Artificial Intelligence has become a buzzword in product development and marketing. From predictive typing to generative art, AI’s reach is omnipresent, but not all AI-powered solutions resonate with users. As designers, we must ask ourselves: do users truly care if a product is labeled “made better with AI✨”? More importantly, what does “better✨” mean to them?

To answer these questions, we need to explore the intersection of AI integration and meaningful user experience (UX) design.

The Problem With “AI-Enhanced✨” Labels

AI is often promoted as a magic wand that elevates products to new heights. However, slapping “AI-powered” onto a service or feature doesn’t guarantee success. While users may be intrigued by the term, their primary concerns revolve around utility, reliability, and relevance. Let’s break this down:

AI Doesn’t Excuse Poor Design

A poorly designed product doesn’t become usable just because AI is involved. A chatbot that misunderstands user queries or a recommendation engine that offers irrelevant suggestions can frustrate users, no matter how sophisticated the underlying algorithm.

Despite competing with successful assistants like Alexa and Siri, Samsung’s Bixby received widespread criticism for being unintuitive and offering limited functionality. Users found Bixby’s voice commands redundant and unhelpful compared to established alternatives. The AI ✨ label didn’t save the feature from feeling like an unnecessary addition.

Users Care About Outcomes, Not Technology

The end goal for users is to solve a problem or achieve a goal. Whether AI is part of the process is secondary. For instance, when using a grammar-checking tool, users value accurate corrections and clarity over the mechanics of how those suggestions are generated.

Google Stadia, a cloud-based gaming platform, used AI to optimize game streaming performance. However, the platform struggled with fundamental issues such as latency, limited game libraries, and unclear value propositions compared to traditional consoles. Gamers didn’t care about AI✨-powered streaming if the core gaming experience was subpar.

Distrust in Gimmicks

Overuse of “AI” in branding can lead to skepticism. If users perceive the feature as an unnecessary addition or a marketing ploy, trust in the product — and by extension, the brand — erodes.

Clearview AI’s facial recognition technology has been criticized for ethical concerns and lack of transparency. While the company marketed its AI as transformative for law enforcement, the controversial use of personal data without user consent sparked widespread distrust. Users viewed it as invasive and unethical rather than helpful.

What “Made Better With AI” Should Mean in UX

The phrase “made better with AI” should signify tangible improvements to the user experience. It should not be about the novelty of AI itself but about how it helps users achieve their goals faster, easier, or with more enjoyment. Here are some principles designers should follow:

Contextual Intelligence

AI should operate in the background, surfacing only when it adds value. For example, an email app that suggests responses based on the message’s context is helpful because it reduces cognitive load. However, if the suggestions are generic or irrelevant, the feature becomes a hindrance.

Google Photos uses AI to identify faces, objects, and scenes, helping users categorize and retrieve their memories effortlessly. Features like “suggested edits” or “rediscover this day” provide timely, contextual prompts, enhancing the experience without overwhelming the user. For instance, AI might suggest enhancing the brightness of a dark photo, saving users the time and effort of manual editing. This unobtrusive use of AI directly improves the user’s interaction with the app.

Empowering Users, Not Replacing Them

AI should augment human capabilities, not replace them entirely. Consider design tools like Figma or Adobe Photoshop. Generative AI in these platforms assists designers by automating repetitive tasks, offering inspiration, or providing suggestions. Yet, the creative control remains firmly in the hands of the user.

Adobe’s integration of AI into Photoshop enables users to expand or alter images with generative fill features. For instance, users can highlight an area and ask Photoshop to “extend” the background or add objects. The AI assists with creative tasks without taking control, leaving the artistic decisions to the designer. This partnership between AI and the user enhances creativity while maintaining user agency.

Ethical and Transparent AI

Users deserve to know when and how AI influences their experience. Transparency builds trust, especially in critical domains like finance or healthcare. For instance, a financial app that provides credit scores should clearly explain the factors considered, rather than presenting opaque results.

Spotify’s AI-curated playlist “Discover Weekly” delivers personalized music recommendations. By transparently explaining that recommendations are based on listening history and user patterns, Spotify makes users feel in control. Users trust the algorithm because it consistently delivers relevant and enjoyable results.

Personalization Without Invasion

Personalization is one of AI’s strengths, but it must be approached carefully. Over-personalization or using data in ways users don’t understand can feel invasive. Spotify’s algorithm-driven playlists work because they balance personalization with discovery, keeping the user experience fresh without crossing boundaries.

Netflix’s AI recommends shows and movies based on user preferences and viewing habits. While deeply personalized, the algorithm ensures diversity in recommendations to avoid over-personalization. By balancing familiarity and exploration, Netflix enhances entertainment discovery without making the experience feel overly scripted or invasive.

What Product Designers Should Do

To harness AI effectively, designers must prioritize the user experience over the technology. Here are actionable steps to align AI implementation with user needs:

Understand User Problems Deeply

Start with the fundamentals of UX research: understand your users’ pain points and aspirations. If users don’t find a feature useful or if it doesn’t solve a pressing problem, AI won’t magically make it relevant. For example, integrating AI into a fitness app should focus on providing actionable insights, like tailored workout plans, rather than flashy but redundant metrics.

Prototype and Test Extensively

AI systems are complex, and their behavior can be unpredictable. Prototyping AI-driven features requires testing not only for functionality but also for usability. For instance, voice assistants like Alexa or Siri require rigorous testing across diverse accents, languages, and use cases to ensure reliability.

Focus on Usability, Not Novelty

Novelty has a short shelf life. A feature might impress users initially, but if it lacks depth or long-term utility, it will be abandoned. Instead of introducing AI features for their “wow” factor, designers should focus on simplifying user workflows, reducing decision fatigue, or enhancing accessibility.

Human-Centric AI Design

Design AI interactions that feel natural and intuitive. A great example is Google Maps’ predictive traffic analysis. Users don’t see the complexity of machine learning models — they experience a smoother, more efficient route to their destination. The technology serves the user without overwhelming them.

Measure Impact, Not Engagement

It’s tempting to measure the success of AI features through engagement rates. However, the real measure of success is whether the feature improves users’ lives. Metrics like task completion rates, error reductions, or user satisfaction scores are more telling.

Balancing Innovation and Responsibility

AI in product design comes with ethical responsibilities. Designers must consider unintended consequences, biases, and the broader impact of their choices. Generative AI, for instance, has raised concerns about misinformation, copyright violations, and ethical dilemmas. Balancing innovation with responsibility requires vigilance and a commitment to user welfare.

Address Bias

AI models learn from data, and data often carries biases. Designers and engineers must work together to identify and mitigate these biases, ensuring equitable experiences for all users. A biased algorithm in hiring platforms or healthcare applications can perpetuate discrimination, harming users rather than helping them.

Prevent Dependency

Over-reliance on AI can diminish users’ critical thinking or problem-solving skills. For instance, GPS overuse can erode natural navigation skills. Designers should provide options for users to engage actively with the system, maintaining a balance between convenience and control.

Plan for Failures

No AI system is perfect. Designers must anticipate failures and design fallback mechanisms. A chatbot, for instance, should gracefully hand over to a human operator when it cannot resolve a user query.

Designing AI for Humans

Ultimately, the success of “AI-enhanced” products hinges on the user, not the technology. Users don’t care about AI for its own sake; they care about experiences that are seamless, intuitive, and empowering. As designers, our responsibility is to ensure that AI serves as a tool for enhancing human potential, not as a shiny label to boost market appeal.

In the rush to integrate AI, let us not lose sight of the principles that define great design: empathy, simplicity, and utility. AI is not the destination — it is a means to create better experiences. And when done right, users may not even notice the AI working in the background, because they’re too busy enjoying how much easier their lives have become.