Lucas Persona is Chief Digital Officer at CI&T, a digital transformation partner for global brands.
Wherever you look today, the rapid advancement of generative AI is creating a state of flux for most industries and their related disciplines. While benefits like efficiency gains and cost reductions are crystal clear, uncertainty surrounds the future of human experts alongside an ever-evolving creative and productive force like AI.
Like most creative and knowledge-based professionals, user experience (UX) designers are pondering how their skills will stay relevant in an era where tools like Figma can generate entire designs from simple inputs.
Yet this concern is far more nuanced than “Will AI replace us?”
Instead, the question becomes, “What new user experiences does AI make possible? And what are the best practices for such unchartered waters?”
Addressing Persistent UX Challenges
Perfecting UX has long been a reductionist challenge, with designers iterating through countless possibilities until they track down the best solution for most users. Until now, this scenario has worked well on the business level since the alternative was a never-ending development scope that was too expensive to pursue.
There’s no question that cost considerations are vital, but they limit the scope of creative exploration and personalization in UX. Budget constraints force designers to approach their craft through a rigid, step-by-step process that emphasizes “how” to improve UX rather than “why” users have particular objectives or “what” the end goal should look like.
With generative AI, the UX world has the opportunity for a radical departure from these traditional constraints, enabling us to create thousands of variations of apps, web interfaces or digital experiences that constantly adapt and evolve to user preferences.
The key is to embrace a transformative shift in mindset that goes beyond efficiency gains and cost reductions.
Continuous Adaptation: Next-Gen UX Personalization
The first shift in mindset recognizes that generative AI empowers UX professionals to create dynamic, responsive systems that adapt to user behavior and preferences in real time.
Instead of aiming for a catch-all design, UX professionals can now design around nuanced user behaviors and intents, likes and dislikes, and granular interactions like clicks and swipes. AI shifts the focus from a singular user experience to a spectrum accommodating diverse preferences and needs.
Take e-commerce, for example. Traditionally, UX designers meticulously map out every screen flow and user action, from searching to filtering results, then adapt accordingly to suit the majority of users. Right now, a search for something like “coffee” comes with toggle filters like brand, size or rating. Yet a more specific text search like “8 ounces of ground Brazilian coffee” yields the same functionality—present a broad category, then allow users to narrow it down. AI-powered searches can adapt and respond more organically, guiding users to the right products much faster.
AI also opens up new possibilities for creating more intuitive and personalized experiences. For instance, an automotive brand’s website could highlight vehicle features based on individual user preferences—a family-focused buyer might see safety features, while a sports car enthusiast sees performance metrics.
Moreover, an apparel brand could use AI-generated images to demonstrate clothing or products on models that mirror the customer’s appearance or preferred style. Companies could base this feature on an individual’s browsing history and past purchases to ensure relevant sizing and fit representation in the images.
These examples are a far cry from an all-encompassing AI chatbot, which can actually dilute the user interface experience; they require flexible AI models that can adapt to user inputs backed by structured human guidance. The result is an intuitive, personalized experience for everyone.
Human Guidance In AI-Powered UX Design
Traditionally, UX designers have concentrated on creating something tangible, like wireframes, while contemplating the underlying purpose of the design. However, with AI tools capable of rapidly producing initial drafts, designers now need to think more strategically upfront about their objectives and desired outcomes.
UX professionals will need to meticulously plan whatever they ask AI to create to align closely with their vision. As a result, their role becomes about setting clear intentions to generate the best possible results.
For instance, in product descriptions, the content must resonate with user interests. A UX designer would need to define this objective and parameters for the AI to align with that goal. Over time, the effectiveness of this approach can be measured through user engagement or conversion rates, offering chances to reiterate and providing tangible proof of success.
Aside from the creative aspect of setting intent and objectives, there’s the technical side of producing material components, which generative AI tools will increasingly handle. Designers will need to guide these tools and decide which of the myriad AI-generated materials to use—a crucial process and skill set as AI drives the cost of production toward zero.
The interaction between UX and development teams will also transform. Developers often act as translators, converting detailed UX instructions into code. But as UX embraces a more generative approach, the need for such detailed instructions goes away.
These factors influence the second mindset shift: reduce focus on detailed, prescriptive execution processes to think more about the reasoning behind a design. This new-found freedom will allow UX teams to prove, without a doubt, that they achieved their objectives, generated value and met organizational goals.
Generative UX: The Next Evolution
With rapid advancements in generative AI, crafting a user experience tailored perfectly for each individual is becoming increasingly feasible. UX professionals will become the tutors of these experiences, setting the foundation for AI tools to evolve and grow within specific boundaries.
The objective becomes less about delivering a “final UX” and more about a “learning UX” that can adapt on the fly and works from a foundation of “why” (the user needs and expectations) and “what” (the end goal; the final result).
As we see it, this incoming wave of generative UX will add a layer of sophistication that expands the creative canvas and transcends the capabilities of traditional UX solutions.
And UX professionals are vital to bring about that change.
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