Design Thinking and AI-Powered Processes

This article is part of a series that collects the studio's reflections on AI topics and its applications. If you don't want to miss any stories from Sketchin, subscribe to our newsletter.

By reading “Design Thinking and AI-Powered Processes,” don't worry, this isn't the usual article explaining how artificial intelligence can streamline the entire process and deliver immediate results. On the contrary, we want to focus on a crucial aspect that we cannot overlook: the "Empathize" phase. That's right, you heard correctly: we cannot ignore the growing importance of research, and if you're curious about what will be the key skill of future designers, it's the ability to put themselves in others' shoes, like methodical actors before playing a role.

We are not discussing research or the “Empathize” phase as a simple chat over a cup of tea, desk research, or some data picked here and there. We are referring to real user research, of an anthropological nature – yes, the serious kind with field research, in-depth interviews, and all the necessary equipment. While some might think these practices seem a bit "dated in an AI-dominated era," we want to clarify that nothing could be further from the truth.

AI, with all its "magic," helps us a lot, especially when it comes to simplifying the more operational stages like prototyping, testing, and implementation. However, when it comes to truly understanding users, capturing those subtle and detailed nuances that make the difference, those unspoken needs that even users themselves cannot identify, AI must give way to humanity. In design thinking, empathizing is not just a phase; it's the art of listening and interpreting the human essence, and AI still has much to learn in this area.

Qualitative research, as opposed to quantitative data, explores the 'how' and 'why' behind users' actions and thoughts, providing a rich understanding of their motivations, feelings, and behaviours. While AI can identify patterns and trends, designers can access users' nuances, peculiarities, and latent needs through interviews, field observations, and ethnographic studies. Methods that reveal hidden necessities and not yet fully articulated desires, offering essential insights to define real problems and opportunities for innovation. Qualitative research allows designers to identify the most relevant and crucial design issues, which are the foundation for truly innovative solutions.

While AI can accelerate and optimize the more mechanical phases of prototyping and testing by automating the collection and analysis of large volumes of data, it cannot replace the human understanding that emerges from qualitative research. Machines can recognize patterns and categorize information but lack the ability to perceive the emotional and cultural subtleties that define the human experience. In this context, AI serves as a tool that complements the designer, freeing up resources that can be better invested in deeply immersing in the user's context and needs.

The new role of the designer

The role of the designer, as everyone is telling us and as we see every day, is evolving. We don't want to talk about how and why; it has already been said and analyzed too many times, and it is, above all, a constant process (design itself evolves over time, and what it is today is not what it will be tomorrow).

What we can say is that the designer will increasingly be an interpreter of human reality. Not an artist, but an analyst of humanity. In a world increasingly mediated by technology, the ability to deeply understand others and interpret human needs becomes an invaluable asset. The future will see designers engage more in research activities, taking on a role similar to that of a researcher or psychologist, connecting the dots in new and creative ways to discover the most relevant and significant insights. This evolution will position them as critical mediators between technology and humanity, interpreters capable of transforming complexity and diversity into innovations that truly respond to human needs.

How AI and designers can collaborate

Invoking philosophy (which increasingly concerns us as designers) as a guide in the world of ethics and analysis, in the context of design thinking, the fusion of deductive and inductive methods, as proposed by Hempel and Oppenheim, demonstrates how the path of knowledge can be traced both from the top down and from the bottom up, integrating empiricism with increasingly higher abstractions. This dual movement of knowledge becomes a continuous cycle where deduction leads from general concepts to specific realities, while induction rises from particular observations to generalizations.

Kurt Gödel, with his incompleteness theorems, teaches us a valuable lesson: no logical system (like AI) can be considered complete and self-sufficient without some form of external input, in this case, human input. This is particularly relevant in design, where the axioms on which solutions are based can only be defined by human intelligence. According to Gödel, axioms cannot derive from the system that uses them; hence the need for an external perspective, which in the context of design is provided by human empathy and intuition, while the analysis system is that of AI.

Integrating AI and design is not limited to functional collaboration but evolves into a deeper symbiosis, where each element brings something unique to the table. Saying that AI offers speed, efficiency, and the ability to manage large datasets, while the designer offers understanding, creativity, and empathy is reductive: this collaboration leads to what we might call an "assisted innovation cycle," where the designer gathers insights and deductions from qualitative research, AI helps identify new opportunities, and the designer interprets and models these opportunities into solutions that genuinely respond to human needs. It is the humanity of the designer that can thus define the effectiveness and success of one solution over another. The uniqueness of understanding and identifying the path to take is the ingredient that makes the work of AI useful and not standardized.

In more advanced future scenarios, where AIs will collaborate directly with other AIs, there is indeed the risk of falling into a loop of standardized and similar information, reproducing and re-proposing always homogenized solutions, leading to a decrease in innovation in the design process and the evolution of product/service ideas and experiences. In the context of design thinking and AI-Powered processes, it is crucial not to underestimate the limitations of this technology. In particular, contextual and emotional understanding remains a significant challenge for AI, which often fails to grasp subtle feelings or the broader context influencing human behaviour. For example, in designing new healthcare products, AI can detect usage patterns but not users' anxiety or emotional discomfort (it cannot fully identify the reasons for a parent's reactions towards an ill child).

Moreover, let us not forget that bias in data is another critical limitation. AI reflects the prejudices present in the training data. This was clearly evident in the case of facial recognition algorithms that showed higher error rates with faces of ethnicities not adequately represented in the datasets. In design, such bias can result in products that are not suitable or fair for all users. Therefore, an impartial understanding of user needs is essential, especially one that is not schematic, which can be avoided with research conducted by well-trained designers and user researchers.

The future of design

Creativity represents another area where AI is limited. While it can generate solutions based on defined parameters, its ability to think beyond conventional frameworks does not match that of humans. Human creativity can discover unexpected connections and insights derived from personal experiences, aspects that AI cannot easily replicate.

Close collaboration between humans and AI is essential to overcome these limitations and can be particularly effective in complex projects. Another strategy to mitigate biases involves continuously training algorithms with diversified datasets and regularly evaluating AI performance. This constant effort could help identify and correct emerging biases, ensuring that AI systems remain fair and representative. This means continuing to set up qualitative research conducted by designers that continuously informs the AIs.

The future of design does not depend solely on adopting new technologies but crucially on the ability to maintain and enhance the human connection. The designer of the future is called to be a technological operator who can interpret human complexity, an indispensable bridge between technology and the varied facets of human life, transforming technical challenges into solutions that authentically respond to human needs.

AI is transforming design processes, making them more efficient, but the true essence of design thinking remains rooted in human understanding. In a world that increasingly emphasizes efficiency and speed, the future of design is a matter of being able to use new technologies but also, above all, of maintaining a deep connection with humanity.