Despite the current hype around generative AI, most organizations are making little use of artificial intelligence. That’s a big mistake, says Tom Davenport, senior adviser to Deloitte’s analytics practice. Companies, especially industry leaders, need to go all-in on AI to stay competitive.
To truly benefit from AI investments, organizations need to rethink how humans and machines interact in the work environment, says Davenport. We start with applications that change the way employees work and interact with customers. AI should help drive every business decision and every product or service offering.
That message is at the heart of Davenport’s new book. All About AI: How Smart Companies Can Succeed Big With Artificial Intelligencewith Deloitte Consulting Principal Nitin Mittal.
“There isn’t much value in tinkering with AI just to experiment here and there,” says Davenport. “We can do AI very easily on a small scale. But integrating it into the way we work means building it into our existing technology architecture.”
Instead, organizations should incorporate AI into their business processes and workflows. We need to improve the skills of our staff working with AI. And we need to make sure our AI technology can scale. We also need to do these things over time so that the world doesn’t change in a way that our efforts are counterproductive.
“There are many benefits that come from being proactive. [AI], and use it to change strategies, business models, and key business processes. This is pushing companies to leverage AI rather than the step-by-step approach that most companies have taken,” he stresses Davenport.
Learn from those who are getting big returns from their AI investments
In the book, Davenport and Mittal identify 30 organizations that have “goed all the way” to AI and have benefited greatly from this strategy.
“The most striking example is Ping in China,” Davenport explains. “The company is the 16th-largest revenue-generating company in the world, but most people don’t know much about it. has five “ecosystems” or business units. In addition to insurance, banking, healthcare and smart cities have been added. [a smart cities business]and automotive service business.
Ping has grown at an incredible rate, says Davenport. The company created this ecosystem approach to partner with other organizations and be able to capture customer data from those relationships. They use that data to create AI models that are better at predicting and classifying behavior. Then grow each business and get more data, he says.
“My favorite example is their healthcare business that created a service called Good Doctor,” continues Davenport. “During the pandemic, we were impressed in the US that people could talk to doctors and get prescriptions on Zoom. But this goes way beyond that.”
Good Doctor is an AI-based system for triage, diagnosis and treatment recommendations, Davenport explains. A real doctor makes the final diagnosis and recommends treatment for a patient, but the doctor receives recommendations from the Good Doctor System.
“The most surprising thing to me is that about 400 million people use it in China, which is more than the population of the United States. “We had a lot of fun,” says Davenport.
Other AI Leaders Transforming Markets
Another example presented in this book is Shell Oil Co., which has adopted AI in many of its business units and redesigned many processes using AI. The most dramatic example is Shell’s large-scale plant and pipeline inspections.
“It literally took up to six years for human inspectors to inspect every aspect of the plant,” says Davenport. “Shell is currently filming for six days using drones and an AI-based image analysis system. Shell has also trained more than 5,000 engineers to be citizen data scientists, in a sense, who can interpret this inspection data without a specialized data science background. increase.”
A third example is Kroger, one of the largest US grocery retailers. Kroger has a wholly owned data science subsidiary called 84 Point 51 Degrees, based in Cincinnati. The name comes from the longitude of Cincinnati.
“This subsidiary is very impressive in terms of the data science work it does for Kroger related to consumer products and the companies that sell products on Kroger,” Davenport explained. increase. “For example, every night they run a giant model that predicts sales for every unit of inventory, every store, and the entire collection of stores.”
Kroger also has the nation’s largest grocery loyalty program. The company uses data from its programs to predict which products and promotions will persuade members to visit local stores more often and buy more.
“They use loyalty programs to recommend new nutritious products and encourage customers to shop in the health food space,” says Davenport. “They also sell some of their data insights to their consumer products partners.
The value of AI for large legacy organizations
The main focus of Davenport and Mittal’s book is traditional organizations that want to be truly transformed with AI.
“It’s not about digital natives. [in those organizations] We already believe in AI and digital transformation,” said Davenport. “Still, many companies say they are doing it. But the AI deployments they show are few and far between. not.”
Davenport acknowledges that many companies may be reluctant to make large investments in this relatively early stage of “modern” AI. But the book aims to show how organizations committed to using AI are reaping significant benefits, and in some cases, transforming their markets.
To that end, these leading organizations are working broadly and deeply on AI adoption, Davenport said. There are several use cases or applications in production. They use different technologies, including machine learning. Many also use robotic process automation and linguistics-based computational chatbots.
“The days of sitting on the sidelines are over,” emphasizes Davenport. “In a way, we were trying to scare readers into saying, ‘If someone in your industry was doing this, it would be hard to keep up, but you’re not.'”
Most importantly, Davenport explains that AI is a difficult area to catch up on quickly because it requires a lot of data and a lot of skill. Organizations should start investing in AI today. There is a fairly easy and cheap way to do this.
“Many vendors are building AI capabilities into their ERP and CRM systems, so you can start there,” says Davenport. “But if you want some kind of competitive advantage with AI, you will probably have to develop some of these capabilities yourself. It means to develop