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Eleven years after IBM Watson wowed the tech world by beating two of Jeopardy’s biggest champions, the days of tackling “moonshot” AI goals outside the research lab are over, IBM chairman and CEO Arvind Krishna told reporters yesterday, ahead of the annual IBM Think conference in Boston. 

“I don’t want us to work only on ‘moonshots,’” he said. “That should be research you work with in the lab.” Instead, he said IBM’s focus will be firmly on AI projects that provide near-term value for clients, adding that he believes generalized artificial intelligence is “still a long time away.” 

“Some people believe it could be as early as 2030, but the vast majority put it out in the 2050 to 2075 range,” he said. “Personally, having grown up as a scientist, I react to anything that is 25 years or further away with the conclusion that we have no idea how this is going to happen.” 

Focus on enterprise AI

That isn’t stopping the company from plowing ahead with specific AI projects that, while clearly not “moonshots,” are examples of IBM’s focus on enterprise applied AI.

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One example is a partnership with McDonald’s to lower costs and boost efficiency by automating customer orders in the face of a vast number of possible variables. “It’s not general question and answer, you have to understand how customers might modify an order – we were trying it out this morning as pretend customers. You order a quarter pounder, then you take the onions off; you might add extra tomato. You order a drink, it asks you medium or large or small,” he said.

Krishna offered a second example, which he called Watson AIops, around how IBM is applying AI to information technology to boost productivity by being predictive rather than reactive. With the current demographics of labor and skills shortages, these examples become even more relevant, he added.

‘Moonshot’ generalized AI goals on back burner

But while IBM is seeing success with narrow, quick-win AI use cases, the company’s bigger, headline-making AI goals will no longer be on the front burner.

For example, Krishna also alluded to the demise of Watson Health – a business that was supposed to signal the future of healthcare but whose data and analytics assets were sold in January – by saying that while he still believed some of the company’s previous healthcare AI goals would eventually happen, “they might take a half-decade or a decade more to come to fruition, given how hard these problems are and the life and death implications.” 

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