Design Challenges in the Age of AI
Design is an ever-evolving field. So much so that it’s hard to keep track of design job titles. Originally, product designers designed industrial products while graphic designers focused on print as a medium. As a creative field, Design found its Analogy with architecture; it was required to be functional, usable, and predictable, while also being beautiful, emotional, and novel. Designers considered business priorities, user needs, and material constraints amongst other factors, and designed the solutions. These factors (or requirements) remained constant while the designers channeled their creativity to come up with innovative and perfectionist solutions for the target market. The present: Create for the personas In the digital world, Design borrowed tenets from the previous era but with one fundamental difference: the solution became a living entity. The digital “Product” of today constantly learns about the user’s understanding of itself and evolves as it learns. The requirement comes in batches and continuously builds on top of the existing solution. Markers in the product give a quantitative assessment of the user’s behavior; We track metrics such as number of sessions, frequency of sessions, time of the day when sessions happen, activation through notification vs organic, session duration, most used buttons (or features), click order per screen, time spent per screen, scrolls per screen, number of list items consumed per screen, etc. A measure of each metric highlights high-level patterns and preferences of the users. I personally prefer percentile (80th) over averages to eliminate outliers. Eg: A “clickstream” visualization here shows patterns for user clicks in a session, we also used a similar visualization for “timestream” which showed time spent patterns in a session. Clickstream: each circle represents a screen, and colors in a circle represent features (or buttons) on the screen. Each “pie” is the percentage of users (on the screen) who clicked on the respective feature. Now, in a data-informed environment, these numbers drive product and design decisions and experiments. These numbers impact product, product impacts user behavior, user behavior impacts the numbers, and hence product evolution is an ongoing exercise always. Since we identify high-level patterns through these metrics, we are essentially designing for Personas. For example, if many of the users come online after 10 pm regularly, we will notice that behavior will probably include a night-mode feature. These users will likely be teenagers (hypothetically), so we are designing for the teenage persona in this case. Also, even before starting to design a product, our ethnographic studies focus on bucketing users in personas. Hence, both our qualitative and quantitative indicators of the day help us in designing for these Personas. The Future: Create for the individuals You must have had some magical experiences with a product. Some feature, some text, or some graphic which really made you feel like the product really got you. I remember Google Home picked out the top 10 restaurants in my area for my anniversary. And Cleartrip highlighted a local holiday for me while making a flight booking for the said date. What if magical experiences were a norm? Personalization in content is ubiquitous these days. Netflix, TikTok, Instagram (discover), Youtube, Facebook, LinkedIn, and all the spooky internet ads are examples of content based on personalized interests. But now, we’re on the verge of elevating it to the product itself. For example, with respect to visual design, here’s an AI churning out a whopping 400 million visual design permutations. LuBan is powered by ML. Here, it churns out 400 million visual permutations. Design’s objective has always been about designing for users a.k.a User-Centric-Design. Techniques such as ethnographic studies, customer journey maps, validation exercises, surveys, product metrics, etc are all measures…
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