Translator | Li Rui
Reviewer | Sun Shujuan
Artificial intelligence development managers and designers often use design thinking methods to build more human-centered and agile development of artificial intelligence systems.
Choosing the right project management method is crucial to the project development of an enterprise. It will help developers reduce errors, speed up the development process, and help identify problems among target groups. Only after gaining a deep understanding of the needs of the target group can developers develop solutions to their problems. There are many methods of project management that focus on problem discovery, and design thinking is one of them.
Artificial intelligence is becoming a more important and critical part of people's lives. From self-driving cars to voice assistants like Siri or Alexa, products and services based on artificial intelligence are everywhere. AI design thinking is the process of designing artificial intelligence systems that can operate in a lean and iterative manner in unpredictable environments with limited resources. Designing for AI requires different skills than designing for other types of technology because AI does not follow predictable rules and behaviors. Businesses need to understand more about how AI affects processes and how to implement it into their AI projects.
Design thinking is one of the ancient (but still very modern) methods of creating a perfect development process. This approach starts with the user and puts the user at the center of the entire development. Users' needs, emotions, feelings, and problems should be the most important thing to the development team.
In the 1960s, explorers began to formulate their first ideas around design thinking, which can be found in L. Bruce Arche’s book The Visual Thinking Experience. Their goal is to use tools and best practices for creative people such as painters, writers, or designers in practical product or service development.
For some time now, Design Thinking was a bit forgotten in the IT world, or it was not the most popular method in the Agile or Scrum era, but its popularity comes with some clear improvements to projects from implementing Design Thinking The degree continues to improve, such as faster and better decision-making, helping enterprises have a clear understanding of the problems of target groups, reducing risks of the entire project, etc.
One of the challenges of applying design thinking in artificial intelligence is that there is no universal approach. After several years of working on similar projects, Nexocode developed a mature and battle-tested process that uses many frameworks and solutions from Design Thinking, such as the one popular in the software development community, the Design Sprint framework solution. Integrate and match knowledge and experience to create a roadmap for every business looking to innovate with machine learning. It starts with an AI Design Sprint workshop tailored for each client, focused on researching AI opportunities, prototyping and testing possible AI implementations. We believe that every business wants to develop a useful AI project to understand why, where and how they should develop the project, which is why the AI Design Sprint focuses on these topics. This was just the beginning, but once the client decided to continue developing the project, every step taken was iterative on the design.
When incorporating design thinking into AI development, teams are key. Therefore, having a team of experienced AI experts is crucial. They will play an important role throughout the entire process and their knowledge and skills will have a significant impact on the project.
Designing artificial intelligence requires different skills than designing other types of technology because artificial intelligence does not follow predictable rules and behaviors. This means creating solutions that are as human-centered as possible, taking into account the needs, emotions, feelings and thoughts of the users who will be using these technologies every day, taking into account all the things they may face while using this AI-based process Problem product or service. The feasibility of a design solution and its impact are not as obvious as in normal software development. Machine learning projects require good, ethical design and reliable data sources. Every project is different, but the project manager’s data science knowledge is critical to successful R&D.
Designers should focus on AI design thinking to create human-centered AI products and services. This is why AI designers must follow the same design thinking process as other types of technology, but also consider the emotions, feelings, and thoughts of the people who use these technologies every day, taking into account all issues, including AI ethics, they use issues that may be faced when implementing such artificial intelligence solutions.
In artificial intelligence projects, taking responsibility is crucial, because products and services based on artificial intelligence are already affecting people's daily lives.
When using artificial intelligence in all aspects of people’s lives, designers who engage in artificial intelligence design thinking should consider all possible scenarios while considering different types of risks during actual use. For example, if an AI system makes a specific decision, who should be held accountable? Are AI system decisions final, or are there human oversights?
Deep learning systems work like black boxes. Their decision-making processes cannot be explained in a similar way to how people might make decisions. To some extent, all AI solutions can and should be explainable. However, AI designers need to understand that AI is not a magic box and that there are rules for the way it works, meaning that one might know why the AI works the way it does in a particular scenario.
Products and services based on artificial intelligence may not be easy to trust. AI algorithms are often opaque, and a lack of interpretive AI can lead to over-reliance on AI. Design thinking is a tool that allows developers to build trust in artificial intelligence by designing systems that provide users with clear feedback loops so that they understand what the AI algorithm is doing.
Human-computer interaction is a new thing and must be different from standard human-computer interaction. There are several human-computer interaction best practices and recommendations. The design thinking approach is a great framework for AI-based products and services because it encourages businesses to think about AI from the end-user’s perspective and focus on possible interactions.
In this context, the main advantage of design thinking compared to other methods is that it allows designing artificial intelligence solutions by considering input data, algorithmic processes, outputs and all possible scenarios in which artificial intelligence can be used. This gives designers more control over the AI decision-making process, making AI more explicit than programming languages.
One of the most important things to understand when talking about design thinking is this stage. Design thinking is a simple process in which the next phase builds on the previous phase and can only begin after the previous phase is completed.
This stage focuses on developing empathy for the company’s users. Many people representing different societies, ways of thinking, experiences and groups should be brought together and together with them discover their feelings, thoughts and expectations. For example, how a business can use its product or service to improve their lives.
What needs to be remembered is that the center of the enterprise development process is always centered on the people and their needs. Enterprises are implementing a back-end AI platform that helps their manufacturing processes. There are many stakeholders involved in every process, and the first phase is about feeling and future goals and opportunities. When implementing artificial intelligence, this stage becomes more complex, as some knowledge about machine learning models, neural networks, or data analysis may be required. The feasibility of AI must be considered at the outset of the project to avoid introducing complex implementation processes into existing solutions.
After interacting with different people, you can define its target groups and target challenges. Developers need to think about opportunities to adopt artificial intelligence. Select a group with specific and legitimate needs and focus on their problem to implement the AI solution. This allows you to clearly see the entire situation and select which issues to address in your project. Now is the time to ask questions, seek insights, and dig deeper.
This stage is to find solutions to the problems of the target group. A team needs to be assembled and team members can brainstorm ideas. The goal of this phase is to unleash the team’s creativity and find some new or unusual ways to solve the target group’s problems. At this stage, it is possible to formulate which AI algorithms, tools, and techniques will be used in the project.
After team members brainstorm, select the most interesting ideas and convert them into prototypes, such as minimum viable products (MVP), to collect knowledge as quickly as possible. There is no need to develop a comprehensive AI solution at this stage, as this is a time-consuming process. The main goal at this stage should be to have the ability to learn. Using this AI development approach, businesses will choose the best method to develop the perfect software.
This is the last but probably one of the most important stages because it helps companies identify and eliminate problems with the product. This is the moment when the prototype is presented to the target group or tested in a near-realistic environment selected in the first phase. You can observe users' reactions, how they use the products provided by the company, and their emotions. Will the business's solution solve their problem? If they don't like it, now is the time to improve.
Improve customer satisfaction and business adoption of internal software within the enterprise in every project (including those based on artificial intelligence) One of the most important advantages of implementing design thinking in projects. Users of products built using design thinking methods show higher levels of satisfaction when using them. Since a company's users are always at the center of product development, customer satisfaction should always be its primary goal.
The time an enterprise spends on design thinking exercises can ensure its long-term artificial intelligence investment. Every business has its own characteristics and needs. This is why the implementation of machine learning should be tailor-made. Design thinking helps identify pain points in an enterprise and define the business case for AI, therefore helping to transform its AI dreams into profitable investments.
The ideation stage of the design thinking process aims to think in non-standard ways, that is, out of the box. This approach can lead businesses to find solutions to problems they had never considered. This may help a business create a very innovative solution and stand out in the market.
When a company investigates the target group and its problems in detail, it will develop products that meet their needs, and the product will have a higher chance of success.
The design thinking method will be around for a long time, which also proves that it is an effective and useful method that can be used for the development of artificial intelligence products or services.
But this is not an easy task, and companies may encounter problems. Therefore, it is essential to find a reliable partner to support its team, and this will start with strategy meetings and end with a successful testing and development phase.
Original title: Applying Design Thinking to Artificial Intelligence. Why Should You Use It in Your AI-Based Projects? , Author: Dorota Owczarek
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