What Changes When AI Is So Accessible That Everyone Can Use It?

H. James Wilson and Paul Daugherty

Mazin Gilbert has an ambitious goal. As vice president of advanced technologies at AT&T, Gilbert wants to make AI technologies widely available throughout the corporation, especially to those who might not have a computer science background and may not even know how to program. Call it the “democratization of AI.” To accomplish that goal, AT&T is building a user-friendly platform with point-and-click tools that will enable employees – up to one-quarter of the company’s workforce – to build their own AI applications.

AT&T and a host of other companies are trying to address a crucial issue in business: the severe shortage of AI talent. According to some estimates, only about 10,000 programmers in the world have the necessary expertise to develop advanced AI algorithms. But that’s barely a drop in the bucket for what companies will need in their future workforces. Tools like AT&T’s platform will help spread AI technologies well beyond just a limited number of “haves” and reach the “have nots” that may lack the technical knowledge and experience.

This democratization of AI will happen in two ways. First, it will enable employees across a large organization like AT&T to develop their own AI applications to make them better at their jobs. But it will also allow smaller firms to deploy some of the same AI capabilities that have heretofore been limited to large corporations. Think of how spreadsheets like Lotus 1-2-3 and Excel helped democratize data analysis, enabling even mom-and-pop shops to perform invaluable “what-if” analyses.

Some assembly required

AT&T’s in-house platform contains AI “widgets” that can be assembled together to create working applications. A marketer at AT&T might, for example, connect a widget for natural language processing together with other components to create an app for gathering and analyzing unstructured data from social media. In the future, AT&T says that it might begin offering the AI platform as a product to other companies.

Somewhat similar tools are already on the market. Consider DataRobot Inc., a Boston-based startup that has developed an automated machine learning platform that enables users to build predictive models that deploy various AI techniques. The firm has more than 100 customers in insurance, banking, and other industries. The product might be deployed, for example, to analyze a huge customer data set to predict which mortgage applicants are most likely to default. Farmers Insurance, for one, is using the DataRobot platform to uncover insights about customer behavior and to improve the design of the company’s different products. Another similar vendor is Petuum, which offers a machine learning platform with a visual interface that enables people to build AI applications quickly without any coding. The company is now working on deploying that general platform to specific industries like manufacturing and health care. And at our company, Accenture, we’ve invested in developing Accenture InsightsPlatform, which can combine and simplify the tools from the major AI platforms. We’ve seen, firsthand, how democratization increases the capabilities and speed of our professionals using AI in developing business solutions.

AI in the cloud

Meanwhile, high-tech giants Google and Microsoft have been busy adding AI to their cloud services. Initially, the tools were for relatively rudimentary tasks like image classification and voice recognition, but over time, the company will likely increase the technical sophistication of its offerings. In Google’s AutoML project, the company is building a machine learning system that will be able to develop other machine learning applications. The goal, according to Jeff Dean and Fei-Fei Li, leading engineers at Google, is to open up the use of AI from thousands of companies to millions. For its part, Microsoft has released tools to help people build deep neural networks, which can be difficult to develop and train. “We are eliminating a lot of the heavy lifting,” says Joseph Sirosh, a vice president at Microsoft. Salesforce, a leader in sales automation, has a similar goal. The company offers myEinstein, a suite of tools that enables customers to build their own chatbots and predictive marketing models without having to do any coding.

And even companies outside of the traditional high-tech industry are getting into the action. Uber, for one, is now offering Michelangelo, a platform that provides machine learning as a service. Included in the platform are the capabilities to manage data; to train, evaluate, and deploy AI predictive models; and to make and monitor predictions based on those models. According to the company, employees have been using Michelangelo in-house for more than a year now, with dozens of teams building and deploying models on the platform. One early success was Uber Eats, an application that predicts how long a takeout order will take, including the time needed to prepare the food (taking into account how busy a restaurant currently is as well as the complexity of the order) and the time required to deliver the meal (taking into account the route and traffic, among other factors). The company says it wants to make “scaling AI to meet the needs of business as easy as requesting a ride.”

Uber’s ambitious goal notwithstanding, it will take considerable advances in the field before AI can be offered to companies as a utility, similar to databases and software testing platforms. But what’s clear is that the democratization of AI is under way, and the competitive advantage could soon be shifting from those companies with advanced in-house AI expertise to those firms with the most innovative worker ideas for utilizing that technology. Rather than displacing workers, AI is actually empowering nontechnical people to use AI to fill today’s growing shortage of technical talent.