At the core of the issue, according to IEEE Spectrum, is an inability to sufficiently monetize Watson, IBM’s much-publicized artificial intelligence (A.I.) platform. “IBM Watson has great AI,” one anonymous engineer told the publication. “It’s like having great shoes, but not knowing how to walk—they have to figure out how to use it.” According to other (also anonymous) engineers, smaller companies with A.I. tools have managed to snipe at the edges of IBM’s business, taking away clients who would otherwise have deployed Watson.
For years, IBM attempted to make Watson a moneymaker in oncology, the study of cancer. It entered into agreements with prestigious medical institutions such as the Memorial Sloan-Kettering Cancer Center in New York City, with the understanding that human researchers would help the software to better process and interpret clinical data.
IBM also attempted to democratize its Watson work, releasing what it called Watson-as-a-Service, designed for both data scientists and those with considerably less experience in complicated analytics. “There’s a real nuts-and-bolts data science approach,” Ryan Anderson, Architect in Residence (Watson West) & Cognitive Prototypes for IBM, told Dice at the time. “But there are people building with cognitive who are more on the marketing and brand side who don’t care what’s going on under the covers, and they just want the API to return the information.”
However, signs emerged that not every client was happy with Watson. In February 2017, MD Anderson Cancer Center in Texas placed its Watson collaboration on hold; an audit cited missed deadlines, integration issues, and escalating costs, according to Forbes. (IBM countered that its system’s results were accurate 90 percent of the time, and MD Anderson issued a statement that the shutdown wasn’t reflective of the “scientific basis or functional capabilities of the system.”)
Although other institutions continue to trial Watson, Big Blue now faces substantial competition on the A.I. front from other tech giants, including Google and Microsoft. These companies aren’t just using machine learning and deep learning to improve their products; they’re also issuing tools and platforms that allow customers to build bots and A.I.-enhanced apps.
It’s clear from its roadmap over the past few years that IBM envisions Watson as a major revenue driver. Why else would it attempt to bake the technology into so many offerings? But artificial intelligence remains relatively nascent, and the potential revenue streams from it haven’t fully matured; and that, combined with fierce competition, means the technology might not replace other lines of business as a profit driver anytime soon. With its big bet on Watson, is IBM betting the farm?