Fewer technologies are hotter than artificial intelligence (AI) and machine learning (ML), which mimic the behavior of the human mind to help companies improve business operations. And for companies embarking on digital transformations, AI and ML are being viewed as cornerstone technologies for wooing customers with new services.
Aggressive marketing has triggered significant hype around these emerging technologies. Restaurants, retailers and airlines are deploying chatbots that approximate human-like conversations with customers. IBM has propped up its core AI technology, Watson, as having the potential to cure cancer (and apparently falling well short, as reports suggest). John Deere just shelled out $305 million for Blue River Technology, which makes crop-spraying equipment that leverages ML. Even Uber, weathering several legal challenges, has made time to reveal Michelangelo, an internal ML-as-a-service platform, that "democratizes machine learning and makes scaling AI to meet the needs of business as easy as requesting a ride."
Yet as is often the case, technology hype exceeds reality. The gap between AI/ML ambition and execution is large at most companies, according to new data from MIT Sloan Management Review and The Boston Consulting Group, which surveyed more than 3,000 executives, managers, and analysts worldwide. Only about 20 percent of executives surveyed say their companies have incorporated some form of AI. Moreover, fewer than 39 percent have an AI strategy in place. Yet 85 percent of those executives are convinced AI will allow their companies to obtain or sustain a competitive advantage.
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Recognizing the opportunity to move the needle for their businesses, some CIOs are experimenting with, building and even patenting new AI and ML technologies. These IT leaders shared their ML use cases with CIO.com.
Banking on better customer insights
Like many large banks, U.S. Bank has collected a wealth of customer data. And like most banks, U.S. Bank has struggled to derive actionable insights from this data. Bill Hoffman, chief analytics officer of U.S. Bank, is working to change that. For the past several months, he has been using Salesforce.com’s Einstein AI/ML technology to increase personalization across the bank’s small business, wholesale, commercial wealth and commercial banking units.
For example, if a customer searched on U.S. Bank’s website for information about mortgage loans, a customer service agent can follow up with that customer the next time they visit a branch. It also helps U.S. Bank find patterns humans might not see. For example, the software can recommend that agents call a prospective client in a particular industry on Thursday between 10 a.m. and 12 p.m. because they are more likely to pick up the phone. Einstein can also put a calendar invite into the agent’s calendar to remind them to call the candidate the following Thursday.
Such capabilities get to the core of what many financial services organizations are trying to do; cultivate a 360-degree view of customers to recommend relevant services in the moment. “We are moving from a world that was describing what happened or what is happening to a world that is more about what will or should happen,” Hoffman says. “The core value is staying a step ahead, anticipating our customer needs and the channel they want to interact with us.”
Key advice: Take a test-and-learn approach to AI and ML and be patient. But also be ready to scale things that are working. “Always have the customer at the center,” Hoffman says. “Ask: How will this benefit the customer?
ML removes ‘toil,’ making work more productive
Ed McLaughlin, president of operations and technology at Mastercard,says ML “pervades everything that we do.” Mastercard is using ML to automate what he calls “toil,” or repetitive and manual tasks, freeing up humans to perform work that adds productivity and value. “It's clear we've reached a state of the art where there is a clear investment case to automate workplace tasks,” McLaughlin says.
Mastercard is also using ML tools to augment change management throughout its product and service ecosystem. For example, ML tools help determine which changes are the most risk-free and which require additional scrutiny. Finally, Mastercard is using ML to detect anomalies in its system that suggest hackers are trying to gain access. McLaughlin also put a “safety net” in the network; when it finds suspicious behavior it trips circuit breakers that protect the network. “We have fraud-scoring systems constantly looking at transactions to update it and score the next transaction that's going in,” he says.
Key advice: As far as McLaughlin is concerned, AI/ML are just tools in the payment processor’s broad toolkit. Despite all of the shiny new tools on the market, he says CIOs shouldn’t rely on them to magically fix business problems.
AI as a product and business enabler
At software maker Adobe Systems, CIO Cynthia Stoddard is reimagining her department with a “data-driven operating model,” relying on Hadoop-based analytics to gain insights to both run IT and the business better. As part of the data-driven strategy, Stoddard says she is experimenting with ML to help analyze tickets in ServiceNow help-desk software to look for trends in system failures. The thinking goes, if the system sees events that suggest an outage could occur, the system can be proactive to eliminate or mitigate those events before they trigger failures.
Identifying patterns in IT service failures, she says, will also empower Adobe to create some “self-healing” capabilities to absorb work that her IT staff currently does. She is also looking into chatbot technology to field employees’ IT support requests. Adobe’s commercial business has also embraced AI. The company last November introduced Sensei, a layer of AI technology it is applying to its product for creating and publishing documents, and for analyzing and tracking web and mobile application performance.
Key advice: Using ML to identify patterns is the key to creating self-healing capabilities. “If you know how you fixed it you can put self-healing component in there and take the human element out of the equation,” Stoddard says.
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