Machine learning and other variations of artificial intelligence (AI) are expected to proliferate in the enterprise in 2017. The majority of IT players, including today’s leading technology companies, have invested in the space and plan to increase efforts for the foreseeable future, according to analysts who cover the market.
However, while machine learning has generated tremendous interest throughout the enterprise, a wide gap still exists between research and beneficial use cases in the real world. And only a small number of companies have the resources to actually drive AI innovation and deliver it to the masses, sources say.
During last month’s Code Enterprise conference, LinkedIn CEO Jeff Weiner said AI was one of leading factors in the company’s decision to be acquired by Microsoft. A limited pool of AI experts means relatively few companies can make machine learning work at scale, he said at the time.
Box CEO Aaron Levie says machine learning and AI are pillars of his cloud-storage company’s strategy. He predicts AI will have a momentous year in the enterprise in 2017. "We are going to see a dramatic change in how enterprise software is designed and how enterprise software takes advantage of all the data that’s in our platforms to produce way better outcomes for customers," he says.
Machine learning has already become "table stakes for data preparation and other tools related to managing curation of data," but the technology will continue to grow and find its way into more applications and services in 2017, according to an Ovum report on tech trends in 2017. The research firm predicts machine learning is still more likely to show up in large-scale services than custom-developed apps, because few organizations outside of the Global 2000 have data scientists with the appropriate skills on their staffs.
Machine learning took the place of big data as the "shiny new thing" in technology, and it will be the "biggest disruptor for big data analytics in 2017," according to Ovum. Enterprises are also under pressure to make data science a team sport, because the most rigorous models and hypotheses will require outside collaboration to reach greater potential.
Apple, Facebook, Google and Microsoft all open-source or share their latest research in AI to advance developments in the space. These moves from such notable organizations also meet the collective interests of scientists and researchers who prefer to share their findings with the larger community, instead of limiting access to a select group.