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AI vs. BI: How do you sell artificial intelligence to the business?

Is artificial intelligence tech quickly becoming enterprise tech? Vendors are betting on it.

Last week at IBM World of Watson, IBM CEO Ginni Rometty laid out her vision for the technology: Namely, that Watson will reach a billion users by the end of 2017, and that the technology will underpin every major personal and corporate decision. Last month, Salesforce rolled out Salesforce Einstein, an AI system that analyzes data to identify trends in marketing and sales.

The list goes on, with Microsoft, Google, Apple and Facebook jumping on the AI train. IT consultancy Gartner recently released its top ten strategic technology trends for 2017. At the top of the list? Advanced machine learning and AI.

That’s why a question from a Welch’s Food employee at EmTech, an emerging technology conference hosted by the MIT Technology Review in Cambridge, Mass., brought things back to reality. At the end of a session on “Applied AI: Intelligent Machines in the Enterprise,” he asked the panel of experts: “How do you take this talk to business executives and tell them this is not BI?”

The big difference between AI vs. BI is what questions they’re used to answer, said Sameer Anand, a consultant with A.T. Kearney Inc.’s operations practice. BI is often used to answer what happened; AI, on the other hand, can be used to answer what will happen next. “What we see is companies are thinking about predictive and foresight, and that’s where I think this is going to be more AI and not BI,” he said.

Still, the audience member’s question suggests a disconnect between the pie-in-the-sky AI sellers and boots-on-the-ground tech buyers. How do employees like help bridge the gap? The panel had a few ideas.

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Houman Motaharian, chief revenue officer at LendingPoint, a startup focused on consumer lending, stressed the importance of getting executive level  support for AI — and even better, a CXO leading the charge. When he was at AmEx eight years ago, for example, it was the chief risk officer who advocated for bringing in machine learning and Hadoop to the company. The CRO vowed to start small by selecting appropriate use cases and building proofs of concepts. “Today, I know for a fact, most decisions at American Express are made with machine learning,” he said, but cautioned that even with the CRO firepower, AI adoption took time. “It didn’t happen overnight. It took six or seven years.”

Vijay Sharma, managing director with Deloitte’s strategy and innovation group, agreed. For companies that still need to win an executive over, Sharma provided similar advice: Starting small can help build a strong case and earn the trust of an executive sponsor. That, he said, is the “key to expanding” AI efforts in the enterprise.

Use cases as templates, not prescriptions

Like the other panelists, A.T Kearney’s Anand agreed that finding an executive sponsor to champion an AI agenda is key. Next, he said, “you try to bring in external use cases: Here are examples of what others have done.”

The AI use cases are for idea generation and not necessarily duplication. Instead, step back and consider how data, analytics and technology are used to solve a problem, he said. And, he said, “Talk to people. Ask what their journey has been.”

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Nicole Laskowski's response does a good job of identifying differentiating aspects of these four disciplines. To some degree they all share a use of statistical methods and principles from data science. More importantly, artificial intelligence and machine learning can be used for data mining and business intelligence.
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