Samsung's Flip 2 enhances a radical interactive screen designed to merge the best of paper flipcharts and interactive whiteboards.
Driven by a veritable tsunami of data and bolstered by powerful analytics tools, the artificial intelligence solution is on the rise in the enterprise space. AI innovations are driving new efficiencies and a wealth of other positive metrics across a range of industries.
In its latest survey of 235 business executives, Narrative Science took a deep look at the adoption of artificial intelligence in the enterprise. The resulting report details the emergence of AI as a core business strategy, with 38 percent of organizations saying they already use AI technologies in the workplace, and 62 percent likely to be using the technology by 2018.
The study found AI manifesting in a number of different forms in the enterprise, including deep learning, natural language generation and recommendation engines. The leading use of artificial intelligence solution tools lay in the area of predictive analytics. This specialized area of analysis uses data mining, statistics, modeling, machine learning and other AI tools to make predictions about the future. More than half of respondents — 58 percent — described predictive technologies as their most commonly used artificial intelligence solution, and 38 percent said the ability to predict activity related to machines, customers or business health is the most important benefit an AI-powered solution should provide.
This report is in line with other researchers’ findings — Gartner, for instance, anticipates that by 2020 predictive technologies will attract 40 percent of the new investments made by enterprises. Narrative Science also points to Dresner Advisory Services’ annual Advanced and Predictive Analytics Market Study, which found that 74 percent of respondents believe that predictive analytics is either important, very important or critical to their mission.
Growing Appeal of AI Solutions Across Industries
The report credits the growing availability of data as a chief driver in the predictive analytics boom, and goes on to posit that the strong interest in the field may also be rooted in the broad appeal of predictive analytics across multiple industries. “In healthcare, it is being used to both anticipate and prevent costly and often unnecessary hospital readmissions. In manufacturing, it’s allowing for much more efficient supply chain management by anticipating and adjusting for potential delays resulting from such factors as inclement weather, strikes or even geopolitical events,” the authors note.
This same observation holds true across many artificial intelligence solution sets. For example, at Children’s Hospital Los Angeles, data scientist David Ledbetter and his research team are utilizing deep learning, or the ability to bring meaning to vast quantities of information (such as the exabytes of data being produced in healthcare), by poring over a decade’s worth of health records to better determine drug treatments for children.
Machine learning algorithms are another AI application that rated high among respondents, and which can be seen in a broad range of verticals. Uber uses algorithms to set pricing according to demand, while Amazon and Netflix employ the technology to spark consumer interest. Such implementations across a range of industries have helped prove the value of algorithms in generating unprecedented levels of intelligence within the enterprise. These algorithms can also be used in fitness tracking wearables like Samsung’s Gear smartwatches, which collects data to provide users with real-time coaching as they’re working out.
Barriers to AI Adoption
While interest in AI is strong, enterprises may face hurdles on the way to adoption, such as a skills shortage. Narrative Science predicts global demand for data scientists will exceed supply by more than 50 percent by 2018. “Without individuals trained at analyzing complex data to relay the high-level insights for quick decision-making, companies can easily miss out on a valuable asset,” the study cautions.
But for the moment, at least, many companies find they do have the necessary skills to meet their AI needs. Of the survey respondents who’ve deployed big data technologies, roughly half felt that their organizations are skilled at using big data to solve business problems. And nearly all (95 percent) of those who say they’re skilled in this area also use AI technologies.
The findings also highlight the need for enterprises to constantly innovate in the realm of AI so they don’t fall behind. “For example, while 63 percent of the survey respondents who have an innovation strategy believe that they are skilled at using big data to solve business problems, only 13 percent of those without a strategy feel the same way,” the survey notes. Likewise, more than 35 percent of those who have an innovation strategy believe that their organization is effective at using the information derived from AI to guide decision making, while only 9 percent of respondents from organizations that lack a strategy can make the same claim.
Clearly, the true value of AI goes to those who invest in innovation.
Algorithms can also be used in healthcare settings. Find out here how Boston Children’s Hospital is using a weather-inspired algorithm to predict flu cases.