AI is relatively easy, until the enterprise gets involved

As AI matures within enterprises, the emphasis shifts from labor replacement to enterprise-building.
Written by Joe McKendrick, Contributing Writer

Artificial intelligence, while sophisticated, is a relatively straightforward technology. Through inference, applications or algorithms sift through data points to spot patterns and arrive at high-percentage probability decisions. It's working great in chatbots and decision engines, but applying it on an enterprise scale requires more expansive thinking -- and essentially what are sales skills..

That's the conclusion of a recent survey of 3,076 executives, published by MIT Technology Review in conjunction with Boston Consulting Group. "Many companies have discovered, often to their surprise, that it is easy to apply AI and get quick results," the team of researchers, led by Sam Ransbotham of Boston College, point out. "What is not so easy is building a system of AI applications along with associated data pipelines that interact and are reliable. Pioneers overwhelmingly see the need for an AI strategy: 85 percent agree they have an urgent need for an AI strategy, and 90 percent say they have a strategy in place already."

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Investments in AI are rising, the MIT-BCG survey also shows. Almost all of the most advanced AI companies, 88 percent, report their AI investments are up over the past year. Even among more novice companies, 62 percent report increasing their AI spending. A significant majority of AI leaders reported investing more in the past year than in prior years in AI talent (81 percent), AI technology (86 percent), the data required to train AI algorithms (79 percent), and the processes required to train the algorithms (80 percent).

The companies making progress with AI recognize that it really shows its value as a revenue-generating environment -- not as a cost-cutting technology. The early use cases demonstrate AI as a way to replace manual, repeatable tasks that required a lot of human time - such as a chatbot handling basic customer-service calls. "Easily documented cost savings are a classic way of garnering support for further investment," the researchers point out. "But the finding here is that all but the most passive organizations anticipate AI will pay off most on the revenue-generating side.

More sophisticated organizations expect more in this direction, as 72 percent of Pioneers say AI will deliver mainly revenue increases in the next five years, while only 28 percent of Pioneers expect mainly cost savings. For the Investigators group, the corresponding numbers are 59 percent and 41 percent. In the near future, AI initiatives will focus on generating revenues, not cutting costs."

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Tellingly, a number of respondents (28 percent) say AI solutions have already led to business model change in their organization. The majority of all organizations (58 percent) foresee modifications of their business models due to AI within five years. "These results suggest that organizations don't expect AI to merely help improve current business operations; they widely expect AI adoption to change business models," the resport states. "What's more, nine out of 10 respondents believe AI will create new value for their business in the next five years."

What does scaling require? "Many efforts simultaneously: creating a strategic vision, taking stock of current capabilities, building AI-supporting processes and platforms, instilling AI understanding into the business, and cultivating AI-related activities," Ransbotham and his co-authors report. "It's a complex undertaking. Most executives have yet to consider, at a deep level, how to scale AI in their business."

They quote Inderpal Bhandari, global chief data officer at IBM: "People don't really understand what enterprise AI is. They do have a good sense of it in the consumer context, and they also had a good sense of AI in the context of point solutions like facial recognition and stuff like that. But they don't really understand from an enterprise context exactly what that could look like."

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Ransbotham and his co-authors recommend that "efforts to scale AI systems and initiatives are more likely to succeed at organizations that are full of people who understand the promise of AI and know something about what effective AI deployments require."

Plus, to reiterate, it's about the business and serving the customer. AI needs to show how it can step up and make a difference.

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