AI’s advancement has led to a major shift in the design industry. More than ever, now designers are appreciating AI for its highly useful capabilities like handling large datasets, generating visual prototypes, spotting usability issues, and more.

While AI can augment every step of the design process, it still has limitations:

• AI cannot yet produce a fully cohesive and reliable end-to-end design process without human intervention.
• Designers need to critically evaluate AI outputs and provide intentionality at every stage.

This blog provides a balanced perspective on leveraging AI while acknowledging its current limitations in design workflows. So keep reading to learn about the top 5 most useful and trending use cases of AI that you can integrate in your workflow to cut down grunt work. While also understanding its limitations to use AI ethically in your design workflow.

Top 5 AI in Design trends of 2025

1. Using AI to cut down label compliance process by 75%

Did you know AI can cut down your label review process from weeks to minutes in just a single click? Yes, this is no clickbait but a possibility made true by software like GoVisually. Using GoVisually’s brands can easily

• Cut down 2-6 weeks of label reviewing process to minutes in just simple upload and click process
• Check labels for global standards and specific guidelines like US CBD food products, Non-GMO projects etc.
• Scale to thousands of labels
• Automate the label review process to achieve 90% reduction in total review time
• Ensure 99.9% label accuracy
• Conduct advance checks like keyword verifications, organic claim validation,and more.

Know exactly how GoVisually’s AI automates label and packaging review process to generate recall-proof labels for industry giants here.

GoVisually’s AI ROI calculator: Calculate your label compliance savings

Want to see exactly how much time and money GoVisually’s AI can save your business? Our ROI calculator helps you quantify the benefits of implementing AI-powered label compliance:

• Time saved per label review: Reduce review cycles from weeks to minutes
• Reduced compliance risks: Minimize costly recalls and regulatory penalties
• Faster time to market: Launch products 50% faster with automated compliance checks

On average, our clients experience a 75% reduction in review time and can bring products to market twice as fast. Depending on your label volume, this translates to potential monthly savings of $3,750 to $15,000.

Simply input your current label review volume to calculate your potential ROI with GoVisually’s AI compliance solution. Also, this use case is specifically helpful for brand managers, product managers, compliance officers, basically anyone overseeing packaging and labeling operations.

Note: While our AI compliance checker is highly accurate, it should be used as a tool to aid human expertise, not replace it. Always consult with qualified professionals for final compliance verification.

2. Generating AI-powered creative idea, concepts and design prototype

AI is completely changing designers’ approach towards ideation and prototyping, enabling them to explore creative possibilities faster than ever before. Major industry giants like Airbnb and Netflix have already integrated AI in their design workflows. 

According to industry reports, Airbnb generates production-ready code from hand-drawn wireframes, while Netflix personalizes artwork and localizes banners using AI tools.

So here are some popular ways designers are primarily using AI in their current design workflow:

• Text-to-image generation: Designers input descriptive prompts and adjust settings to produce visual concepts, helping overcome the dreaded “blank canvas” syndrome. According to research, U.S. creative professionals who use technology for image creation rely on AI for 78% of their visual output.
• Image-to-image transformation: Starting with existing designs, AI can generate variations and iterations, enabling rapid exploration of different visual directions without starting from scratch.
• Text-to-text creation: AI assists in crafting content variations, translations, and generating text elements that complement visual designs.
• Text-to-video conversion: Emerging AI tools can transform text descriptions into video content, expanding the designer’s toolkit beyond static imagery.

The real value of AI in the ideation phase lies in its ability to function as a creative partner.

This rapid prototyping use case enables designers to:

• Generate diverse concepts in minutes instead of hours
• Explore unconventional directions they might not have considered
• Create multiple variations to present to clients
• Overcome creative blocks by having an AI co-creator

Also note that while AI excels at generating options, human designers remain essential for applying critical thinking, selecting viable concepts, and refining them into polished designs that truly connect with users.

As Ioana from Interaction Design Foundation also quotes

There’s always a person orchestrating this. The future in which AI could generate a cohesive, coherent, reliable and relevant design process end to end is a very distant future for now, and there’s an impetuous need for someone governing over this process, applying critical thinking and showing intentionality at every stage of the design process.

The future of design isn’t about AI replacing humans, but about the powerful collaboration between human creativity and AI’s generative capabilities. 

3. Automating designing grunt work

One of the most practical applications of AI in design is automating tedious, repetitive tasks that consume valuable creative time. Let’s understand how AI transforms these mundane design tasks:

• One-click image upscaling

Traditional image upscaling often resulted in pixelation and quality loss. Designers would spend hours manually retouching enlarged images. Now, AI tools like Recraft offer “creative upscale” features that enhance image resolution and detail instantly without quality degradation, perfect for repurposing low-resolution assets.

• Intelligent background removal

The process of removing backgrounds earlier required designers to create masks and selections meticulously, taking up to an hour for complex images. With AI now understanding contextual information, background removal has become quicker and more precise, even with intricate subjects like hair.

• Smart resizing and reformatting

Adapting designs to different formats and platforms traditionally required rebuilding layouts for each size. AI can now intelligently resize images while preserving the focal elements, allowing designers to create variations for social media, web, and print without starting from scratch.

• Vector conversion automation

Converting raster images to vector format used to involve painstaking manual tracing. AI-powered vectorization tools can now transform complex raster images into clean, scalable SVG formats within seconds, making them ideal for print and digital projects.

For busy design teams, these AI automation tools can save an around 15-20 hours weekly per designer. That translates to roughly 60-80 hours monthly that can be redirected toward client strategy, brand development, and the creative problem-solving that actually builds business value and drives results.

4. Improving UX through behavioral insights

Analyzing, understanding and improving user experience through AI is one of the most prominent and helpful use cases of AI in the design workflow.

Designers use AI to understand user behavior at unprecedented depths and scale. By analyzing how users interact with digital products, AI can reveal patterns, preferences, and pain points that might otherwise remain hidden.

Let’s look at a few examples of how industry giant Netflix integrates AI in its current workflow to improve user experience:

• Smart viewing pattern analysis

Netflix employs advanced behavioral analytics to enhance user experience. Their recommendation system doesn’t just track what you watch, but examines subtle interaction patterns like when you pause, what you rewatch, how quickly you finish a series, and even when you abandon content. This behavioral data helps Netflix create a more intuitive browsing experience personalized to individual viewing habits.

• Automatic thumbnail creation

Netflix uses AI to determine which thumbnails generate the most clicks for specific user segments. Rather than using a one-size-fits-all approach, they create thumbnails that appeal to your unique preferences. If you frequently watch content featuring certain actors, those actors might appear more prominently in thumbnails shown to you.

• Adaptive streaming quality

Beyond content recommendations, Netflix’s AI also examines viewing patterns to optimize streaming quality in real-time. By monitoring network conditions and device capabilities, their system can adjust video quality to ensure smooth playback even with fluctuating internet speeds.

Designers across industries can apply similar approaches by:

• Using AI tools to analyze user session recordings and identify interaction patterns
• Creating heat maps of user engagement to spot design elements that attract or repel attention
• Implementing A/B testing with AI analysis to understand which design variations perform better
• Using natural language processing to analyze user feedback at scale

The key insight for designers is that AI doesn’t replace human creativity but enhances it with deeper user understanding. By revealing behavioral patterns that traditional research might miss, AI helps create more intuitive, responsive, and personalized user experiences that truly connect with users.

5. Spotting usability issues: Identifying blind spots and improving designs

AI tools are changing how designers test and improve usability by finding problems that might otherwise go unnoticed. Using smart algorithms and machine learning, AI analyzes user behavior data and offers practical suggestions to enhance design effectiveness.

• Automated Heuristic Analysis

AI checks designs against proven usability principles, quickly finding navigation problems, inconsistent layouts, and accessibility barriers without needing hours of manual testing. Designers can simply upload interface screenshots and receive an analysis based on established design standards.

• Predictive attention mapping

Tools like Attention Insight create heat maps showing where users will likely focus their attention. This helps designers place important elements where they’ll get noticed and create a clear visual hierarchy before investing in costly user testing sessions.

• Accessibility compliance scanning

AI tools search for accessibility issues such as poor color contrast, missing image descriptions, or incorrect heading structure. Applications like Axe DevTools and WAVE automatically check if designs meet WCAG standards, making it easier to create products everyone can use.

• Pattern recognition in user behavior

Artificial intelligence provides an excellent insight into how people use products. By examining data from analytics, recordings, and heat maps, AI can pinpoint where users get stuck or abandon processes, highlighting potential usability problems that need fixing.

When it comes to utilizing AI for usability enhancements, it’s important to remember that AI tools are augmentative, not definitive. So you need to balance AI-driven analytics with qualitative data, such as user interviews or contextual inquiries, to enhance UX. Using this blended approach, designers can deliver more intuitive, accessible, and user-centric products.

Limitations and issues of AI in design

While AI offers exciting possibilities for designers, it comes with significant limitations that need to be acknowledged. Understanding these constraints helps designers use AI tools more effectively and maintain realistic expectations.

1. Limited visual understanding

Most AI tools struggle with visual context and nuance. They can analyze text transcripts but often miss critical visual information in usability tests. For example, when reviewing interfaces, AI might miss how users interact with elements they don’t verbalize or misinterpret user hesitations that would be obvious to human observers.

2. Oversimplified and vague outputs

AI tends to generate generic recommendations like “improve navigation” without specific, actionable insights. This vagueness can create more work for designers who must interpret and refine these broad suggestions into practical design changes.

3. Contextual blindness

Current AI tools have limited ability to incorporate background information, research history, or project goals. Without this context, AI may rank minor issues as critical or miss the unique considerations of specific user groups or industries.

4. Citation and verification challenges

AI sometimes fails to reference where it found information, making it difficult to verify claims or trace insights back to user data. This can cause problems for design decisions that need to be evidence-based and accountable. One of the recent Harvard Business Review reports also explains how AI data cannot be fully trusted to make unsupervised decisions. 

5. Inherent biases

Despite marketing claims, AI tools are not bias-free. They reflect biases in their training data and can perpetuate design patterns that exclude certain user groups. As the Nielsen Norman Group notes, AI systems can contain systematic, statistical, and computational biases that influence their outputs.

6. Technical limitations

Current AI design tools often face performance issues, including crashes when processing complex design files, inability to handle certain file formats, and difficulty with responsive designs that adapt to different screen sizes.

These limitations are significant, but they don’t diminish the value of AI as a design assistant. So it is crucial to adopt a balanced approach in which AI is used to handle data-intensive tasks while humans provide context and empathy, as well as creative judgment. While these technologies will likely improve, humans will remain essential for creating effective and inclusive designs as they evolve.