On an early winter morning in 1996, I heard a loud knock on my dorm room door. It was 2 a.m., and as I was waking up, I could gather some of the words spoken loudly by my wilderness instructor, Tom Lamberth, from behind the door.

“Good morning Baha, there is a missing skier, and we have been called to assist in the search. We leave in 40 minutes.”

That’s all Tom said as he continued his rush down the hallway to wake up the rest of the team members in their rooms. Tom was the wilderness instructor during my time at the United World College of the American West (UWC USA) in New Mexico. The reason he was knocking on my door early that morning, I was a team leader in the wilderness program.

In his book Learned Optimism Martin Seligman describes a study in 1985 that focused on fifteen thousand insurance agent applicants to Met Life [1]. The study involved one thousand of the fifteen thousand applicants who failed the standard industry test, but who took an additional test called the ASQ that determined whether they were optimists or pessimists. The higher the ASQ score the more optimistic an agent was determined to be. The goal of the study was to hire these agents as part of Met Life’s workforce and measure the performance of optimistic agents compared to the pessimistic ones.

The methodology and best practices behind design are constantly evolving, yet they have always been deeply rooted in Human-Computer Interaction (HCI). I think about how my career progressed in context with the rapidly changing nature and landscape of design and usability, especially when it comes to the lightning speed with which AI technologies have evolved and the ubiquity of user interfaces and technologies supporting them.

On a recent project I worked on I found that I was not very clear on the subject matter and the complexity of the problems that were presented. I did not know any of the business stakeholders well, and while I had previously worked with some of the project team members, I had not yet developed a meaningful working relationship with them. I needed to get up to speed quickly so that I could start thinking about how to run discovery sessions, and how to frame the problem and ask the right questions in my stakeholder interviews.

In this article, I discuss why design serves as an excellent example of a profession that defines how AI can assist designers by allowing them to produce superior designs with greater efficiency, rather than supplanting the essential skills that proficient designers contribute to their field. I show how AI can make the future of design more exciting and promising as the technology continues to evolve and enable designers to do more. In the process, AI will enable designers to focus on developing the design skills that matter, namely those anchored deeply in design thinking, empathy and user research.  

In this case study, we examine a team that has recognized the potential of Machine Learning (ML) and Artificial Intelligence (AI) to refine and enhance a longstanding methodology for forecasting product order volumes. By leveraging AI and ML, the team can achieve more precise ordering based on those forecasts, while also gaining the capability to monitor market prices and receive insights on how to adjust orders to minimize costs. Historically, this team has relied on Excel for manual and meticulous user input, maintaining continuous communication among members and adhering to a process honed over several decades. While this approach has been effective, they have now realized that integrating AI and ML can significantly enhance their workflow by handling larger datasets at a faster pace and generating profound insights aimed at maximizing efficiency, reducing costs, and driving business growth.  

The rapid integration of Artificial Intelligence (AI) and Machine Learning (ML) capabilities into products and applications necessitates a shift in the role of Product Designers.

AI offers significant opportunities for organizations to address complex business problems. Users are now able to provide input into detailed data models that can process extensive datasets and generate insights through what-if scenarios and simulations.

AI-driven scenarios and simulations often rely on substantial input data and calibration tailored to user needs. As output complexity increases, it becomes essential for Product Designers to be at the forefront, understanding these sophisticated models and shaping designs that present the results in an intuitive, user-friendly way.