AI can design an app now. Are designers redundant?

Artificial Intelligence can now generate design elements, write labels for buttons, and transform sketches into functional prototypes in mere minutes. This raises the question that many designers have silently pondered at 2 a.m.: have designers become obsolete? To address this question accurately, we must differentiate between creating design artifacts and undertaking the actual design process.

AI streamlines the output of design—screens, copy, and prototypes—but it does not handle the critical elements, such as judgments, context, and validation. As the production of drafts becomes more accessible, the challenge shifts to the designer's judgment, context understanding, and validation capabilities. Tasks within a designer's role will be reduced.

However, the job itself will elevate in significance. Designers face redundancy only if their role is limited to producing artifacts. AI can rapidly present numerous options; however, design truly revolves around selecting which option best meets the needs of the end-users. Design is a multifaceted process, extending beyond merely creating screens.

An app feature's journey involves five layers: understanding the user and their objectives, determining priority among potential features, outlining the sequence of interactions, designing the interface, and validating the solution through real-world testing. AI excels at handling the first two layers—interface design and problem validation.

However, it falters in the subsequent stages that demand human insight: defining priorities, shaping user flows, and ensuring the product's success. This discrepancy explains why AI may appear to replace designers, while in reality, it merely automates a segment of their responsibilities. There are four compelling reasons that render designers irreplaceable: 1.

With AI drafting multiple design options swiftly, the decision-making process becomes paramount. As the time required for a single screen reduced from a day to mere minutes, teams are now tasked with comparing dozens of designs generated by AI. The crucial skill now shifts from generating options to judiciously selecting the most appropriate one.

Judging among a plethora of choices remains a scarce and indispensable skill. 2. AI lacks contextual awareness. While AI is trained on vast datasets comprising millions of apps, it hasn't experienced real-world scenarios. Consider an app designed for a supermarket setting, where tasks such as holding a basket and using a phone simultaneously present unique challenges for interface design.

These context-specific factors, such as limited hand mobility, visual distractions, and the need for swift decision-making, AI cannot grasp. Only through direct observation of users can these nuances be identified. 3. AI tends to generate the most probable answers, which are often common or average solutions. In design, however, the most likely answer typically fails to stand out.

Designers must deliberately choose to differentiate their products, steering away from the status quo. 4. Ultimately, someone must own the final product. If a design decision fails to accommodate older users, violates accessibility standards, or incorporates deceptive practices, the responsibility lies with the designer, not the AI.

Assigning design approval to a machine negates the person's accountability for user experience. The strength of AI lies in its ability to generate design options, while design's core essence lies in making deliberate choices. To assess the value of AI in the design process, we connect its capabilities to various design layers. AI shines at the interface drafting stage, generating layouts, variations, microcopy, adhering to design systems, and constructing rapid prototypes.

Nevertheless, it falls short when addressing problem definition and validation, which necessitate human expertise. While AI can summarize reviews and identify patterns across user feedback, it struggles to prioritize decisions or foresee edge cases that could harm user experience. Thus, designers remain indispensable at the higher layers of design, where AI's capabilities are limited.

Though AI enhances efficiency in certain design tasks, this advancement does not signify the end of designers' relevance. In fact, certain design tasks are becoming automated, but the design role remains intact. As products compete on user experience, target specific audiences, and face real-world risks, the upper levels of design, encompassing problem-solving, prioritization, flow, and validation, return to the forefront.

These higher-level tasks fundamentally involve human judgment and cannot be delegated to AI. For designers, the evolving landscape brings new value in areas like user research, problem framing, designing robust systems, critiquing AI-generated outputs, ensuring accessibility, addressing ethical concerns, and handling edge cases.

Conversely, producing screens from a clear specification, creating numerous variants manually, producing pixel-perfect mockups that go untested, and design work confined to the hand-off become less critical. At Nexa Tech, our approach to integrating AI revolves around a simple principle: AI accelerates tasks we can validate, while humans retain control over decisions requiring nuanced judgment.

Research tasks, such as analyzing app store reviews and feedback, utilize AI to categorize themes, but designers meticulously review raw quotes to form accurate conclusions. Similarly, AI assists in generating layout options, but the team selects a direction based on contextual user insights rather than superficial visual appeal.

In production, AI efficiently drafts screens using established components and tokens, with designers refining hierarchy, spacing, and state transitions. Validation remains a critical human responsibility, involving real user testing to validate the efficacy of AI-generated prototypes. While AI can predict user behavior, it cannot replace the critical human oversight needed to ensure a product's success.