AI & Design: What we learned (& unlearned) in 2024?

In 2024, the art world buzzed with excitement when an AI-generated painting was auctioned at Sotheby’s for an astonishing $432,500. The artwork, a hauntingly abstract piece titled Portrait of Edmond de Belamy, sparked debates about the very nature of creativity. Was this art? Could a machine truly create, or was it merely mimicking human expression?

For us as designers, this moment symbolized the broader shifts happening in creative industries. AI was no longer confined to algorithms crunching data behind the scenes—it was stepping into domains traditionally reserved for human ingenuity.

As a design studio with over 16 years of experience, we saw 2024 as a pivotal year for redefining how we engage with AI. This wasn’t just about tools or efficiency; it was about recalibrating the relationship between human creativity and machine intelligence.

In this narrative, we unpack the evolving role of AI in design, the responsibilities of design in shaping AI, and the lessons learned as we prepare for the future.

AI for Design

Artificial Intelligence has grown from being a functional tool into a collaborative partner in the design process. Its applications span multiple dimensions, fundamentally transforming how we approach creative challenges:

AI now drives automation with precision, as tools like Figma’s AI plugins and MidJourney redefine efficiency. Designers can quickly iterate through ideation, wireframing, and prototyping, saving valuable time without sacrificing quality. Beyond efficiency, AI unlocks new realms of creativity by generating variations and solutions that might not emerge through traditional brainstorming. This empowers designers to explore a broader spectrum of ideas, enriching the creative process.

Moreover, AI excels in harnessing data-driven insights to uncover patterns in user behavior. These insights guide user-centric designs that respond directly to real-world needs.

Perhaps most revolutionary is AI’s role in personalization: it enables dynamic content creation at scale, tailoring user experiences to individual preferences in ways that were once unimaginable.

This shift has made hyper-personalized interactions—once the domain of luxury experiences—a universal expectation.

Design for AI 

While AI transforms the "how," design shapes the "why" and "what," giving direction and meaning to technological advancements. The relationship between design and AI is not merely complementary but profoundly symbiotic. Designers stand as interpreters, translating the silent logic of algorithms into human experiences that are intuitive, emotionally resonant, and contextually relevant. This goes beyond usability—it’s about creating connections that feel personal and purposeful, even when powered by machine intelligence.

Designers also serve as custodians of ethics in the AI age. With AI’s growing influence, there’s an urgent need to embed principles of transparency, inclusivity, and fairness at every touchpoint. For instance, ensuring that AI recommendations in hiring platforms don’t reinforce biases or that healthcare AI systems prioritize equitable access is not just a technical challenge but a design mandate.

Designers simplify intricate AI processes into actionable, trust-building interfaces while simultaneously creating systems that explain the "why" behind decisions—essential in maintaining user confidence in AI.

Furthermore, feedback loops designed by human hands enable AI systems to evolve in ways that prioritize user needs over abstract optimization. An AI that learns to refine its predictions based on nuanced user input—rather than just aggregated data—demonstrates how design creates pathways for empathetic machine behavior. This interplay of human insight and machine adaptability is where the real potential of AI lies, pushing the boundaries of innovation while safeguarding humanity's role in defining its trajectory.

Design Principles for Designing For & With AI

Designing AI-powered experiences necessitates a thoughtful approach to ensure that the technology is both effective and user-friendly. By aligning AI interfaces with human behavior and expectations, we can create more intuitive, transparent, and adaptable systems. Here are the key principles to follow when designing for and with AI, supported by real-life examples:

1. Transparency

Principle: Clearly communicate AI’s role in decision-making processes to ensure users understand when they’re interacting with AI and how it influences outcomes.

Why it matters: Transparency helps build trust. When users are aware of how AI contributes to a process, they can make informed decisions and avoid feeling manipulated or confused.

Example:

• Google Maps: When you use Google Maps for navigation, it often indicates whether directions are AI-generated or influenced by real-time data. For instance, if the app redirects you due to traffic, it might show a message saying, "Based on traffic patterns, we suggest an alternate route." This transparency reassures users that AI is guiding them to make better decisions, not just random suggestions.

2. Trust & Reliability

Principle: Ensure that AI systems deliver consistent and unbiased outcomes to foster user trust and engagement.

Why it matters: Trust is a critical factor for user adoption. If AI systems deliver inconsistent or biased results, users are less likely to rely on them. Trustworthy systems promote long-term engagement.

Example:

• Amazon’s Alexa: Voice assistants like Alexa must reliably respond to user commands. If Alexa occasionally misinterprets commands or provides inaccurate information, users may lose confidence in its ability to meet their needs. Amazon has implemented continuous improvements and transparency about limitations to ensure users trust the device.
• AI in Hiring (e.g., HireVue): AI-based hiring systems must be designed to mitigate bias. Tools like HireVue have incorporated AI to assess candidates based on structured interviews, but the company makes it clear that algorithms are continuously monitored for bias, ensuring equal treatment for all candidates regardless of gender, ethnicity, or background.

3. Collaboration

Principle: Strive for a balanced approach where control is shared between AI systems and human users, empowering users without overwhelming them.

Why it matters: AI should enhance human decision-making rather than replace it. Systems designed to collaborate with users rather than dictate outcomes result in a more positive experience, where AI complements human intuition and expertise.

Example:

• Autonomous Vehicles (e.g., Tesla): In autonomous cars, AI aids in navigation, obstacle detection, and speed control. However, human drivers are still expected to monitor the system and intervene when necessary. Tesla's Autopilot feature allows drivers to maintain control while benefiting from AI-powered assistance, such as automatic lane-keeping and collision detection.
• AI-Driven Design Tools (e.g., Figma's AI Features): Figma has introduced AI tools that help designers generate layouts, suggest color palettes, or optimize design elements. But the tool allows designers to tweak these suggestions, keeping humans in the loop to ensure creativity and judgment shape the final product.

4. Adaptability

Principle: Design AI-powered systems that can evolve as the AI's capabilities improve, ensuring they remain relevant and effective as technology advances.

Why it matters: AI technology is continuously improving, and designs that are rigid or static will become outdated. Interfaces should be flexible enough to accommodate new features, enhancements, and learnings over time.

Example:

• Spotify: The platform uses AI to recommend songs and create personalized playlists. As users interact with the app, the recommendations evolve, refining the algorithm’s understanding of a user's music taste. This adaptability ensures that users consistently get relevant suggestions, even as their preferences change.
• Personalized Learning Systems (e.g., Duolingo): Duolingo uses AI to adapt the difficulty of lessons based on a learner's progress. It constantly updates content, adjusts pacing, and tailors learning paths as the user’s proficiency evolves, making the experience increasingly personalized over time.

5. Simplicity

Principle: Avoid overwhelming users with the complexity of AI systems. AI-powered interactions should be designed to appear seamless, even if the underlying technology is complex.

Why it matters: Users should focus on achieving their goals without needing to understand or manage the underlying AI. Simplicity ensures that AI’s advanced capabilities don’t create cognitive overload.

Example:

• Apple's Siri: Siri has been designed to simplify interaction with technology through voice commands. Users don’t need to understand natural language processing or machine learning models behind Siri. They can simply ask a question or give a command, and Siri handles the complexity of interpretation and execution, making the interaction feel effortless.
• AI in Email Management (e.g., Gmail’s Smart Compose): Gmail’s Smart Compose feature uses AI to predict and complete sentences as users type their emails. The feature works seamlessly without requiring users to understand how machine learning models are making predictions. Users enjoy faster email composition, focusing on content rather than worrying about the AI’s inner workings.

Common Fears, Anxieties & Roadblocks for Designers

AI's rapid development and widespread adoption have sparked a range of concerns among both designers and users. These fears, though often exaggerated, are real challenges that need to be addressed through education, empathy, and thoughtful design.

1. Job Security: Concerns About AI Replacing Creative Roles

Fear: There’s a prevalent concern that AI will replace jobs, particularly in creative fields like design, writing, and art. As AI tools become increasingly sophisticated, many worry that their roles might become redundant.

Response: While AI can automate certain repetitive tasks or suggest creative options, it cannot fully replicate human creativity, intuition, or emotional intelligence. Designers and creatives can use AI as a tool to enhance their work, not replace it. By automating mundane tasks, AI allows designers to focus on higher-level creative challenges, improving efficiency and output quality.

2. Ethical Dilemmas: Fear of AI Misuse, Biases, and Lack of Accountability

Fear: With AI making decisions that impact lives, from hiring to healthcare, the fear of biases creeping into AI algorithms and the lack of accountability is a major concern. How can we trust systems that are built by humans with potentially harmful biases or ethical blind spots?

Response: Designers and developers must prioritize fairness, transparency, and accountability when building AI systems. AI should be trained on diverse datasets to minimize bias, and clear guidelines should be set for when and how AI is used in decision-making. Ethical AI frameworks and constant auditing can mitigate risks and ensure systems remain accountable.

3. Loss of Human Touch: Anxiety Over AI-Generated Experiences Lacking Empathy

Fear: Many people fear that AI-generated interactions lack the empathy, warmth, and emotional intelligence that human beings offer. Whether it’s a chatbot or a personalized recommendation, AI may feel cold and mechanical, missing the human connection that users value.

Response: AI can be designed to recognize and respond to human emotions in a way that feels more personal. By incorporating empathetic responses and human-like interactions, AI can maintain a balance between efficiency and emotional connection. The goal is to combine the best of both worlds—AI’s efficiency and humans’ emotional intelligence.

4. Learning Curve: Designers Face Steep Learning Curves to Leverage AI Effectively

Fear: Many designers feel overwhelmed by the complexity of AI tools and the steep learning curve associated with mastering them. They worry about the time and effort required to effectively integrate AI into their workflow.

Response: Providing accessible education, resources, and hands-on training can help designers overcome this challenge. AI tools should also be user-friendly, with intuitive interfaces that make it easier for designers to integrate them into their workflows. Emphasizing AI as an enabler rather than a barrier can empower teams to adopt and make the most of these technologies.

How AI is changed user preferences in 2024?

AI has reshaped user expectations across all industries, pushing for more speed, personalization, interactivity, and proactive solutions. As technology continues to evolve, users increasingly demand experiences that feel more tailored to their needs, are seamless, and anticipate their preferences.

1. Speed & Convenience: AI-Driven Predictive Text and Autofill

Shift: Users now expect faster, more efficient interactions. AI-powered tools that predict text, fill out forms automatically, or streamline tasks are becoming the standard for delivering convenience.

2. Hyper-Personalization: Apps That Curate Unique Experiences

Shift: Users now expect experiences that are specifically tailored to them, driven by AI’s ability to analyze and understand their behavior. Hyper-personalized content recommendations, tailored offers, and customized user journeys are pushing brands to rethink how they engage with customers.

3. Interactive AI: Chatbots That Are Engaging, Not Just Functional

Shift: The rise of conversational AI means users are no longer satisfied with basic, functional interactions. They want AI that feels engaging, intelligent, and able to carry on fluid conversations.

4. Proactive Solutions: Users Expect Systems to Anticipate Their Needs

Shift: Users are becoming accustomed to AI systems that not only respond to queries but also anticipate their needs before they arise. Proactive AI delivers tailored solutions or insights without users needing to ask for them.

AI for Design. Design for AI.

As we reflect on our journey, the integration of AI in design has taught us that while AI can be a powerful tool, it is ultimately only as effective as the human vision guiding it. Collaboration between humans and AI is key to unlocking its full potential. One of the most profound lessons we've learned is that perfectionism, a natural instinct in design, can often slow down progress. 

With AI, the focus should be on iterative improvement, where small, continuous refinements lead to more meaningful outcomes.

This shift in mindset challenges traditional notions of flawlessness, encouraging designers to embrace evolution over static perfection. 

Moreover, ethical design has proven to be non-negotiable. As gatekeepers of trust, our responsibility to ensure that AI is used thoughtfully and transparently has never been more crucial. Equally important is unlearning the need for total control; allowing AI’s creative capabilities to flourish often means stepping back and enabling its generative potential to surprise us with novel solutions.

What lies ahead?

Looking ahead to 2025, the mindset required for designers and studios to thrive in an AI-driven world is centered on adaptability and continuous learning. Designers must approach AI not as a rival, but as a co-creator that can augment human creativity. The key is to stay curious, embracing new tools and technologies that push the boundaries of design. 

At the same time, a strong commitment to ethics must underpin every decision, ensuring that AI is used responsibly and for the benefit of all.

The future of design lies in fostering multidisciplinary collaboration, where human intuition and AI intelligence work in tandem to create solutions that are both innovative and empathetic. 

Above all, designers must celebrate human creativity, recognizing that while AI can assist in the creative process, it is our uniquely human perspectives and emotional intelligence that will continue to shape the future of design.