When AI first became technologically feasible, the only people who were trying to harness the true potential of AI were programmers and engineers with advanced degrees in mathematics. Not anymore. According to an article in the Harvard Business Review, AI has moved from the theoretical (what can AI do?) to the practical (what can AI do for me?). This is the age of “deployed AI,” which is destined to impact everyone. After all, the range of AI applications is staggering. Imagine, having an AI solution that could:
- Read an x-ray and provide a preliminary diagnosis.
- Detect a potential heart problem based on an ECG and then make a life-saving call to emergency first responders.
- Inspire students to discover new medications and then schedule clinical trials.
The possibilities are endless, but realizing that potential requires mainstream application of AI technology. We need to bring AI to the masses, and to do that, we need to make each AI solution people-centric. Design thinking is a crucial framework to secure the future potential of AI.
Design thinking 101
Design thinking is an iterative process that puts people first. Instead of looking at a problem and immediately working toward a solution, design thinking starts by seeking to understand the people who will be using the product or solution and then figuring out what they need and how to provide it. Design thinking reframes traditional development methods to one that focuses entirely on seeing things through the eyes of the user. And it’s an essential—though often overlooked—element of successful AI design.
AI and big data: The potential for real collaboration
Cloud computing and the availability of big data, including time-based data, provides an untold wealth of AI possibilities. The current model of cloud AI focuses on unlocking value for enterprises with storehouses of data. For example, there are several open datasets that have already resulted in robust AI solutions, such as image detection. But there’s a large amount of data that lies buried in corporate vaults, and this is where we need massive collaboration between companies to bring either the data itself in the open or bring AI services trained on this data in the open. Data is still king, and the success of an AI solution relies heavily on the quality of the data gathered.
Consider what Google, Microsoft and Amazon are doing. With the data Google has available, they have been able to step out into the forefront of speech recognition and neuro-linguistic programming. Microsoft is breaking new ground in healthcare AI, while Amazon continues to dominate in transactional AI. These are just a few examples of where we need to introduce design thinking if our intent is to bring AI mainstream.
Applying design thinking to AI development: Putting people first, not a competitive advantage
The quickest way to get a design thinking framework in place is easy: involve more people in the process. Now that AI development no longer requires advanced mathematics degrees, it’s time to collaborate. Working on an AI application to track the migration patterns of the Black-Tailed Godwit? Such a development team could include ornithologists, cartographers, meteorologists, and so on.
But what about company’s competitive advantage? I used to wonder if we were truly able to bring AI to doers while also aspiring to maintain our competitive advantage all the time. What would it take to strike a balance? The key is to augment our thinking around collaborative advantage. If we don’t shift our focus toward collaboration, we’ll miss our opportunity to put AI solutions into the hands of the people who need them. Once again, this is where design thinking comes into play. In the AI products, platforms and services we roll out, the first question we need to ask is, how do we maximize human potential? And we need to be honest about the answer. In other words, how do we put people in the center when it comes to deciding what our competitive advantages are?
Seeing beyond competitive advantage: Moving forward with AI and design thinking
I believe that if we use the same competitive mindset when considering AI assets, we might win in the short term as individuals, but we will ultimately lose as a community. As technology builders and data gatherers, we have a responsibility to put humans first before securing our competitive edge.
Thankfully, while there’s still a long way to go, evidence of collaboration is abundant. Organizations in the open source community are examples of that effort. There are also AI consortiums on ethics and policies. But we need to do a better job reevaluating our product decisions through design thinking. Unless we put humans in the center and put our product decisions in an appropriate framework, our AI applications will look like the tip of an iceberg, with all the rest of the AI potential hidden underwater.