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Layoffs at Watson Health Reveal IBM's Problem with AI (ieee.org)
Last month IBM, which has staked much of its future on its flagship AI Watson, announced a major round of layoffs in the division. Now the engineers who had been let go allege that the move shows that difficulties IBM is facing in turning its AI into a profitable business. A report on IEEE Spectrum says: "IBM Watson has great AI," one engineer said, who asked to remain anonymous so he wouldn't lose his severance package. "It's like having great shoes, but not knowing how to walk -- they have to figure out how to use it." The layoffs at the end of May cut a swath through the Watson Health division. According to anonymous accounts submitted to the site Watching IBM, the cuts primarily affecting workers from three acquired companies: Phytel, Explorys, and Truven. These companies, acquired between 2015 and 2016, brought with them hefty troves of healthcare data, proprietary analytics systems to mine the data for insights, as well as their customers. The report adds: Two laid-off engineers from Phytel spoke to IEEE Spectrum in depth. They allege that IBM's leadership mismanaged their company since its acquisition, and say the problems at Phytel are emblematic of IBM's struggles to make Watson profitable. Several other Phytel employees corroborated the basic facts of their accounts. Both engineers worked for Phytel since before its 2015 acquisition, and say they were excited to become part of Big Blue. "Everyone expected that we would join IBM and be propelled by their support, that it would be the beginning of great things," says the first engineer.
The problem is that they focused on the wrong industry. Healthcare in America is bloated and inefficient at 18% of the economy, double any other country. But, while many other industries are "bottom heavy" with plenty of powerless assembly line workers or clerks whose jobs can be automated away or shipped overseas, healthcare is "top heavy" with a vast number of professionals represented by powerful organizations.
Decades ago it was obvious that many doctors could be replaced, since a nurse using a paper checklist could diagnose with the same accuracy. This is exactly what was done in many countries, with nurses or PAs handling the routine cases, while referring the difficult cases to MDs. But in America, we instead got an institutional resistance to any reform that could reduce profits. There are no incentives for doctors, or patients, or insurance companies to control costs. It is no surprise that IBM was not able to change this. What is surprising is that they thought they could.