To make it easier for staffers to create highlight reels and other media from all this material, the NFL partnered with Amazon Web Services in December 2019 to use artificial intelligence to search and tag its video content. The first step of the process required the NFL’s content creation team to teach the AI what to find. The team created metadata tags for every player, team, jersey, stadium, and other visually recognizable content it wanted to identify within its video collection. It then combined those tags with Amazon’s existing image-recognition AI system, which Amazon had already trained on tens of millions of images. The AI was able to use both sets of data to flag relevant imagery within the video library, and the content creation team was able to approve each tag in just a few clicks. Whereas employees once had to manually search, find, and clip each video, store it in a repository, and then tag the video with metadata, Amazon’s AI automated most of the process.
In a previous HBR article (“Collaborative Intelligence: Humans and AI Are Joining Forces,” July–August 2018), we described how some leading organizations are defying the conventional expectation that technology will render people obsolete—they are instead using the power of human-machine collaboration to transform their businesses and improve their bottom lines. Now several companies are not merely out-innovating their competitors with this approach; they’re turning even more decisively toward human-centered AI technology and upending the very nature of innovation as it was practiced over the previous decade.
In the NFL’s case, for example, AI accelerated the image-recognition process, but the system would have failed without employees determining which data needed to be uploaded and then approved. And the NFL didn’t simply hand the job of making highlight reels over to AI; content creation experts performed that work, but they did it faster and more easily thanks to AI’s unique ability to quickly sort through massive volumes of information.
The new human-focused approach to AI is changing assumptions about the basic building blocks of innovation. Companies such as Etsy, L.L.Bean, McDonald’s, and Ocado are redefining how AI and automation can knit together a wide range of cutting-edge information technologies and systems that enable agile adaptability and seamless human-machine integration. (Disclosure: Several companies named in this article are Accenture clients.) These path-breaking firms have invested in digital technologies at unprecedented rates to respond to new operational challenges and rapidly shifting customer demands. They’ve dramatically increased investments in cloud services, AI, and the like, and they’re generating revenue at twice the speed of laggards, according to a 2019 Accenture survey of more than 8,300 companies. A second study, of more than 4,000 companies in 2021, shows that the 10% making the biggest commitment to digital technologies are rocketing even further ahead, growing revenue five times as fast as laggards.
We’ve turned what we’ve learned from this research into guidance that business leaders can use to compete in a world where most companies will owe their success to humans rather than machines. Our IDEAS framework calls for attention to five elements of the emerging technology landscape: intelligence, data, expertise, architecture, and strategy. It can help both technical and nontechnical executives to better understand those elements and conceive of ways they might be woven together into powerful engines of innovation.
In this article, we use the IDEAS framework to examine examples of businesses that have implemented human-driven AI processes and applications to solve problems in e-commerce, online grocery delivery, robotics, and more. You can do likewise, marshaling the skills and experience of your own people to manage technological innovation in everything from R&D and operations to talent management and business-model development.
Intelligence: Make AI More Human and Less Artificial
Human intelligence and artificial intelligence are complementary. No machine powered by AI can match the ease and efficiency with which even the youngest humans learn, comprehend, and contextualize. Accidentally drop an object and a one-year-old who sees you reaching for it will retrieve it for you. Throw it down on purpose and the child will ignore it. In other words, even very small children understand that people have intentions—an extraordinary cognitive ability that seems to come almost prewired in the human brain.
That’s not all. Beginning at a very young age, children develop an intuitive sense of physics: They expect objects to move along smooth paths, remain in existence, and fall when unsupported. Before they’ve acquired language, they distinguish animate agents from inanimate objects. As they learn language, they exhibit a remarkable ability to generalize from very few examples, picking up new words after hearing them only once or twice. And they learn to walk on their own, through trial and error.
Conversely, AI can do many things that people, despite being endowed with natural intelligence, find impossible or difficult to do well: recognize patterns in vast amounts of data; defeat the greatest champions at chess; run complex manufacturing processes; simultaneously answer many calls to customer service centers; analyze weather, soil conditions, and satellite imagery to help farmers maximize crop yields; scan millions of internet images in the fight against child exploitation; detect financial fraud; predict consumer preferences; personalize advertising; and much else. Most important, AI has enabled humans and machines to work together efficiently. And contrary to automation doomsayers, such collaboration is creating an array of new, high-value jobs.
At Obeta, a German electronics wholesaler whose warehouse is run by the Austrian warehouse logistics company Knapp, human workers are teaching a new generation of robot pickers how to handle differently sized and textured items. The robots employ an off-the-shelf industrial arm, a suction gripper, and a vision system. Crucially, they are also equipped with AI software from Covariant, a start-up based in California.
To train a robot, Knapp workers put unfamiliar objects in front of it and see if it can successfully adapt to them. When it fails, it can update its understanding of what it’s seeing and try different approaches. When it succeeds, it gets a reward signal, programmed by humans, to reinforce the learning. When a set of SKUs differs totally from other sets, the team reverts to supervised learning—collecting and labeling a lot of new training data, as happens with deep-learning systems.
Thanks to the Covariant Brain software, Knapp’s robot pickers are acquiring general-purpose abilities, including 3D perception, an understanding of how objects can be moved and manipulated, the capacity for real-time motion planning, and the capacity to master a task after only a few training examples (few-shot learning). These abilities enable them to perform their job—to pick items from bulk storage bins and add them to individual orders for shipping—without being told what to do. In many cases, the items have not been precategorized, which is unusual for industrial packaging systems; it means the robots are learning how to handle them in real-time. This is a critical skill to have when dealing with electronics, especially when you consider the different care required to handle a light bulb and a stove.
To succeed in a commercial environment, robots must perform to a very high standard. Previously, Knapp’s robot pickers reliably handled only about 15% of objects; the Covariant-powered robots now reliably handle about 95% of objects. And they’re faster than humans, picking about 600 objects an hour versus 450 for humans. Nevertheless, they have not caused any staff layoffs off at the Obeta facility. Human workers, instead of losing their jobs, have been retrained to understand more about robotics and computers.
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