Not so long ago, back in 2011, IBM’s artificial intelligence technology (later packaged and sold as Watson) triumphed in the game of Jeopardy. Watson played against the two most successful contestants ever to appear on the show. This victory reflected the result of an enormous amount of work done by IBM and others to mine human language for the semantic meaning of words, and allow a machine to answer Jeopardy questions that would have been impossible for any computer just a few years earlier.
Having achieved this milestone, IBM set out to capitalize on its technology advantage. It created a new business division for “cognitive computing” and invested heavily to apply Watson to big, important problems that could actually pay money. IBM soon settled on health care as the best place to plant its flag, and not long afterwards, some exciting partnerships were announced with leading health care institutions around the country.
There was a lot of unmet need in health care. Something like 45% of medical practice was not based on the best available evidence, and so much new information is being generated each year that it is impossible for doctors to keep up with all the new information. And the market size was huge, amounting to more than 10% of world GDP.
So far, so good. IBM had a clear lead in the market, IBM invested significantly to build upon its edge, and targeted an extremely large market where its technology could really make a difference.
But with the passage of more time, it must be said that IBM Watson has not delivered the results that IBM expected. One particular moment was the decision of MD Anderson’s Cancer Center to withdraw from its partnership with IBM in 2017. An internal audit by the University of Texas found that the university had spent over $62 million dollars (not counting internal staff time) and did not meet its goals.[i] Other health partners soon followed.
Two weeks ago, IBM announced the departure of its CEO, Ginny Rometty, and the promotion of the head of its cloud business, Arvind Krishna, to be its new CEO. While many factors were no doubt at work here, the shift in IBM’s focus from cognitive computing to cloud computing is certainly a part of this change.
Why did IBM come up short with its AI technology? There are technical reasons, owing to the rapid and unexpected progress of other forms of AI, such as deep learning. But another likely reason has to do with how IBM took its then-leading technology to market.
IBM commercialized Watson in health care with a vertically integrated strategy. All of the activities from the laboratory to the customer were delivered by IBM itself. There were no third party system integrators. There were no APIs for others to use to link to and build upon Watson. There was no software development kit, or open source reference designs to help people explore Watson’s abilities, and figure out whether and how they might solve other problems. Even customers were only able to access a black box, putting queries in and receiving the result, with no way to comprehend how the result was developed.
Being vertically integrated gave IBM complete end-to-end control over Watson. But it condemned Watson to being applied in only a few areas. IBM essentially had to guess where this powerful technology could best be applied. Even within health care, some likely areas for Watson like radiology were not pursued in its early years. Because of the limited number of areas IBM was able to explore for using Watson, we will never know whether there were other areas where Watson might have performed beautifully. Note that, in the cloud computing business, IBM is partnered with many third party organizations, and provides lots of entry points for people to build upon its technologies.
There is a lesson here for any company seeking to commercialize a broad, powerful technology like AI. AI is a “general purpose technology” (GPT) that can be used in a myriad of ways. No single company can explore all of these possible ways on their own, so it is good business practice to open up. Establish platforms and partnerships that allow third parties to co-create and build upon your own technology. This more open approach will result in many more experiments being done (some by you, more by your ecosystem partners) in the market much faster. And the actual best uses of the GPT will be much more likely to emerge. The ascent of Arvind Krishna suggests that IBM has learned this lesson, and is more likely to succeed in the next generation of cloud computing as a result.
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