Design Thinking: Bridging Engineering Precision, Innovation, and Business Acumen
Decisions in this world are seen as driven by
emotion more than logic; desire is seen as a more powerful
motivator than reason. In this world, there is only one individual
“truth”—and answers are either “better” or “worse.”
Mindset: Only through contact, observation, empathy with the end users, you can expect to design solutions that fit in their environment.
Opposition: Design thinking is in opposition to the traditional managerial approach of solving problems like- Brainstorming Sessions held in the Board Room.
emotion more than logic; desire is seen as a more powerful
motivator than reason. In this world, there is only one individual
“truth”—and answers are either “better” or “worse.”
Mindset: Only through contact, observation, empathy with the end users, you can expect to design solutions that fit in their environment.
Opposition: Design thinking is in opposition to the traditional managerial approach of solving problems like- Brainstorming Sessions held in the Board Room.
As a successful licensed structural engineer who has developed AI and machine learning products, I've found that design thinking is a powerful approach that bridges the gap between traditional engineering problem-solving and innovative, user-centric solutions. This methodology, which I've successfully applied in both structural engineering and machine learning projects, follows a cyclical process that can be summarized in six key steps:
The beauty of design thinking lies in its iterative nature. This cyclical process ensures continuous improvement and adaptation, much like how we continuously optimize algorithms in machine learning or refine structural designs based on new data and technologies.
Real-World Application
Let me share a concrete example of how I've applied design thinking in my work. When developing an AI-powered building inspection system, we started by understanding the current inspection process and observing inspectors in the field. We synthesized our findings into a key problem statement: "Building inspectors need a way to quickly identify potential structural issues while reducing the risk of oversight."
During ideation, we came up with various solutions, from drone-based imaging to sensor networks. We prototyped a mobile app that used computer vision to highlight potential issues in real-time as inspectors moved through a building. Through iterations and user testing, we refined the app to include features like voice notes and automated report generation.
This design thinking approach resulted in a solution that not only leveraged cutting-edge AI technology but also seamlessly integrated into inspectors' workflows, significantly improving efficiency and accuracy.
Conclusion
By applying design thinking principles, we can create solutions that are not only technically sound but also deeply resonant with user needs. Whether we're designing bridges, buildings, or AI systems, this human-centered approach helps ensure that our innovations truly serve their intended purpose and make a meaningful impact.
In my journey from structural engineering to AI development, I've found design thinking to be an invaluable tool for innovation. It allows us to combine the rigorous, analytical approach of engineering with the creative, user-focused mindset needed to solve complex, multifaceted problems in our rapidly evolving technological landscape.
- Understand: Just as we begin a structural analysis by understanding the project requirements, design thinking starts with a deep dive into the problem space. This involves researching the context, stakeholders, and underlying issues. For instance, when I worked on Florida's I-4 highway bridges, this phase involved understanding traffic patterns, environmental conditions, and local regulations.
- Observe: In engineering, we conduct site surveys and material tests. Similarly, in design thinking, we observe users in their natural environment. As Henry Ford reportedly said, "If I asked people what they wanted, they would have said faster horses." Our job is to uncover unspoken needs. In my AI projects, this often involves observing how engineers interact with existing software to identify pain points and inefficiencies.
- Synthesize: This stage is akin to creating a structural model. We synthesize our observations into a clear problem statement, focusing on one key issue. For example, in an educational technology project, our view statement might be: "Students in resource-constrained schools need collaborative computing solutions that allow multiple users to interact with a single device simultaneously." In bridge design, it might be: "Commuters need a bridge that can withstand Category 5 hurricane winds while minimizing traffic disruption during construction."
- Ideate: This is where engineering creativity meets brainstorming. We generate numerous ideas rapidly, often aiming for 60-100 ideas per minute. In my experience, this quantity-over-quality approach often leads to unexpected breakthroughs, whether designing bridge supports or AI algorithms. For instance, this approach led to innovative stress distribution techniques in bridge design and novel feature engineering methods in my machine learning projects.
- Prototype: Just as we create scaled models in structural engineering, in design thinking we build low-fidelity prototypes to test our ideas. These prototypes should answer specific questions about functionality and user experience. For a collaborative computing solution, we might prototype different input device configurations. In bridge design, we might use 3D printing to rapidly test different structural configurations.
- Iterate: Finally, we test our prototypes with real users, gathering feedback on what works and what doesn't. This iterative process is similar to how we refine structural designs based on simulation results and real-world performance data. In AI, this might involve A/B testing different algorithms or user interfaces with a subset of users.
The beauty of design thinking lies in its iterative nature. This cyclical process ensures continuous improvement and adaptation, much like how we continuously optimize algorithms in machine learning or refine structural designs based on new data and technologies.
Real-World Application
Let me share a concrete example of how I've applied design thinking in my work. When developing an AI-powered building inspection system, we started by understanding the current inspection process and observing inspectors in the field. We synthesized our findings into a key problem statement: "Building inspectors need a way to quickly identify potential structural issues while reducing the risk of oversight."
During ideation, we came up with various solutions, from drone-based imaging to sensor networks. We prototyped a mobile app that used computer vision to highlight potential issues in real-time as inspectors moved through a building. Through iterations and user testing, we refined the app to include features like voice notes and automated report generation.
This design thinking approach resulted in a solution that not only leveraged cutting-edge AI technology but also seamlessly integrated into inspectors' workflows, significantly improving efficiency and accuracy.
Conclusion
By applying design thinking principles, we can create solutions that are not only technically sound but also deeply resonant with user needs. Whether we're designing bridges, buildings, or AI systems, this human-centered approach helps ensure that our innovations truly serve their intended purpose and make a meaningful impact.
In my journey from structural engineering to AI development, I've found design thinking to be an invaluable tool for innovation. It allows us to combine the rigorous, analytical approach of engineering with the creative, user-focused mindset needed to solve complex, multifaceted problems in our rapidly evolving technological landscape.