AI can create more than just new pixels for designers
In the dynamic landscape of UX/UI design, the integration of AI tools has become ubiquitous, revolutionising the way designers create digital experiences.
While AI’s role in shaping graphical user interfaces is well-acknowledged, the scope of its impact extends far beyond mere layout designs.
With that said, a noticeable gap has emerged between the current AI offerings and the demands of deep design thinking.
Let’s take a deeper look at it.
Some primary gaps between existing tools and a designer’s needs
1. Lack of confidence in generative models’ results
- No rationale behind design decisions:
Designers often grapple with a notable absence of rationale accompanying the results generated by AI models. The lack of a transparent explanation for design decisions leaves designers in the dark, fostering a hesitancy to integrate these results into their creative process. - Little control by designers over the generative process:
Current AI tools usually only ask the user to give a single-line description of their product and describe the style, to generate designs. These fail to ask real questions about what problem is it trying to solve, who is it designing for, what are the constraints, and more.
2. Current generative models are restricted to graphic design elements
- Lack of assistance in design thinking activities
Designers actively participate in activities that require careful evaluation of user experiences, functionality, and overall product strategy. The focus solely on graphic design elements overlooks the comprehensive approach essential for design thinking. This results in a failure to address the diverse factors that contribute to the success of a product.
3. Existing generative models’ results are not context-specific
- Lack of nuanced and context-aware approach
This generic nature becomes a challenge for designers aiming to create customized solutions for different contexts and user scenarios. The one-size-fits-all approach can result in outcomes that may not align with the specific intricacies of each design challenge.
Designers often desire solutions that can offer the depth and sophistication required to address complex real-world problems through design.
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Let’s discuss some areas where AI-enabled design support tools can be beneficial for product designers:
Opportunities for AI-enabled design support tools
1. Check for violations of design guidelines
- Automated Design Rule Checking (DRC):
AI-enabled tools offer a revolutionary opportunity through Automated Design Rule Checking (DRC). By programming these tools with design guidelines, accessibility standards, and best practices, designers can benefit from automated scans of their design files. This functionality acts as a proactive guardian, identifying rule violations, saving time, and ensuring design standards are followed. - Accessibility compliance:
AI tools can be customized to focus on accessibility standards, like WCAG, ensuring that designs meet critical guidelines. Automating accessibility evaluation empowers designers to create inclusive digital experiences for users with diverse needs. - Consistency checks:
From colour schemes and typography to spacing, AI tools could conduct thorough consistency checks, verifying that the design aligns seamlessly with the defined style guide. This capability acts as a guardian of design coherence, aiding designers in maintaining a visually harmonious and well-integrated user interface. - Prioritisation of Issues:
AI tools can provide a strategic advantage by prioritizing identified violations based on severity. This helps designers focus on addressing the most critical issues first, streamlining the problem-solving process and ensuring effective allocation of efforts.
2. Design system customisation
- Usability testing and feedback integration:
AI tools to integrate usability testing results and user feedback into the design system customisation process, guiding designers to make data-driven customisation decisions for improved usability. - Dynamic style and size shifts:
AI can dynamically customize component styles based on contextual factors, such as screen size and device type. Essentially, the designer can focus on creating a single good design, and we allow the machines to handle the consistency across different devices.
3. Design inspiration search
- Contextually relevant design analysis and feedback:
AI tools can analyse the specific design problems that designers are currently facing within the ongoing project. By understanding the context of those challenges, AI can tailor inspiration suggestions to address those specific needs.
Designers will benefit more from a more targeted and relevant pool of inspirations. - Provide rationale behind references:
A crucial aspect of AI’s role in design inspiration lies in providing a rationale behind the references it suggests. This entails an insightful explanation of why each suggested inspiration is relevant to the identified design challenges. This emphasis on providing a rationale facilitates informed decision-making, empowering designers to not only draw inspiration but also to understand the practical applications of each reference within the context of their project.
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4. Design alternative exploration, along with the rationale behind it
- Product analytics tools that also tell the “Why” behind the insights:
Existing product analytics tools usually only give you insights into users’ actions, and do not capture the “why” behind their actions. Analytics tools can move beyond providing mere observations (“What”) and delve into the underlying reasons (“Why”) behind design data.
Moreover, they can go a step further by offering actionable suggestions on how these insights can be translated into effective design changes. - Integration with user journey mapping:
Through the analysis of user journey data, AI can not only identify touchpoints and pain points in the user experience but also suggest design alternatives that specifically address these aspects.
This integration ensures that design alternatives are not arbitrary but strategically aligned with the actual user interactions.
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AI tools can consider the project’s specific context, such as industry, target audience, and cultural considerations. The emphasis shifts towards suggestions that are not only visually pleasing but also deeply contextually relevant. This will help designers craft alternatives that resonate with the unique characteristics of the project.
Think of AI as augmented intelligence
Designers are increasingly relying on AI tools to enhance their creative process beyond just creating new pixels. These tools offer assistance in understanding why certain design choices should be made and help bridge the gap between existing AI offerings and deep design thinking.
One primary gap that AI tools can address is the lack of confidence in generative models’ results. By providing transparent explanations for design decisions and involving designers in the generative process, these tools enable designers to integrate AI-generated results with confidence.
AI tools have the potential to become indispensable collaborators in the design process by augmenting designers’ intelligence and assisting in critical thinking, ethical thinking, and more. They not only assist in designing new pixels but also help designers understand why those pixels should be there in the first place — An alternative way to think about AI is “augmented intelligence”, and we need AI to assist us in design thinking. Critical thinking. Ethical thinking. And much more.
Canvs Editorial regularly brings you insightful reads on design and anything related. Check out the work we do at Canvs Club.
The Canvs Editorial team comprises of Editorial Writer and Researcher — Paridhi Agrawal, the Editor’s Desk- Aalhad Joshi and Debprotim Roy, and Content Operations- Abin Rajan. Follow Canvs on Instagram for more design-related content.
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