In today’s rapidly evolving business landscape, the ability to adapt and innovate is paramount to success. Innovative enterprises such as Apple, Airbnb, HSBC, Google, Amazon and Tesla are increasingly using Design Thinking as a strategic tool to navigate complexity and drive business transformation.
Design thinking has evolved significantly since its inception, moving from a primarily qualitative, human-centered approach to a sophisticated methodology that leverages data, technology, and artificial intelligence. This evolution has profound implications for business decision-making. It empowers leaders to make faster decisions, reduce risks, and foster a culture of change.
The five videos (From part 1 to part 5) below will explore the evolution of Design Thinking from its early stages, DT 1.0, to its current iteration, DT 3.0. They will also highlight the integration of AI and its impact on management decisions.
Part 1: The Rise of Design Thinking in Business
Part 2: A Human-Centered Approach to Problem Solving
Part 3: Integrating Data into the Design Process
Part 4: The Transformative Power of AI and Automation
Part 5: Implications for Business Leaders
Part 1
Introduction: The Rise of Design Thinking in Business
Duration: 1 mins
Overview:
Design Thinking (DT) has evolved from a human-centric approach to a sophisticated method integrating AI, transforming decision-making in businesses like Apple and Google. Originally qualitative, DT now leverages technology to enhance speed, reduce risks, and nurture innovation. This video outlines DT’s evolution from DT 1.0 to DT 3.0, highlighting AI’s role and its benefits at each stage and demonstrating its strategic impact on fostering competitive advantages in a dynamic marketplace……
Part 2
DT 1.0: A Human-Centered Approach to Problem-Solving
Duration: 3 mins
Overview:
DT 1.0 introduced a human-centered approach to problem-solving in business, emphasizing empathy, co-creation, and iteration. This phase focused on understanding user needs through design research, persona creation, and customer journey mapping. By engaging deeply with user experiences and prioritizing qualitative data, teams could develop meaningful, resonant solutions. The iterative nature of DT 1.0 encouraged rapid prototyping and user testing, fostering a culture of empathy, collaboration, and continuous improvement, ultimately leading to more innovative, user-centered products……
Part 3
DT 2.0: Integrating Data into the Design Process
Duration: 2 mins
Overview:
DT 2.0 represents an evolution in Design Thinking, integrating data analytics with the human-centric principles of DT 1.0 to enhance decision-making and innovation. This approach leverages digital prototyping, data analytics, and user feedback platforms to validate assumptions and refine solutions. By segmenting customers and analyzing their needs, businesses develop targeted, personalized solutions. DT 2.0 enables rapid testing and adaptation to market demands, increasing the effectiveness and relevance of design solutions through informed, data-driven strategies……
Part 4
DT 3.0: The Transformative Power of AI and Automation
Duration: 3 mins
Overview:
DT 3.0 enhances Design Thinking by integrating AI, machine learning, and automation, optimizing innovation processes across all phases. This iteration leverages advanced technologies for in-depth data analysis and hyper-personalization, allowing businesses to develop highly tailored, effective products. AI automates routine tasks, freeing teams to focus on strategic and creative endeavors. With tools like social listening and real-time feedback analysis, DT 3.0 significantly reduces time to market and adapts swiftly to user needs, driving sustained business growth.
Part 5
Conclusion: Implications for Business Leaders
Duration: 2 min
Overview:
The evolution of Design Thinking from DT 1.0 through DT 3.0 has significantly enhanced business decision-making. This progression, from empathizing with user needs to integrate data analytics and AI equips leaders to make agile, informed decisions that enhance customer engagement and organizational resilience. AI-driven Design Thinking empowers all levels of management to adapt strategies to real-time market and customer dynamics, fostering a culture of innovation and ensuring sustained success in a volatile business environment.
Reference:
Böckle, M., & Kouris, I. (2023). Design Thinking and AI : A New Frontier for Designing Human‐Centered AI Solutions. Design Management Journal, 18(1), 20-31. https://doi.org/10.1111/dmj.12085
Chung, D. (2020). Creating a memorable experience to retain valued banking customers. In Design Thinking Business CaseBook 2020 (pp. 21 to 24). Vocational Training Council (Business Discipline), Hong Kong SAR Government.
Chung, D. (2022). Creating a caring experience for passengers with special needs in the public transportation. In Design Thinking Business CaseBook 2022 (pp. 21 to 24). Vocational Training Council (Business Discipline), Hong Kong SAR Government.
Chung, D., Choi, Y., Lee, P., Lee, S., & Liu, T. (2022). Creating a memorable experience for Kwun Tong Yue Man Hawker Bazaar. In Design Thinking Business CaseBook 2022 (pp. 17 to 20). Vocational Training Council (Business Discipline), Hong Kong SAR Government.
Chung, G., & Chung, D. (2018). WOW the Hospitality Customers: Transforming Innovation into Performance Through Design Thinking and Human Performance Technology. Performance improvement (International Society for Performance Improvement), 57(2), 14-25. https://doi.org/10.1002/pfi.21772
Dash, S. K. (2023). Artificial Intelligence (AI) Facilitated Data-Driven Design Thinking. In (pp. 17-24). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-49215-0_3
Kan, S., & Chung, D. (2019). Creating the Flying Beyond passenger experience. In Design Thinking Business CaseBook 2019 (pp. 17 to 20). Vocational Training Council (Business Discipline), Hong Kong SAR Government.
Kan, S., Chung, D., & Chung, G. (2019). Customer Experience Transformation in the Aviation Industry: Business Strategy Realization through Design Thinking, Innovation Management, and HPT. Performance improvement (International Society for Performance Improvement), 58(1), 13-30. https://doi.org/10.1002/pfi.21823
Krishnan, T., Singh, A., Kumar, K., & Ganesh, J. (2022). Leverage Design Thinking to Build Enterprise AI. California Management Review Insights.
Leão, C. P., Silva, V., & Costa, S. (2024). Exploring the Intersection of Ergonomics, Design Thinking, and AI/ML in Design Innovation. Applied system innovation, 7(4), 65. https://doi.org/10.3390/asi7040065
Magistretti, S., Legnani, M., Pham, C. T. A., & Dell’Era, C. (2024). The 4S Model for AI Adoption. Research-Technology Management, 67(3), 54-63. https://doi.org/10.1080/08956308.2024.2325859
Man, I., & Chung, D. (2019). Creating Unlimited Business Opportunities for an Insurance Sales Force. In Design Thinking Business CaseBook 2019 (pp. 21 to 24). Vocational Training Council (Business Discipline), Hong Kong SAR Government.
Man, I., & Chung, D. (2019). Creating Unlimited Business Opportunities for an Insurance Sales Force Through Design Thinking. In Cases on Learning Design and Human Performance Technology (pp. 287-304). IGI Global. https://doi.org/10.4018/978-1-7998-0054-5.ch015
Niehaus, M., & Mocan, M. (2024). Cultivating Design Thinking for Sustainable Business Transformation in a VUCA World: Insights from a German Case Study. Sustainability, 16(6), 2447.
Poleac, D. (2024). Design Thinking with AI. Proceedings of the International Conference on Business Excellence, 18(1), 2891-2900. https://doi.org/doi:10.2478/picbe-2024-0240
Skywark, E. R., Chen, E., & Jagannathan, V. (2022). Using the Design Thinking Process to Co-create a New, Interdisciplinary Design Thinking Course to Train 21st Century Graduate Students. Frontiers in public health, 9, 777869-777869. https://doi.org/10.3389/fpubh.2021.777869
Stackowiak, R., & Kelly, T. (2020). Design thinking in software and AI projects: proving ideas through rapid prototyping. Springer.
Staub, L., van Giffen, B., Hehn, J., & Sturm, S. (2023). Design Thinking for Artificial Intelligence: How Design Thinking Can Help Organizations to Address Common AI Project Challenges. In (pp. 251-267). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-48057-7_16
