Will AI Replace UI UX Design?

When we look at UX Design as a discipline, we understand that it is a combination of two main phases, each with a specific set of activities. The first phase handles the theoretical aspects—the abstract study of human dynamics. This primarily involves information collection and information synthesis regarding human behavior. The second phase handles the execution of information gathered in the first, and this is where the majority of the structural and visual work happens with the help of design tools.

That being said, now let's try to understand how AI can impact, or is impacting, UX Design as a discipline. My answer to this, which is based on my 15 years of design experience and working with AI tools on a daily basis, is that AI has limited impact on UX design and it cannot replace UI/UX designers completely. This would only happen if we, as humans, really wanted to experiment, push the envelope, and leave ourselves open to unintended consequences. Just like every other digital discipline is now being augmented by AI (or in some cases are nearing replacement), UX, I believe, is arguably the only discipline where AI will primarily serve as an augmentation tool, not a replacement. Furthermore, in contrast to not-so-common speculations in the industry on the AI-driven death of UX design, the reality is far more encouraging and revitalizing. We can also be assured that negative speculations on UX are born out of a poor understanding and execution of the UX discipline, the rationale for which we will explore in detail throughout this article.

UI/UX design is not a single skill; it is a combination of multiple soft (or interpersonal), analytical, and practical skills. This is why, within the User Experience domain, we have a variety of specializations, such as UX Researcher, UX Architect, Interaction Designer, Visual/UI Designer, and UX Writer. This specialization continues even further, as within UX Research, for example, we find Qualitative and Quantitative researchers. Now, consider: which, and how many, of these skills can be replaced by AI? Which skills will be augmented by AI, and which cannot be replaced at all? Even before we reach the end of this article, you will have your answer by simply exploring these questions.

However, there is more to the story of the evolution of UX Design in the age of AI: the fundamental UX skill itself is not transforming; rather, the tools are transforming because of AI. The core UX principles that address human dynamics remain the same. Because of the application of AI in UI/UX tools, we are now witnessing the democratization of some parts of the discipline.

So what does this mean in practice, and what are its implications for future companies, entrepreneurs, startups, solopreneurs, design professionals, product managers, and freelancers building digital products? Well, for one, AI is making design tools accessible to everyone but at the same time setting the wrong precedent for poor design.

For example, prior to the integration of AI into popular design tools like Figma, or prior to the emergence of specialized UI design tools like Lovable or UIzard, only skilled designers could utilize their expertise to design an initial version of a digital product. But now, anyone on the team can create initial designs with the help of a prompt and do so iteratively. However, this common approach to designing user interfaces with AI is not without its flaws. These flaws are deep, often emanating from a poor understanding of the UX design discipline as a process. This shift, however, hints that the responsibility of user experience is moving from a designer-specific domain into a universal organizational standard necessary for delivering intuitive, user-centric products. Therefore, a team-wide understanding of the UX design process and concepts becomes even more critical, although the heavy lifting and in-depth design work will still be done by dedicated designers. Without an essential understanding of UX concepts, the product or service we may be trying to build for our users will lack the critical human touch, born out of the most fundamental and core UX principle: Empathy.

If we analyze all the UX methodologies that exist today—for example, Design Thinking, Double Diamond, Lean Design, or UCD—empathy is the thread and the binder that runs through the entire UI/UX design process. At each stage of UI/UX Design, designers strive to achieve optimal user experience by continuously and iteratively empathizing with users by conducting user research, synthesizing the research with methods like Empathy Maps, User Journeys, and Personas, and then testing wireframes, mockups, or prototypes to iteratively improve the designs.

A typical UI/UX process, as I mentioned in the beginning, largely involves three phases. The first phase is understanding the users, which designers do by (1) conducting user research, (2) synthesizing the user research, and (3) UI planning & designing.

Whether qualitative or quantitative, the decision to employ the type of research is at the discretion of the designers. This decision is predominantly influenced by the product's development stage, as well as constraints under which designers must operate (e.g., time, budget, access to tools, and, in many cases, the degree of importance given to user research by company leadership).

This phase is strictly a human endeavor, and AI has a very limited role here. Anyone who thinks AI can entirely replace humans in this phase will quickly discover the futility of such an approach.

When designers conduct one-on-one user interviews, it is the human-to-human interaction that can yield first-hand new insights, grounded in genuine human experience. AI, on the other hand, can help synthesize interview transcripts or audio recordings, highlight recurring patterns, simplify the findings, and even present the findings visually. Furthermore, even prior to conducting the interview, AI can help formulate ideal interview questions. However, all the work that AI is capable of, even at this stage, remains secondary. The research approach has to be human-first. This is why I never teach my students how to use AI at this stage; I want them to first do the exercise with real people, come up with their own interpretations, and only then utilize AI to either add value to their findings or accelerate documentation. Without a human-first approach, any company attempting to use AI solely for product development risks wasting effort on building superficial and unoriginal products. There is a fundamental, innate reason for this, and it’s human creativity. We can break human creativity, particularly in the context of innovation, into three key components:

1. Knowledge (Data): The base information.

2. Synthesis: The ability to identify and create novel relationships by combining existing knowledge and data in unconventional ways. (This ability is negligible in AI, which primarily generates variations based on patterns it was trained on.)

3. Experience: The subjective, emotional, and cultural context (lived experience) necessary for evaluating the synthesized relationships for genuine human resonance and impact. (This is non-existent in AI.)

Once designers have curated sufficient research, whether qualitative or quantitative, we then move to synthesizing this information. To do that, we have multiple methods at our disposal: we use Journey Maps to evaluate the user's entire journey when engaging with the product; we use Affinity Maps to classify patterns; we use Empathy Maps to evaluate behavioral and psychological patterns; and we develop rich Personas to direct design decisions. Although personas can also be created at the onset, it is always best to revise them based on insights gained from research. Designers may brainstorm on features, scope, define problem statements, and develop initial sketches and crude prototypes.

This phase is a highly collaborative approach, as it is through human collaboration at this stage that deep, unexpected, and diverse insights and ideas emerge. This is a phase of absolute chaos and synergy that enables innovation; here, collective human ingenuity takes shape by brainstorming to envision innovative ideas. AI has a very limited role here, if any. It can definitely be utilized to help do various surface-level tasks, like creating a template for journey maps in FigJam using a prompt or helping define initial minimal personas in a GPT model based on project requirements. The tasks that AI can do here are only optional, as Affinity Maps, Journey Maps, Empathy Maps, brainstorming, and developing personas can be done with basic tools like whiteboards, pen, and paper. However, it does help to digitize the findings for later processing. Synthesis phase, with user research as its foundation, is one of the most ignored phases by companies that do not understand UX design as a process, or perceive UI as UX, or rush the product for whatever reason, either to satisfy stakeholders or clients.

Once the research has been synthesized and scoped, the UX process begins to move from the abstract phase to the concrete phase, which is Information Architecture. Information Architecture is not fundamentally User Interface Design but a step between UX and UI where information for the product is structured. Here, designers create sitemaps, do card sorting exercises, and evaluate user flows before moving into UI design to develop low-fidelity to mid-fidelity to high-fidelity wireframes, prototypes, and final mockups.

Interestingly, as AI tools are now capable of developing designs or design-related assets based on prompts or design documents, we are witnessing the highest adoption of AI tools here, primarily for UI Design. As for Information Architecture, AI is capable of creating sitemaps and user flows to some extent, but the whole point of doing Information Architecture is to validate the information structure with real users through user testing and methods like card sorting.

Just because AI is capable of creating these visual and structural assets, a fundamental misconception is being amplified that AI will replace UI/UX Design. However, the reality is far from it. In fact, human dynamics in the UX/UI process will never be replaced, as AI is only a companion that can accelerate the process. Without validating structures and designs—either created by AI or human or both—with real users, we will only do injustice to our users, stakeholders, and the product itself, missing opportunities to develop innovative and timeless products, wasting time and efforts repeatedly.

On the contrary, since AI tools are able to automate this phase to a large extent, this is also adding to the misconception that anyone can now design. Again, reality is far from it. Just as vibe coding is not coding, vibe designing is not designing. In fact, it is even more difficult to design using prompts and create something that is original. AI design tools may have the flair, but they lack originality, storytelling, and optimal user connection. Just as empathy is the thread that binds the entire UX Design process, user testing and iteration are critical aspects that bring incremental cohesion to the products we are designing for our users.

The evolution of UX design in the age of AI isn't a story of replacement; it's a story of realignment. The core of the discipline—the abstract study and synthesis of human dynamics—remains fiercely human. AI fundamentally serves as an accelerant, not a substitute.

It excels at the repetitive, visual, and structural tasks of the second phase (UI design and initial Information Architecture), automating the creation of screens and assets based on patterns. However, these artifacts are only useful if they are built upon the robust, empathetic foundation laid in the first phase: Research and Synthesis.

The future of the UX designer isn't about being fast with Figma or perfect with prompts; it's about being the guardian of human experience. As AI democratizes the surface-level creation of digital products, the true value of the dedicated designer shifts. They move from merely executing designs to becoming indispensable strategists, validating the empathy that AI cannot generate and iterating with real users to achieve true product cohesion. For non-designers, it is now an opportunity to learn core fundamentals of UX Design to make the best of AI Design tools and create products that resonate with users. If you're ready to bridge the gap between AI tools and human-centered strategy, check out my comprehensive UX/UI Design course to master the essential principles that truly drive innovation. Ultimately, AI allows UX professionals and non-designers alike to leave the canvas and focus on the messy, complex, and vital work of understanding people. Far from facing extinction, the UX discipline is being revitalized, freed to focus on the human problems that only humans can truly solve.