By Thor Olavsrud
July 22, 2016
IBM Watson is a great example, Frankel says.
"Two years ago, three years ago, testing Watson was a multi-million dollar, potentially multi-year initiative for most organizations," he explains. "There are only so many companies that can take on that kind of commitment. Watson has followed a wide-sweeping trend to make web services available, make technologies available via a series of APIs. That makes it much easier to test. If organizations can test it and get value out of it, that leads to much greater adoption downstream."
Of the AI technologies currently in use, predictive analytics dominates. The survey found 58 percent of respondents are using a combination of data mining, statistics, modeling and machine learning to analyze current data and make predictions about the future.
"Certainly one of the key headlines from our research is that predictive analytics is really moving into the enterprise very, very quickly," Frankel says. "Companies are starting to see the real value in their data. They're making decisions against their data instead of just viewing their data."
Narrative Science notes that organizations are likely gravitating toward predictive analytics because of the tremendous potential it offers across many industries, whether it's preventing costly hospital re-admissions in the healthcare sector or reducing unplanned downtime and allowing for more efficient supply chain management in the manufacturing space.
Research firm Gartner predicts 40 percent of the new investment made by enterprises will be in predictive analytics by 2020.
The study found that companies that place a priority on innovation tend to generate the most value from their technology investments. Fifty-four percent of respondents said their organization has an innovation strategy, and 62 percent said their organization has a dedicated innovation budget. Of those organizations with an innovation strategy, 63 percent believe they are skilled at using big data to solve business problems. Only 13 percent of respondents from organizations without an innovation strategy felt the same way.
Of the companies that have an innovation strategy, 61 percent use AI to identify opportunities in data that would otherwise be missed. Only 22 percent of respondents without a strategy said the same.
Where are the data scientists hiding?
Unsurprisingly, the survey also found the data science talent shortage remains top of mind: 59 percent of respondents said the shortage of data science talent was a primary barrier to generating value from their data. Of the survey respondents who said they have deployed big data technologies, about roughly 50 percent said their organization is skilled at using big data to solve business problems.
Of the group that said they were skilled, 95 percent use AI technologies. That's up from 59 percent last year, which Frankel says is an indication that companies are turning to intelligent systems to augment their data science capabilities in response to the shortage.