Artificial Intelligence: 5 Technology Executives Report from the Front Lines | Straight Talk


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Practical observations about the impact of AI on the enterprise.

By Straight Talk Editors

A recent National Business Research Institute (NBRI) survey of 235 business executives found that 38% of enterprises are already using AI technologies and 62% plan to use them by 2018. 26% are currently using AI technologies to automate manual, repetitive tasks, up from 15% in 2015, and 61% of enterprises with an innovation strategy are applying AI to their data to find previously missed opportunities such as process improvements or new revenue streams.

Big data has spawned the current interest and increased investment in artificial intelligence. The availability of large volumes of data—plus new algorithms and more computing power—have finally pulled AI out of its long “winter.” At Straight Talk, we like to hear directly from tech executives, in this case those who have been applying machine learning, predictive analytics, text analysis, voice processing and other AI technologies to their organizations’ data, processes, and interactions with customers.

At Schneider Electric, Prith Banerjee, executive vice president and CTO, reports on the successful marriage of machine learning with another emerging set of technologies—the Internet of Things or the IoT. Says Banerjee: “We are using machine learning to drive predictive maintenance of our large assets that are being monitored by the IoT. Instead of waiting to replace an asset after it has failed, we are using machine learning algorithms to predict when assets such as drives, PLCs, breakers, or transformers will fail. This is part of our overall Asset Performance Management (APM) application.”

Banerjee explains the benefits of the application: “Customers with high value assets like transformers and switchgear or datacenters can experience significant business loss during downtime of an asset. Typically, when an asset fails, the customer calls Schneider for service. By doing preventive maintenance we repair assets once a year or two years and therefore avoid failures and downtime.  Using machine learning and predictive analytics, we predict when an asset will fail, say in 7 days, and send a replacement part and a repair person to replace the asset before it fails, therefore preventing any downtime.”

As with other applications of artificial intelligence technologies, data—and the quantity of data—plays a major role in ensuring a significant return on investment. Banerjee: “While machine learning is a known concept, the challenges have always been in the training sets of the machine learning algorithms.  The more real data you can use to train the machine learning algorithms the better the accuracy.  The difficulty is that for our industry, failures are rare and we typically wait for a long period, some two to five years, for a failure to occur. It is hard to train machine learning algorithms with very few samples.  With time, our approach will become better as we will develop larger data sets.”

At a company serving other businesses such as Schneider Electric, the equipment now “calls home” with an alert about an emerging or immanent issue rather than the customer reporting about a failure after the fact. At a company serving consumers, a lot of the interactions with customers are now conducted through social media rather than by a phone call to a call center.

“We have 100,000 mentions a week on social media,” says Tjalling Smit, senior vice president of Digital at KLM Royal Dutch Airlines. “We handle around 15,000 customer service cases a week and we answer our customers 24/7 in 10 different languages.” As social channels proliferate, KLM makes sure it is present where its customers live online. “We were the first airline to allow customers to get their boarding passes and flight confirmation through Facebook Messenger,” says Smit.

To handle the increasing volume of interactions with customers as they add new social media channels to their portfolio, KLM is using artificial intelligence. “We believe that we should be where our customers are,” says Smit. “They spend hours daily on their mobile phones but not necessarily on our website. Many travel with us once or twice a year so the likelihood that they have downloaded our app is limited.  They spend a lot of time on messaging platforms—WeChat in China, for instance. So that’s where we want to provide our service and we need to use artificial intelligence to facilitate the dialog with our customers. AI is a facilitator for the continued human interaction between our brand and our customers. The increase in interactions with customers over online channels has not been compensated by a decrease in phone interactions. Social media lowered the barrier for customers to interact with us. We don’t replace human agents with technology—we use technology to facilitate the dialog with our customers.”

In the future, says Smit, “we are going to implement AI also in the call center and I imagine we could use it also in back-office type functions. I’m not scared by AI. Like digital in general, like technology in general, it improves the quality of airlines, making them more personal and relevant with their customers. We will use AI to create a personalized and relevant experience for an individual customer. But human interaction will always be key.”

In addition to applying artificial intelligence and machine learning technologies and algorithms to improving relations with customers, more and more enterprises use them internally to improve decision making. At Maersk, the world’s largest container shipping company, AI “captures the knowledge people acquire on the job and enables other employees, who do not have a similar experience, to have a head start in making a decision instead of starting from zero,” says Ibrahim Gokcen, Head of Data Science & Analytics. “We want to make AI part of our digital journey,” says Gokcen. “Strong technology platforms with AI capabilities help the data science and analytics people focus on the business logic, the algorithms, churning models very quickly. These platforms give a head start not just to employees making decisions but also to our data scientists.”

Adds Gokcen: “Throughout their careers, employees learn how to make the connection between data points and interfaces to make a decision for the company. You cannot scale that because people move out of the company among other reasons. Other employees don’t have the experience of the context of the decision. Our hypothesis is that we can use AI technology to empower the employee to make those decisions even though they don’t have the experience of years of working in these specific operations. We use AI technology to make the best possible decision at a specific point in time using all the data that’s available to the employee."

Gokcen also reflects on the past and future of artificial intelligence: “Today, with deep learning, we really solved very complex problems. AI, as a domain, has become very popular and hot again. When I did my PhD, AI was a bad word, we tried it and it failed. We are now looking at a much broader definition of AI. In the industrial revolution, certain jobs ceased to exist but other jobs were created. The same thing is happening in the Internet revolution. We will see impact on certain roles but also the creation of new high-impact jobs and the creation of a lot more value from existing work.”

Similarly, Steven Hatch, manager of enterprise logging at Cox Automotive, a subsidiary of Atlanta-based Cox Enterprises, has first-hand experience with using AI to improve decision making. With multiple brands such as Manheim, Autotrader and Kelley Blue Book, Cox Automotive is changing the car buying and selling business, helping people buy and sell cars from their homes, offices and mobile devices.

“Cox Automotive represents the overall lifecycle of a car,” says Hatch. “Whether it’s a dealer or a consumer there are specific functions within that lifecycle that generate a lot of data, up to 2 terabytes a day. Machine learning can be applied to this data to understand better how the marketing campaign is impacting web traffic or how dealers leverage parts and services. We have all of this digital exhaust over the lifecycle of a car and machine learning can draw a picture and analyze events, trends and activities we didn’t even have thought of before. That’s the beauty of machine learning.”

Business users at Cox Automotive, says Hatch, “can improve their business decisions because they now have insights they never imagined before, or have data to support going forward with a specific initiative.”

The fundamental benefit for the business is in the act of the aggregation of all the massive amounts of data machines generate, something that was not possible before the advent of cloud computing. “Six years ago, this would have been a monumental effort to centralize all of this data because we didn’t have the luxury of having compute and storage in the cloud the way we have it now,” says Hatch. Now, all of this valuable data can be accessed and analyzed by the people of Cox Automotive who could use it to make better decisions. Concludes Hatch: “Big data is what it’s all about. Big data is not the data that is rigged to the hard drive. Big data becomes big when it can be shared across the business to make the right decisions.”

Bob Rogers, Chief Data Scientist at Intel, also sees the value of AI in extracting value from big data. It helps “surface the information from a large volume of data so we can do the next level of inference ourselves,” says Rogers. “AI superimposes a layer that identifies the context. These are capabilities that are most interesting when they are used to amplify human capabilities. There are things that we are good at cognitively but we cannot do at scale. AI is a group of technologies that will increasingly be used to augment human capabilities, and make us better at the things we do best. What’s more, AI isn’t a story set in the distant future. It’s here today, and improving our lives in countless ways,” concludes Rogers.

Artificial intelligence is here today, transforming machine learning into a valuable tool for specific business activities, improving customer relations and enhancing the quality of decision-making, in many enterprises.