of productivity. When machines handle repeatable tasks and heavy lifting, designers can focus on more meaningful work. But how does this impact how we utilize the work produced by machines?

Josh Clark, the founder of design studio Big Medium, posed this question during his talk, ‘A.I. is your New Design Material’. Some of the most remarkable technological advancements today, such as facial recognition, predictive text, and image search, are powered by machine learning. However, it's crucial to remember that all these technologies are still based on code. The benefit is a reduced margin for error; these technologies operate without emotions, expectations, or feelings affecting their performance.

However, when facial recognition fails, we often assume the entire process is flawed. But is it really?

According to Josh, understanding this is fundamental when dealing with machines. Failing to meet our human expectations doesn’t necessarily mean the technology itself is flawed. These technologies are built on logic, which raises the question: Can a robot's solution be considered wrong?

The purpose of integrating machine learning into our products was never to have them do all the work. Instead, algorithms and logic-based solutions should provide humans with better insights to empower us to arrive at better solutions faster.

This understanding of our users is what truly helps us create better products. While it may be a simple example, if a computer can learn to walk on its own, it's time to investigate why and how these solutions were formed.

How do we design for the unknown future?

Jared Spool, Co-Founder of UIE, asks, “What was the most important thing you learned yesterday, and how will it impact what you do in the future?”

As designers and researchers, we must always consider how to design products for the future, even as we meet the demands of present-day design. This is particularly challenging in an era where things evolve rapidly.

To begin, Jared suggests looking back at how our design processes have evolved.

Do you remember when UX/UI wasn't a priority for many companies? Jared, a consultant during a time before the internet reached mass market appeal, helped many companies shift their focus to consider the user experience of their products.

This retrospective allows us to understand how UX and UI have evolved over the years, providing insights into what these concepts may look like in the future. Jared introduces a concept called "The UX Tipping Point" and offers actionable steps to reach it.

In the past, designers had to fight for a seat at the table. If today you’re not starting from a place of advocating for user experience (like they were 10 years ago), you're likely not at that tipping point. Therefore, designers still need to ensure that UX matures within the company, along with an understanding of its importance. When an organization fully embraces UX design in everything it does, it has truly reached The UX Tipping Point.

Are we designing for users or ourselves?

People don't always know what they want, even if they think they do. As UX psychologist Joe Leech puts it, "People want more choices, but can't deal with them.”

So how do we design for our users if they aren’t always truthful? This is a critical question, addressed by extensive UX research.

In the 2000s, psychologists Sheena Iyengar and Mark Lepper conducted a study on consumer choices. They found that more jam was sold during a week when only 6 varieties were available compared to a week with 30 choices. Interestingly, when consumers were asked which week they preferred, they chose the one with 30 choices.

Using this analogy, Joe argues, “A designer who doesn't understand psychology is going to be more successful than an architect who doesn't understand physics."

User research, conducted on a large scale, helps teams get as close as possible to understanding the root of a user’s needs over their wants. Though studying responses on a larger scale requires more effort, it forms the foundation for true UX.