How this integration can transform creative and analytical processes and raise questions that need to be addressed in innovation
*By Mirian Luzzi, Daniela Jacobina, Lucas Bastianello Scremin, Ricardo Pereira
The combination of Design Thinking and Generative Artificial Intelligence (GAI) is emerging as one of the most promising frontiers of contemporary innovation. This union not only enhances human creativity with the analytical power of machines, but also reconfigures the way complex problems are approached in various sectors. However, while the possibilities seem limitless, this merger also poses significant ethical challenges that must be carefully considered so that innovation does not become a dehumanized or irresponsible process.
Design Thinking, which dates back to the 1960s, represents one of the most central and influential approaches to creative problem-solving and challenge-solving. Initially outlined by Herbert Simon, this methodology stands out for its focus on creating new forms and solutions, differentiating itself from the natural sciences that traditionally deal with the discovery of universal truths. Over the years, this approach has been enriched by important theoretical contributions, such as those of Donald Schön, who in “The Reflective Practitioner” challenged the predominance of technical knowledge and exalted the reflective practice of designers. For Schön, Design Thinking is not just a technical discipline, but a continuous learning process, in which art and science meet in the search for solutions to problematic situations.
By expanding this perspective and treating Design Thinking as a response to “wicked” problems—those that do not have a single solution and that require a deep understanding of the social, cultural, and economic contexts in which they are embedded—Richard Buchanan has shown us that Design Thinking is a holistic approach that excels precisely in situations where traditional solutions fail. Buchanan argues that these “wicked” problems cannot be solved with linear or logical methods alone; they require an approach that combines creativity, intuition, and a systemic view of the problem. In this context, Design Thinking enables the construction of innovative solutions that take into account the complexities and interdependencies of the factors involved, promoting interdisciplinary collaboration and the involvement of multiple stakeholders. By directly involving users in the design process, Design Thinking also helps ensure that the solutions developed are more inclusive and equitable, responding to the real needs of people and the nuances of the environments in which they live. This approach is particularly valuable in an increasingly volatile, uncertain, complex and ambiguous (VUCA) world, in which organizations need to be agile and adaptable to meet multifaceted and dynamic challenges.
The Design Thinking process is generally described in five fundamental steps. The first phase, immersion, involves a deep understanding of the problem to be solved and the context in which it occurs. This phase is followed by definition, when the information gathered is synthesized to formulate a clear problem statement. The third phase, ideation, involves generating a wide range of creative ideas to address the identified problem. Prototyping then allows these ideas to be transformed into tangible models that can be tested and refined in the final phase, testing, when user feedback determines the direction of improvement of the solutions.
With the advent of Generative AI, Design Thinking has entered a new era. Tools such as large-scale language models (LLMs) offer capabilities that perfectly complement the ideation and prototyping stages. AI can process and analyze large volumes of data in record time, providing valuable insights that are often missed by traditional human analysis. In addition, it can generate creative suggestions and even early prototypes, increasing both the efficiency and quality of work performed on innovation projects.
However, this integration is not without its challenges. The introduction of AI into Design Thinking raises important ethical questions. One of the main risks is the possibility of plagiarism or the diminishment of human creative input, as AI may begin to play a leading role in idea generation. There is also concern that over-reliance on these technologies could lead to the creation of solutions that, while effective, lack the sensitivity and empathy that are hallmarks of human-centered Design Thinking.
It is therefore imperative that organizations adopting AI in their Design Thinking processes implement clear guidelines that help ensure the ethical integrity of their practices. This includes ensuring that AI is used as an auxiliary tool, not a replacement for human creativity. It is also necessary to foster a culture of transparency, where users and customers are aware of how AI is used in the development of products and services.
The application of AI in Design Thinking is vast and full of possibilities. Emerging areas of research include developing more transparent and explainable AI algorithms that can be better understood and managed by humans. Furthermore, customizing these tools for different industries and specific contexts promises to further expand the possibilities for innovation. Integrating Design Thinking with other innovation methodologies, such as Lean Startup and Agile, helps create an ecosystem that is robust and, at the same time, more flexible and quickly adaptable to market changes and new consumer demands.
The combination of Design Thinking and IAG has the potential to radically transform the way organizations innovate and solve problems. Academic institutions, companies, and even governments can benefit from this approach, fostering an iterative dialogue that combines the analytical power of technologies with human creativity and empathy. However, for this transformation to be successful, there needs to be clear and rigorous guidance on the ethical risks associated with the use of these tools. Only then will it be possible to fully leverage this synergy, without compromising innovation or the core values that guide Design Thinking.
In short, the integration of Design Thinking and IGA represents one of the most exciting and challenging opportunities in the current innovation landscape. As we move toward an increasingly digital and automated future, the ability to balance the use of technology with human creativity and ethics will be critical to the success of organizations. Those who can navigate this new territory responsibly will not only be at the forefront of innovation, but also at the forefront of a movement that promises to redefine what it means to innovate in the 21st century.
*Mirian Luzzi is a Planning Specialist at the Brazilian Association of Software Companies (ABES); Daniela Jacobina has a master's degree in Intellectual Property and Technology Transfer for Innovation from the Federal University of Santa Catarina (UFSC). She is currently a project coordinator at the Think Tank – Center for Innovation Intelligence, Public Policies and Innovation of the Brazilian Association of Software Companies (ABES); Lucas Bastianello Scremin is a Professor at the Federal Institute of Education, Science and Technology of Santa Catarina (IFSC) and a PhD student in the Postgraduate Program in Engineering, Management and Knowledge Media (PPEGC/UFSC); and Ricardo Pereira is a Professor and researcher, creator of @mentor.IA
Notice: The opinion expressed in this article is the responsibility of its authors and not of ABES – Brazilian Association of Software Companies
Article originally published on the IT Forum website https://itforum.com.br/colunas/unir-design-thinking-ia-generativa-inovacao/
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