Contact: Brenon Daly
Coming off a 60% increase in the number of machine-learning-related transactions last year, the trend of adding ‘smarts’ to technology looks likely to drive even more deals in 2017. Senior investment bankers picked machine learning as the top M&A theme for the coming year in last month’s 451 Research Tech Banking Outlook Survey, with more than eight out of 10 respondents (82%) predicting an uptick in activity. That outlook for machine learning outpaced the view in all individual technology markets as well as the other four cross-market themes of the Internet of Things, big data, cloud computing and converged IT.
One of the reasons why machine learning (and the related – but broader – theme of artificial intelligence) is expected to figure into so many transactions is that the technology is broadly applicable. Basically, any company that is looking to make its products more efficient – which, in turn, makes the users of those products more efficient – could be viewed as a potential acquirer of machine-learning technology. (To be clear, our view of machine learning is that the technology is a subset of artificial intelligence, focused on using algorithms that learn and improve without being explicitly programmed to do so. For a more in-depth look at the AI/ML market, see our recent sector overview led by my colleague Nick Patience.)
Certainly, machine learning appears to be an almost foundational technology when we consider the broad pool of buyers. Just in the past year, acquirers as diverse as Ford Motor, Salesforce, Intel and GE Digital have all announced machine-learning-related transactions, according to the M&A KnowledgeBase. Those deals have been part of a surge of M&A that saw buyers announce as many machine-learning-themed purchases in 2016 as they did from 2002-14, according to the M&A KnowledgeBase.
