AI-Powered “4-Day Design Sprint”: Driving Result-Driven Innovation through Iterative Double Diamond (4D) Model

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Overview | Ideal Projects | Iterative 4D Model | 2 Uniqueness | 4-Day Agenda | Industry Impact

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Part 1: Driving Rapid Innovation

The Design Sprint method is widely regarded as one of the most effective innovation execution models within the Design Thinking method. It is particularly well-suited for innovation projects through user empathizing, rapid prototyping, and user testing. Esteemed global organizations such as 3M, Airbnb, HSBC, Prudential, H&M, and Samsung have leveraged this approach to achieve impactful and exceptionally efficient outcomes.

We, InnoEdge Consulting, have successfully applied the Design Sprint method in Hong Kong, delivering significant results in both the business and public service sectors. For example, our Design Sprint project helped generate over HK$3 billion in business for the banking industry (Details). In the public sector, we used an AI-driven Design Sprint to co-create innovative solutions with the different stakeholders of the community, reimagining the future of the Sham Shui Po Fabric Market (Details). These cases highlight the power of Design Sprints to drive transformative impact locally.

Typically, the methodology completes an entire innovation cycle in as short as four days. Despite its condensed timeline, the Design Sprint method excels at facilitating the testing and validation of new solutions, ensuring success across three critical dimensions: Desirability, Feasibility, and Viability. By integrating Artificial Intelligence (AI) into the Design Sprint process, the framework significantly enhances the efficiency of user needs identification, improving these processes by up to 94%.

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Part 2: Six Ideal Project Types for Design Sprint

The Design Sprint projects deliver actionable results within 4 days to 4 weeks (one day per week), minimizing wasted effort and resources. Perfect for tackling user pain points, enhancing products, or optimizing processes, it enables rapid validation and confident decision-making. Below are six project types where Design Sprints excel, with real-world examples.

1. Product Development of Light Industries: Designing a mobile app for a fitness tracking device for health-conscious youth seeking affordable solutions.
2. Digital Product and Services: Developing a chatbot to enhance customer support for e-commerce shoppers and tech-savvy users demanding quick, personalized responses.
3. Customer Experience Improvement: Redesigning the booking flow for a travel website to improve conversion rates for families and professionals seeking a seamless experience.
4. Market Expansion Initiatives: Localizing an e-learning platform for students and professionals in emerging markets seeking affordable and culturally relevant education.
5. Process Optimization: Streamlining the onboarding process with a digital workflow tool for HR teams and new hires in medium-to-large organizations.
6. Feature Enhancement of any product and service: Adding a voice-command feature to a smart home device for tech-savvy users seeking hands-free convenience and improved accessibility.

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Part 3: Iterative Double Diamond (4D) Model

There are two major types of design thinking projects, each with a distinct focus.

The first is the Design Discovery project, which prioritizes the Discover stage to uncover users’ unmet, hidden, and potential needs. This approach invests significant effort in deeply understanding and defining the problem space, leading to the creation of a single primary prototype for initial validation and refinement based on user insights. In such projects, over 75% of the time is concentrated on the first three stages—Discover, Define, and Develop—before progressing to the Deliver stage.

In contrast, the Iterative Double Diamond Model for Design Sprint emphasizes Deliver, targeting the development of workable prototypes and tangible outcomes. This iterative method dedicates around 75% of the time to the Deliver stage, where solutions are continuously refined and validated to ensure alignment with user needs, technical feasibility, and business objectives. This distinction illustrates the Double Diamond Model’s adaptability in effectively addressing various design challenges.

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Part 4: Two Unique Advantages of the Iterative Double Diamond Model

In addition to its iterative approach, the Double Diamond Model provides two transformative advantages for design sprint projects. First, it facilitates a profound, evidence-based understanding of users and their pain points, surpassing the superficial insights often generated by traditional methods. Second, it employs progressive convergence strategies that strategically refine ideas and prototypes, ensuring efficient resource allocation, a sharper focus on user needs, and accelerated progress through the innovation cycle, ultimately reducing risks and maximizing impact.

(1) Enabling a deeper and more accurate understanding of users

The Iterative Double Diamond Model addresses the limitations of traditional design sprints by prioritizing a deeper understanding of user pain points in the Discover and Define stages (the first diamond). Traditional design sprints often assume that the innovation team already has a comprehensive understanding of user needs and preferences. However, this assumption can lead to flawed problem definitions, which only become apparent during the first testing phase. This misalignment has the potential to waste time, effort, and resources on solutions that fail to address real user problems. The traditional approach lacks a systematic way to validate assumptions early, leaving teams vulnerable to costly mistakes and misdirected efforts.

By leveraging advanced AI tools like social listening and sentiment analysis, Design Research in the first diamond achieves over 90% accuracy in recognizing targeted user sentiment from social media data. These tools deliver actionable insights, validate assumptions, and refine problem definitions—all within one day. As a result, solutions developed in the subsequent stages are more aligned with real user needs, minimizing the risk of misalignment and accelerating the innovation process.

(2) Systematic Refinement Through Progressive Prototyping

In the Deliver stage (the second diamond), the Iterative Double Diamond Model uses progressive convergence strategies to structure the prototyping process into three sub-stages: Functional, Interactional, and Call-to-Commitment Prototyping—aligned with the dimensions of Desirability, Feasibility, and Viability. AI tools enhance each stage for speed and insight.

• Functional Prototyping (Desirability): Tests workflows and usability using low-fidelity prototypes (e.g., sketches, storyboards, wireframes). Validates core ideas and eliminates early flaws. AI tools like GPT analyze feedback, and DALL-E generates visuals. [Details]
• Interactional Prototyping (Feasibility): Refines usability via medium- to high-fidelity prototypes that simulate interactions and functionality. AI tools like Stormz aid ideation, and Sentiment Analysis detects user emotions. [Details]
• Call-to-Action Prototyping (Viability): Tests high-fidelity product and marketing prototypes for user commitment (e.g., purchases, referrals). AI tools like Social Listening track sentiment, and AICG generates marketing assets. [Details]

This structured progression ensures that prototypes evolve systematically, becoming increasingly focused and actionable as teams converge on the most viable and user-centered solutions. By addressing Desirability, Feasibility, and Viability step by step, this approach minimizes risks, enhances focus, and ensures that the final solution is not only user-friendly and technically sound but also viable in the market.

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Part 5: Four-Day AI-Powered Design Sprint Agenda

The 4-Day AI-Powered Design Sprint Agenda is a fast-paced, structured process that enables innovation teams to quickly move from understanding user needs to producing over 50 prototypes ready for testing with users and key stakeholders. Divided into four stages—Discover, Define, Develop, and Deliver—this 4D process ensures maximum innovation within a condensed timeframe. From refining challenge statements and uncovering actionable insights to conducting multiple rounds of user testing, the agenda focuses on creating solutions that are user-centered, practical, and market-ready.

In addition, we implement AI tools to empower innovation teams to innovate smarter and faster, from identifying user pain points to delivering actionable prototypes.

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Stage 1: Discover (Duration: 0.5 Day)

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Stage 2: Define (Duration: 0.25 Day)

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Stage 3: Develop (Duration: 0.25 Day)

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Stage 4: Deliver (Duration: 3 Day)

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Part 6: Recognition and Industry Impact

Our successful Design Sprint project (please click here for details) was featured in the Design Thinking Case Book 2020 (please click here for details), published by the VTC. This prestigious recognition highlights the project’s practicality, scalability, and impact, solidifying its position as a benchmark for innovation in the banking industry.

The success of this transformative project not only delivered measurable business results but also garnered significant recognition within the Hong Kong banking sector. The Business Discipline of the Vocational Training Council (VTC), under the Hong Kong SAR Government, identified the case as a pioneering example of innovation and design thinking in action.

The inclusion of this case in the Design Thinking Case Book underscores its importance as a learning tool for the next generation of business and innovation professionals. It serves as a valuable resource for illustrating how customer-centric approaches, cross-departmental collaboration, and continuous innovation can drive exceptional business outcomes, even in one of the world’s most competitive financial hubs.

This acknowledgment from the Hong Kong SAR Government reflects the project’s far-reaching influence and reinforces its role as a game-changer for the Hong Kong banking sector. It stands as a testament to how cutting-edge design sprints can redefine industry standards and create lasting business impact.

Beyond the success of this groundbreaking banking project, our Design Thinking Practices have been recognized for delivering impactful solutions across other major sectors in Hong Kong. We were invited to share four additional successful cases in industries such as aviation, insurance, public transport, and community development, showcasing how this methodology drives innovation, solves complex challenges, and creates measurable value across diverse fields. This recognition highlights the versatility and transformational power of Design Sprints in shaping the future of Hong Kong’s key industries.

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Reference

Winfield, K., Sizer, N., & Siena, F. (2022). Design sprint methodologies transformed in a digital environment.

Bordin, S. (2020). Design Sprint: fast problem-solving through collaboration. Proceedings http://ceur-ws. org ISSN, 1613, 0073.

Cheung, J. (2023). What Are Design Sprints? Retrieved 2023 from https://careerfoundry.com/en/blog/ux-design/what-are-design-sprints/

Gray, C. M., & Exter, M. E. (2023). A Design Sprint towards a Four-Year Curriculum in Transdisciplinary Studies. International Journal of Designs for Learning, 14(1), 70-87.

Hölttä-Otto, K., Niutanen, V., Eppinger, S., Browning, T. R., Stowe, H. M., Lampinen, R., & Rahardjo, A. (2018). Design sprint for complex system architecture analysis. International Design Engineering Technical Conferences and Computers and Information in Engineering Conference,

Huić, I., Horvat, N., & Škec, S. (2023). Design sprint: Use of design methods and technologies. Proceedings of the Design Society, 3, 1317-1326.

Jake-Schoffman, D. E., & McVay, M. A. (2021). Using the Design Sprint process to enhance and accelerate behavioral medicine progress: a case study and guidance. Translational Behavioral Medicine, 11(5), 1099-1106.

Magistretti, S., Allo, L., Verganti, R., Dell’Era, C., & Reutter, F. (2021). The microfoundations of design sprint: how Johnson & Johnson cultivates innovation in a highly regulated market. Journal of Knowledge Management, 25(11), 88-104.

Magistretti, S., Dell’Era, C., & Doppio, N. (2020). Design sprint for SMEs: an organizational taxonomy based on configuration theory. Management Decision, 58(9), 1803-1817.

Mendonça de Sá Araújo, C. M., Miranda Santos, I., Dias Canedo, E., & Favacho de Araújo, A. P. (2019). Design thinking versus design sprint: A comparative study. Design, User Experience, and Usability. Design Philosophy and Theory: 8th International Conference, DUXU 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Orlando, FL, USA, July 26–31, 2019, Proceedings, Part I 21,

Michalowski, A., Cavanaugh, K. L., Hamm, M., Wilkie, C., Olejniczak, D. M., Eneanya, N. D., Colditz, J., Jhamb, M., Bulls, H. W., & Liebschutz, J. M. (2023). Stakeholder-driven intervention development for dialysis trials using a design sprint methodology. Kidney Medicine, 5(12), 100729.

O’Donoghue, J. (2022). Design Thinking vs Design Sprints. Retrieved 2022 from https://makeiterate.com/design-thinking-vs-design-sprints/

Poulter, C., Wang, C., & DelRio Gayo, I. (2022). Applying the Design Sprint to Interactive Machine Learning Experience Design: A Case Study from Aveni. International Conference on Human-Computer Interaction,

Roto, V., Larusdottir, M., Lucero, A., Stage, J., & Šmorgun, I. (2021). Focus, structure, reflection! integrating user-centred design and design sprint. Human-Computer Interaction–INTERACT 2021: 18th IFIP TC 13 International Conference, Bari, Italy, August 30–September 3, 2021, Proceedings, Part II 18,

Sari, E., & Tedjasaputra, A. (2017). Designing valuable products with design sprint. Human-Computer Interaction–INTERACT 2017: 16th IFIP TC 13 International Conference, Mumbai, India, September 25-29, 2017, Proceedings, Part IV 16,

Wangsa, K., Chugh, R., Karim, S., & Sandu, R. (2022). A comparative study between design thinking, agile, and design sprint methodologies. International Journal of Agile Systems and Management, 15(2), 225-242.