Dr. Pavan Soni
Not many people know that both artificial intelligence and design thinking can be traced back to the pioneering work of Nobel Prize winner Herbert Simon. In 1956, Simon, along with Alan Newell and Cliff Shaw, developed the first AI computer program, the logic theorist. His 1969 book Artificial scienceSimon formally introduced the concept of AI, suggesting how problems can be solved in the real world, and the role of heuristics (the thumb rules). In the same paper, he laid the foundation for design thinking and stated, “We design people who will device courses of action that aim to turn existing situations into situations that everyone likes.”
Fast forward to 21st century. Thanks to its journey in calculation, communication and commercial, both design thinking and artificial intelligence have almost reached mainstream society and industry. Design thinking is rooted in empathy, but AI is often seen as “indifference.” It's a relaxed chunk of the value chain, pulling away work, evacuating industry. The right question is, how do you adjust the two? Looking at the process model of design thinking, we identify several ways that AI can lend more than aid. Potentially, it can make design thinking broader and more effective without robbing the essence of human centrality. Let's dig deeper into the path of convergence.
At a very high level, design thinking can be understood as a human-centered, iterative model of problem solving. It consists of five stages. Inspire (Set the “why” in question) I will relate to define (Understanding the issue from the perspective of the stakeholders) Ideas (generates many related ideas), Prototyping and testing (to validate these ideas in real context), and scale (Show ideas to products and profits). The entire process is iterative and guarantees the following conditions: boldness in goals, ambiguity in context, access to customers, availability of time, and diversity in teams.
Where AI intervene: 3 important phases
There are three areas where AI can help you with the problem-solving process of design thinking LEDs. First, it defines the stages by empathy. Traditional approaches to product design and creation of experience relied heavily on human-human interactions, ethnography, anthropology, insight clinics, interviews, focused groups, observations, and more. Ergo, vast insights are drawn from a limited qualitative sample in the hope that subsequent ideas will work on a large scale. Many failed products, especially those conceptualized in the West, or those drawn from dipsticks with elites, prove that self-limiting is often an archaic means of design thinking.
Because it relies on data and generation capabilities, AI can help reach more audiences and use machine intelligence to enhance human intelligence. You can curate questions, analyze answers in detail, and employ AI in this fuzzy front-end for problem solving to ensure more coherent images. For example, the alphabet and meta regularly scan many customer forums and media, hearing not only complaining, but also gather novel insights into what works and what doesn't.
Second, in the idea stage, AI can be extremely valuable. One of the spirit of creativity is that quantity leads to quality. “The way to get good ideas is to get lots of ideas and throw away the bad ones,” he said, winning Nobel Prize winner Rhines Polling twice. In a typical idea workshop, participants often become saturated and generate predictable, trivial ideas. The situation is getting worse by not wanting to throw away the bad stuff. But what happens if such an idea is generated by a machine? Humans will give prompts, machines will generate multiple combinations of otherwise common ideas, and together they will select a more appropriate idea.
Such an approach not only solves the problem of the rarity of the idea, but also takes away the moral hazard of not being able to “kill your beloved.” In the new realm of inventiveness, it always happens. The algorithm throws all the possible combinations on a skilled scientist and sifts it to get to the most viable. Such mechanical intervention can reduce type-2 (false negative) errors, thereby reducing the likelihood that the human mind will lose a promising idea that it has not been able to recall itself.
Finally, AI can save time and money during the prototyping and verification phases. In many cases, not all ideas are tested due to lack of time or human bias. As a result, the dominant voice on the table will take over, or companies will be recoiled against low-risk ideas that have been tried and tested. It equals the generation of insights and wasted stages of ideas, and the attenuation of employee morale. What if, if possible, you can systematically test more ideas in parallel? You can make more data-driven judgments about their fate. AI can do it for you and on a compelling budget. Thanks to large-scale language models and self-learning capabilities, the algorithm identifies the appropriate target audience, assesses the effectiveness of the idea, confirms the importance of the sample, and asks for responses. Think about how good a good machine is when conducting A/B tests, because it launches a new website or places ads on Insta pages.
It supports machines, but guides humans
With machines that help us, it is important that humans move up the value chain, rather than wanting the same space. The role of artificial intelligence in thinking is a compelling case that Herbert Simon was pretty clear. I hope that without losing either essence, it gives AI a chance on the design journey.
The writer is a bestselling book author, designing your thinking and designing your career.
Disclaimer: The views expressed are personal and do not reflect the official position or policy of FinancialExpress.com. Reproducing this content without permission is prohibited.
