Design's Role In Accelerating AI Adoption

Design is part of the DNA of teams, systems, and processes that define the who, what and why of physical and digital experiences. From concept to delivery, design brings a human-centric approach to the table, considering how technology could be used to solve problems and improve our quality of life.

AI For Humans

However, it took decades for companies to see value in the designer's perspective – how considering human needs could elevate "tech for tech's sake" into products millions of people wanted. For the first time in history, we have an extraordinarily complex and powerful AI technology that, for better or worse, has the potential to create large scale impact on both businesses and individuals. 

By looking at AI (or any technology for that matter) through a lens of human-centered needs, those of us who aren't technical experts can contribute to imagining ways it could be used to solve problems – or cause them. 

AI can offer a myriad of advantages including automating repetitive tasks, predicting problems and recommending specific solutions, drive data-driven decisions, and creating more personalised experiences. However, adoption of AI requires a human-centered perspective to get better user outcomes. 

AI By Humans

The next question that arises is, "Can an idea be shaped by AI?" This is a question that product teams in a technology company often go through identify business goals, consider users' needs, ideate ways to solve the problem, align with experts on technical feasibility etc.

However, what makes an AI concept technically feasible generally comes down to two things:

• Do the AI models needed for performing the functions of the concept exist or can be created?

• Is the data necessary to train the AI models legally available, and ethically structured?

Whenever an AI model is built, the results it delivers will be a reflection and magnification of what humans teach it. If it only learns from one type of person, its results will neglect the needs of others. Hence, when it comes to AI, having a team with diverse perspectives, skillsets, abilities, cultures, languages, etc. is essential to creating responsible AI models.

Needless to say, diversity on teams doesn't always lead to effortless collaboration. Humans, however, come with their own set of conscious and unconscious biases, which can lead to biased outcomes from AI models. And therein lies another powerful way in which design is changing our journey with AI.

Bringing people who don't usually collaborate out of their shells, through design thinking to build more trustworthy AI has not only improved its quality and accelerated delivery, but has elevated and matured team cultures.

Traditional design thinking exercises such as empathy maps and user journeys can also help diverse teams understand each other's roles. Everyone's ability to understand the full picture of the problem increases the chance of finding solutions faster and at scale. Furthermore, this leads to a new depth of diversity and collaboration across teams. 

In fact, data scientists and machine learning engineers are learning to take a human centered viewpoint on their work, and evolving design thinking to include technical aspects of AI, allowing non-tech experts to contribute.

The Way Forward

We are still at the early stages of this journey with AI. However, a shift is taking place with the role of design changing across all levels including in boardrooms. Similarly, we are seeing the growth of applied AI to help improve people’s lives; whether it is simplifying bus journeys in India through algorithms, or making our world more sustainable with AI-based sensors. 

We're all learning how various roles can collaborate and even discovering what few roles need to emerge. Few examples from the design field include:

• Conversation Designers: Design the use of language, dialect, and culture as the main point of interaction between a user and a system

• AI Choreographers: Design user journeys that involve multiple devices and modes of interaction, working together to accomplish a job or develop a relationship

• Data Storytellers: Ability to look at data and communicate what kinds of problems it could be used to solve

To organisations embarking on an AI journey, here are the key takeaways:

• Be crystal clear on the user pain point you are trying to solve, and if AI can help with it. Don’t force fit AI just for the sake of technology.

• For your AI solution to be successful, it's critical that you have diverse minds working together from concept to delivery.

• Design can be a natural starting point for bringing multi-disciplinary teams together to fully understand, stress-test and succeed in creating the right kinds of trusted and effective AI solutions.