The Semantic Web may have failed, but higher intelligence is coming to applications anyway, in another form: Cognition-as-a-Service. And this may just be the next evolution of the operating system. Cognitive computing services might include everything from predictive maintenance to customer experience and medical diagnoses. But there’s a skills gap.
According to a recent report from Allied Market Research, the cognitive computing market is expected to generate revenue of $13.7 billion by 2020. On top of that, IBM CEO Ginni Rometty recently told CNBC that Watson would reach more than 1 billion consumers by the end of 2017. The cognitive computing market is growing in size and complexity—and quickly.
IBM defines cognitive computing as a set of “augmented intelligence” capabilities that include machine learning, reasoning and decision technology, language, speech and vision, human-interface tech, distributed and high-performance computing, and new computing architectures and devices. When combined, the capabilities are meant to solve practical problems and foster new discoveries.
To help understand the marketplace, and both its many opportunities and hurdles that must still be crossed, IBM has released “The Cognitive Advantage Report,” a comprehensive guide including insights from early adopters. The report aims to showcase how future-looking organizations are already driving business value from cognitive computing and perhaps help others do the same for their own businesses.
IBM surveyed 600 key players at organizations around the world and found that advanced users, which are those using two or more cognitive technologies for more than a year, made up 22 percent of respondents. Beginners, using the technology for less than a year or just one technology for more than a year, are a majority—54 percent. Planners, who want to adopt cognitive technology within two years, are the remaining 24 percent.
Generally cognitive technology excels in use cases where there is simply too much data for humans to sort, where making automated and fast decisions on a mass of data is critical to the business, and where the rules of the game are well-defined. Machine learning, for instance, is being used to detect and deal with fraud in a more proactive manner, reduce customer churn, or boost sales through personalization.
