How can design thinking support the development and adoption of emerging technologies like AI and IoT?

It is very tempting in early discovery phase to start with a solution in mind like "we have to use AI/IOT". Design thinking is about flipping that on its head and start with the customer in mind. We should ask "What problem do they have when doing X"? AI and IOT is just a tool. They might not be the best solution for every problem. If we specifically want them as a solution, I would recommend doing that later on in the brainstorming stage (after understanding the problem). You can allocate time to specifically think about "How might we solve this problem using AI/IOT/or other emerging tech".

Understand user needs--By empathizing with users, you can gain valuable insights into the specific challenges you are trying to solve, and design AI solutions that address those needs. Define the problem and interpret the results-- You can use design thinking to synthesize the user research data; identify patterns/trends, and define specific AI challenge you want to tackle. Ideation and prototyping --Brainstorming allows you to explore a wide range of ideas that can then be quickly prototyped & tested with users to gather feedback & refine the AI concepts. Testing and Refinement --The iterative approach can help you to continuously refine & improve AI solutions based on the ongoing feedback from users.

In my experience, design thinking is key in shaping how we use emerging technologies like AI and IoT: Start by understanding the problem: Focus on the user's needs rather than jumping straight to technology solutions. Define the problem clearly: Pinpoint where AI or IoT can make a difference based on user insights. Get creative with ideas: Brainstorm various ways technology can address the defined problem. Prototype and test: Create prototypes and gather feedback to refine the solutions. Iterate for improvement: Continuously refine AI or IoT solutions based on user feedback. By following these steps, design thinking ensures that technology adoption is user-centered and impactful.

Jumping into AI and IoT has its perks – efficiency, personalization, you name it. But let's not ignore the flip side: ethical dilemmas, security headaches, and more. To make these tech wonders work for us, we've gotta get real with the context they're in and the problems they'll solve. That's where design thinking steps in – helping us dig deep with user research, stakeholder chats, and scenario playbacks. Think of it as seeing the big picture with empathy goggles on. We're not just chasing efficiency; we're crafting solutions that play nice with our values and tackle risks head-on. In today's tech whirlwind, design thinking isn't just a nice-to-have – it's a must for building a future that's both kickass and responsible.

Sure you got to find out the capabilities of emerging tech, what they can do in various use cases. But design thinking start with the users, so the context you need to have a grasp in is what unmet needs and latent desires that can be met with the application of emerging tech.

In my experience, defining the problem through design thinking involves understanding people's needs rather than just focusing on technology. It's about empathizing with users, listening, and asking questions to grasp the big picture. Design thinking offers the freedom to set our own path, whether it's engaging with users, stakeholders, or exploring technologies. By considering the human element, we define the problem in a meaningful and impactful way, ensuring our solutions address real needs effectively.

The process of defining the problem to solve with the emerging technology is the same with the common method we use in any design thinking work. In order to specify it to work hand in hand with the technology, we could specify it as a condition when coming up with the HMW questions.

It is not so much a technological problem as a psychological one. From a bird's eye view, the innovation process can be divided into three large blocks: Exploring, experimenting and implementing. From a frog's-eye view, there are countless tapping steps, some of which follow each other and between which the innovators keep going back in order to incorporate newly acquired knowledge into the development process. But it always starts with listening, understanding, asking questions, listening again and so on. #FutureSkills #CorporateLearning #transformationleader

Through Design Thinking, you are free to set your own path on navigating through the problem. Who I should talk first? The user who is suffering? The CEO who is going to sponsor? Or the vendor who will provide the technology? Design Thinking makes you ideate how you are going to target the problem, without thinking on the problem itself yet. It's crucial.

In emerging tech, especially AI and IoT, there are many problems. Focus on solving the most pressing issue first, then move to the next, ensuring effective, step-by-step problem resolution. Design thinking helps with: 1. Identifying issues via brainstorming, user observation, and data analysis. 2. Encouraging a deep dive into root causes for meaningful solutions, rather than temporary fixes. 3. Deep-diving into every problem is not possible. So, group-related issues to find core problems. This approach streamlines focus and increases the impact of solutions at the foundational level, vital in the complex AI and IoT ecosystems. Targeting core problems, design thinking facilitates the creation of more effective and sustainable technologies.

In my experience, generating ideas through design thinking involves embracing creativity and exploring diverse techniques. I often start by jotting down every idea, even the wildest ones, on a blank page to clear my mind and reduce anxiety. Additionally, I avoid staring at blank post-its during brainstorming sessions and instead utilize techniques like the "worst idea" method or combining Jobs-To-Be-Done with current trends. By mixing creativity with varied approaches, we can uncover innovative solutions to the problem at hand.

I love to get a blank notebook paper, and in 1 minute, to write down exhaustively, all the ideas that could solve the challenge you have, including the crazy ones! Believe that, this will: Reduce your anxiety, because you cleaned your mind. Take note that sometimes the crazy ideas are not so crazy in a second view, and suppose to be the best ones.

Use different techniques for ideation. Never ever brainstorm staring at the blank post-its, especially in groups. - Worst idea - Brainswarming - JTBD + trends These are just some ideation techniques that can be used.

Mind Mapping helps in visually exploring AI/IoT applications (e.g. healthcare diagnostics or smart home functionalities). While brainstorming generates ideas for IoT devices (e.g., smart home features) or AI functionalities in finance (such as personalized financial advice). Prioritizing features for IoT devices (e.g., inventory tracking vs. recipe suggestions) or AI interfaces in finance based on user needs and impact can be does using Voting/Ranking and helps us keep on course. These techniques allow for comprehensive exploration of possibilities while swiftly narrowing down and refining ideas, ensuring that emerging technologies like AI and IoT meet user needs and are seamlessly integrated into various industries.

In design thinking, generating ideas is a critical phase, especially for AI and IoT development. This stage involves brainstorming a wide range of solutions, encouraging creativity and out-of-the-box thinking. It's about exploring various possibilities without the constraints of feasibility or practicality initially. This approach is particularly important in AI and IoT, where the potential applications are vast and often uncharted. By fostering an environment where any idea is welcome, design thinking ensures a diverse set of solutions are considered, leading to more innovative and effective uses of these emerging technologies.

In my experience, prototyping solutions in design thinking involves swiftly bringing ideas to life and testing them. I typically begin by manually simulating processes before diving into complex coding, ensuring a thorough understanding and potential improvement of concepts. Additionally, prioritizing a prototyping mindset over specific tools enables me to adapt visualizations at different stages of development—starting with sketches or clay models, transitioning to wireframes or makeshift prototypes, and finally utilizing tools like 3D renderings or high-fidelity prototypes to present a refined vision. This iterative approach fosters quick iteration and visualization, allowing for effective refinement of ideas to meet user needs.

Prototype means how fast you can materialize and test an idea. Best ways of testing an AI or IoT idea is first of course trying manually, make exactly the process manual before bring the AI technology, than quickly without any coding or experience, you can simulate many possibilities before the hard programming aspect.

When adopting new technologies, we may also face a lower level of awareness among our targeted users. It’s important to remember that while adopting new technologies may be our internal goal, users often do not care about this until it directly affects them. When everyone is boosting the topic, it creates fatigue around the new technology. Yes, everyone is talking about it, and yes, it is the future, but does it really help or address the needs of the user? Prototyping can bridge the gap between adopting new technologies and addressing user needs. Trying different scenarios can provide insights into where the sweet spot is — where adoption meets real user needs and creates synergy.

Prototyping mindset is more important than the tools itself. If you have a builder mindset, at every stage of the development you got to know what to use to visualize the vision. 1. At the early stage, lead with sketches, clay models, etc. 2. At mid-stages, lead with wireframes, make-shift prototypes, etc. 3. At late stage, lead with 3d renderings, hi-fi prototypes, visionary videos, etc.

Design Thinking enables quick prototyping by designing basic models like a prototype of a smart home device for IoT or a simplified interface of an AI-driven financial advisor. Putting these prototypes in front of users to gather feedback on functionality and usability. For instance, testing the functionalities of a smart thermostat prototype in a controlled home environment or getting user input on the ease of navigation in an AI-powered financial app interface. The prototype can then be refined by incorporating user feedback into successive iterations of prototypes. For example, based on user input, additional energy-saving features can be included in the smart home device

our products will not be used by robots. Our users are people. And people are not homogeneous. Your users will likely have different life experiences, motivations, pain points, and expectations from the technology. They might be at different levels of technological experience and, well, they might just change their minds for no obvious reason. Therefore, there is no substitute for usability testing with your users. AI can help for example in speeding up tests on compliance with heuristics, for example Heurix or by allowing you to quickly summarize and analyze various test sessions. By essentially defining a solid design to begin testing on, we will likely need fewer iterations and should have a shorter and more efficient design process.

Testing and iteration are stages where adoption often fails. There are a few reasons for this. First, as organizations, we often pull ourselves to maintain the status quo — change is difficult, and we may be reluctant to push until it is noticeable and drives value. Secondly, we tend to focus on ticking the box of adoption rather than truly understanding user needs. This creates a situation where we believe we are done once the technology is adopted. We're only motivated to iterate further when we focus on the user and see the friction in the use of new technology. It will be hard to find motivation to iterate if the initial goal was just to ensure we use something new.

The "Test and Iterate" phase in design thinking is crucial for refining AI and IoT solutions. During testing, prototypes are exposed to real-world scenarios and user feedback is collected. This feedback is invaluable, as it highlights the strengths and weaknesses of the solution in actual use cases. Iteration follows, where insights from testing are used to make improvements. This iterative process is particularly important in AI and IoT due to the complexity and rapidly evolving nature of these technologies. Continuous testing and iteration ensure that the final product not only meets user needs effectively but also remains adaptable and up-to-date with technological advancements and changing user preferences.

In the testing phase and when presenting results to the client, the use of artificial intelligence has transformed a task that was once complex and time-consuming into a significantly faster and more efficient process. Regarding the documentation of tests, AI has greatly simplified steps that were previously slow and less productive. With the ability to complete documentation more swiftly and efficiently, it's now possible to more quickly focus on the necessary corrections and adjustments that the testing process uncovers. This AI-facilitated advancement in the testing phase is crucial for the development and enhancement of digital products, allowing for a focus on the adjustments that truly drive the product's progress.

It is so (SO!) important to have variety and depth in your users. Especially as we get into the use of more and more AI technologies, its vital that your user testers not only represent your ideal users, but they have diverse backgrounds.

“Technology is at its best when it is invisible.”, said Nassim Nicholas Taleb, author of The Black Swan and Antifragile. The best experiences, enabled by technology such as AI and IOT, solve wicked problems and create delight for consumers. And design thinking can un-GEEK the experience with its human-centric approach. How? 1) By fostering empathy and encouraging innovative solutions tailored to real-world needs. 2) It addresses ethical concerns like privacy and different types of bias, vital in AI and IoT. 3) Its iterative process allows for continuous refinement, crucial in evolving tech fields. 4) Promotes interdisciplinary collaboration with psychology, anthropology, business & technology for better innovation outcomes.

Design thinking is a critical tool in the development and adoption of AI and IoT technologies. It involves empathy, clear definition, creative problem-solving, rapid prototyping, and testing with real users. This human-centered approach ensures that AI and IoT solutions are user-centric and effectively address real-world problems. By understanding the needs, challenges, and context of end-users, design thinking helps create solutions that address the right issues and provide meaningful solutions. This approach saves time and resources, enables quick testing, and ensures intuitive, effective, and valuable technology for users.

The goal is to be quick, as soon as you start collecting the feedback from your stakeholders you can quickly change the course of action if needed, which means, you can make enhancements at the workflow of your AI, in any layer, like Data, ML or the DS aspect of the solution, other than enhancements you can add new inputs, remove or drop everything in case the success criteria was not achieved, fail fast and redo faster.

Design Thinking opens up the aperture for real world adoption. It helps create possible futures and real world applications rooted in evidence not theory. At the end of the day it can guide designers to create use cases that will accelerate public adaption at an exponential rate.

The 5 Step AI Innovation Action Plan essentially taps on the engine of Design Thinking- where we apply the Innovation Process for organisations to adopt technology for example AI. Now, it follows these 5 steps: 1?? IDENTIFY- Identify value add opportunities within industry / organisation 2?? ASSOCIATE- Associate the opportunities with the AI current + future use cases 3?? INNOVATE Create a new AI experience to achieve the opportunity 4?? INTEGRATE- Integrate the AI solution into different aspects of operation 5?? LEARN- Learn from feedback and evaluation Lets process the adoption of AI together!