When it comes to digital transformation, humans—believe it or not—play an integral role. In fact, companies that make strong use of the combined human/machine workforce have a far greater chance of success in digital transformation. Accenture calls these combined people/bot workspaces “future systems”—systems that seamlessly integrate humans and robots to create business goals that are limitless, agile, and “radically human.” I consider the companies that harness the power of humans and machines will be the ultimate winners of the future of work.
The good news: these systems are already happening. The bad news: only 8% of those surveyed by Accenture seem to be using them right now, despite the fact that revenue growth in future system companies is 50% higher than those in average or laggard adoptees.
If your company has not already implemented some type of combined human/machine workforce, it will be hard—in fact, nearly impossible—to catch up. That’s because the power of AI and machine learning amplifies the skills, insights, and capabilities of leading users to the nth degree.
In our 2019 book, Human/Machine, that I wrote with Olivier Blanchard we noted the rise of human machine partnerships and the three archetypes of human machine technologies being:
- Big Brother: A well understood concept dating back to Orwell’s book “1984,” Big Brother can be defined by technologies used to surveil.
- Big Mother: Well intended technologies that can quickly become overbearing and intrusive.
- Big Butler: Technologies that are designed to enhance productivity, minimize intrusion and protect privacy—The most ideal type of technology augmentation for humans.
It’s essential that all leaders, regardless of company size or location on the digital transformation spectrum consider the ways that technology can augment workers as well as deliver customer experiences—The “Big Butler” of human machine partnerships noted above. And while we have a long way to go, there are opportunities to start learning now from companies that are at the forefront of the human-machine pack. The following are just a few examples.
Human and Machine Partnership: CVS
One of the most exciting examples of humans and machines working together to improve customer experience is CVS. The company is on a mission to connect the “physical and digital experience” in healthcare, making the act of staying healthy easier for every customer. The company recently purchased Aetna, and the two are working to create consumer-focused and tech-enabled healthcare powered by AI, virtual care, and connected devices—all seamlessly interacting with brick-and-mortar care hubs.
For instance, the company’s health app can analyze a customer’s health data, instantly share it with his doctor, and alert him when the doctor prescribes an increase in insulin. Another example: a mom at home can use the company’s AI symptom checker to diagnose the cause of fever in her sick child. For CVS, it’s all about meeting customers—and patients—where they are, which is what technology and innovation are all about. For a large pharmacy chain, that’s pretty impressive.
Human and Machine Partnership: Airbnb
There’s a reason Airbnb disrupted the hotel industry. The company has invested a ton in machine learning to create a personalized recommendation process—recommending winning price points to hosts and right-fit homes for guests. By investing in this type of technology, and equipping hosts with the data they find, the company has been able to increase its conversion rate—the number of people who actually book after viewing properties. Airbnb also uses machine learning to analyze how long it takes specific users to decide where to stay and optimizes their search for quicker decision-making. Yes, companies like Marriott are trying to use tech to catch up with Airbnb. In my opinion, Airbnb has created such a strong “learning” app for meeting travelers’ needs that coming from behind will be difficult.
Human and Machine Partnership: McDonald’s
Who knew McDonald’s had a presence in Silicon Valley? It’s not just serving up burgers anymore. Its tech arm, McD Tech Labs, is working hard on numerous tech projects to improve employee and customer experience. One recent acquisition involved a personalization and decision logic technology, which has been implemented in more than 8,000 drive-thru windows in the United States and Australia. The tech will vary its outdoor digital menu to show food based on time of day, restaurant traffic, trending menu items, etc. It will also instantly display additional items based on their current selections.
Still, mega companies aren’t the only winners in the human/bot revolution. One large organic grocery store, for instance, used voice recognition and AI to help customers shop for their specific diets, for instance highlighting vegan, paleo, or keto options on the shelf to make them easier to find. That’s radically helpful and radically human. Another example: a small firm focusing on delinquent payment collection used AI to send batch texts to their lists, rather than spending time on the phone. The technology reduced the number of hours spent on boring work, which increases employee satisfaction.
It’s not just about big technologies either. Smaller technologies like collaboration tools can help employees work more efficiently. Sensors in machinery can help companies operate more effectively. Even something as small as allowing employees to use their own device with work software can make a difference in digital transformation.
Indeed, it’s important to note that these technologies aren’t just improving the companies’ bottom line—they’re improving the experience for customers and employees alike. Yes, McDonald’s may increase its sales volume by automatically recommending fries with every order, but it could also limit employee stress by promoting easy-to-build items at peak dining times. The power of the human/tech partnership is that it helps both ways.
For anyone worried that humans will soon be pushed out of human + tech equation, rest assured: humans will still have a place in the market moving forward, refining algorithms, using human judgment regarding machine learning decisions, and “teaching” the robot what it needs to grow. The difference is humans will be able to get the products and services they want and need more quickly and easily. That sounds like a winning equation to me.