Creating Symbiotic Intelligences: Why Design Thinking is Behind the most Successful AIs

Using Design Thinking at every step of AI development creates a deeper purpose for AI that enhances our lives and businesses.

Every day artificial intelligence performs incredible feats that improve our lives. From predicting our needs, to curating our interests, to classifying and storing our data, people and machines are more interdependent than ever before.

Because of this interdependency, putting people-centered needs and experiences first is what separates purposeful AI projects from unsuccessful ones. This means considering users from concept ideation to UX. No matter how accurate our AIs are, or how intricate our models become, they won’t be as relevant, insightful or respected if they do not engage with the end-user and provide value to the end-user every step of the way.

Luckily, a human-centered design framework exists that will benefit every AI project that implements it.

Design Thinking, a concept popularized by Stanford’s, harnesses the core principles of design and product development to create a human-centered approach to problem solving. In 5 interlinked steps (empathize, define, ideate, prototype, test), it enables businesses and developers to scale their functions to prioritize both human-centered experiences and highly disruptive products.

The most integral component of Design Thinking involves cultivating empathy toward the people for whom your AI will interface. To do this, each step attempts to create a holistic experience of how your idea will ultimately be consumed. Throughout the process, problems can be framed and reframed, new questions can be asked, ideas and prototypes can be brainstormed and tested, and, eventually, the best solutions can be chosen.

These steps aren’t linear or finite; they can occur simultaneously and be repeated. It’s a living, breathing framework, just like AI itself, one that is constantly building off itself and growing in new, unforeseen ways. This is what makes AI and Design Thinking such a perfect match: they are not simply a means to an end. They are both endless roads toward growth.

Think of personal assistant AIs like Siri, Cortana or Alexa. These are actually great examples of human-centered design, and are no doubt the products of Design Thinking. Their value is not in the machine or the algorithm, it is in the psychology of the end-user. They make traditionally slow and multi-step tasks much smoother and easier and they utilize a complex, constantly learning machine intelligence. Although they are highly functional, ultimately it is not just the technology capabilities that make these product disruptive.

What makes AIs like Siri, Cortana, and Alexa so successful is the fact that the machine and its user interact in the most human way possible — through natural language, voice recognition, and conversation. Marrying the most integral aspects of human psychology with the burgeoning abilities of AI is the surest path to innovation in the modern world.

There are many other ways that human-centered design and Design Thinking can give purpose and nuance to AI. Here are just a few examples:

  • Design Thinking powered AI can help eliminate algorithmic bias that often leads to exclusionary and even discriminatory practices
  • Design Thinking can assist and enhance the curation of the data that is required for AI feature engineering
  • Design Thinking can facilitate crowdsourcing of growth ideas from employees and customers which drive solutions powered by AI technology
  • Design Thinking powered AI can help translate AI’s process and output into relatable, human contexts such as a conceptual illustration, data visualization, or a voice interface
  • Design Thinking powered AI can analyze the responses and thoughts of end-users, to allow the AI to learn and adapt over time

What is important to remember is that designing AI does not mean we are creating robots that are isolated from human needs or interactions. AI is not a replacement for human thought. It’s a symbiotic partner. Because of this, successful applications of AI require more than just big data, powerful processing, and complicated algorithms. Designing truly useful AI requires a complete understanding of user needs, experiences, and, on an even deeper level, psychology.


Design Thinking provides large and small AI projects a framework to do just that. It enables designers to engage with the users’ desires every step of the way, in order to achieve AI solutions that are purposeful, engaging, and intrinsically human.

For personalized insights on how to implement Design Thinking into your next AI project, contact Liquid Analytics for a free consultation.