Recently the New York Times reported on how minor league baseball is starting to use artificial intelligence (AI) to help umpires. As the paper reported, the umpire wears an earpiece which provides information as to whether a pitch was a ball or a strike. Anyone who watches baseball will probably be unsurprised by the move, given the prevalence of technology already in use, particularly for those watching on TV.

What is interesting about this, and what makes it a great example for what is happening in many industries, is that baseball games will still require an umpire. They will remain a critical part of the game, and there is no suggestion that their job will disappear. AI in this case is therefore helping them become better at their job, and helping them become more accurate in a particular part of their role. In this article, I want to discuss the role of Design Thinking in creating these systems where AI works side-by-side with people, helping them to become better at their jobs.

Another great example is with customer service agents, where a company can use bots to answer certain customer queries, but humans are responsible for other situations – such as when more empathy or understanding is required. Or look at the case of creating maps from satellite imagery. Despite advances in machine learning, humans are still required to provide contextual analysis and insight.

These successful implementations of AI reflect the technologies’ maturity. We can see how it is already transforming how businesses operate, their systems, and how they engage and interact with their customers. But for all the talk of whether machines will replace jobs, I believe this is actually what we will become used to. People will work together with machines to make better, more informed decisions. For several years now in the world of big data, we’ve spoken about attempts to make business executives less reliant on their intuition, and instead used data to make informed decisions. When this data becomes more easily accessible, more usable, more timely, then people not only use it, but want to use it.

The role of Design Thinking

The challenge therefore for business and technology leaders is to create services and workflows where people and machines complement each other. That’s why I believe Design Thinking will have an ever more important role to play, when companies create these AI systems – because the approach helps bring in the end user’s perspective, and how people will actually use the system.

Research from the Harvard Business Review involving 1,500 companies supports this assertion. They found that those companies using AI to displace workers only achieved a short-term productivity gain. The real long-term value came when humans and AI worked together, as this key quote highlights: “Through such collaborative intelligence, humans and AI actively enhance each other’s complementary strengths: the leadership, teamwork, creativity, and social skills of the former, and the speed, scalability, and quantitative capabilities of the latter”.

Building human-centric AI systems with Design Thinking

In light of this, we have to consider how to best build AI systems that help people with the task at hand. I recommend considering:

• Evaluate where AI can provide the most value. Tools and approaches such as customer journey mapping and evaluating customer experiences will provide critical analysis to identify where intelligent systems can provide the most value.
• Bring in Design Thinking at the earliest stage possible. Ideally during the ideation and design phase of development, make sure to bring in the perspective of the end user, and how they are likely to use the system and what is the result they want to achieve?
• Work with the people who currently have the expertise. For example, if you’re building a system to help people check into their hotel, then work with the check-in staff, who speak every day with customers, and who will have a wealth of knowledge about the typical concerns and questions that people have. The idea here is to try and ensure your company culture is reflected in the AI system that you’re building.
• Look to build systems of intelligence. In an insightful article, the venture capitalists at Greylock Partners, talk about the emergence of “systems of intelligence” which bring together various data sets and various systems of record, to create intelligent new products and services. The challenge for many companies is that the data that will inform these new systems is held in various different databases throughout the company. The VCs however believe such systems will be the new “moats” upon which companies can build sustainable and defensible business models.

What it means for developing AI systems

For those of us in the tech industry, working on creating such AI systems, this means evaluating carefully how we expect people to use these systems. In the design phase of creating a new product, we need to look to approaches such as Design Thinking and customer journey mapping, to understand how people will use the system, and how it will help them fulfill the task at hand.

This is a more evolutionary approach, more human-centered perspective, and one that recognizes that AI systems work best when they help people, rather than replace them. We will increasingly see such approaches, which will ultimately form the basis of long-term business success.