One day we will “think about machine learning the way we think about electricity: It’s hard to imagine the world without it,” said SAP Chief Innovation Officer Juergen Mueller at the recent SAP TechEd Barcelona.
Under Mueller, SAP has embarked on a journey to bring machine learning to business around the world, essentially “electrifying” all applications with this technology.
For the uninitiated, machine learning takes Big Data, runs it against sophisticated algorithms and helps applications to learn from this information. Massively improved computing power makes this possible in real time. Most importantly, it allows applications to “think” and independently resolve problems – going beyond what they were explicitly programmed to do, and often what humans can do.
SAP has embarked on a journey to bring machine learning to business around the world
Unlike many technology companies that are just beginning to explore machine learning and its meaning for businesses, SAP has a well-developed vision and strategy to win in a market that IDC estimates to be $47 billion USD by 2020. Mueller says, “SAP has a clear way to deliver business value with machine learning.”
Markus Noga, who heads up machine learning for SAP globally said, “SAP wants to be the business enterprise leader in machine learning. Data is the fuel for machine learning, and over seventy percent of all business transaction touch an SAP system. We understand our customers and their data – that’s what differentiates us and gives us a strong right to win.”
At SAPPHIRE NOW in May 2016, SAP rolled out several machine learning prototypes and put a stake in the ground, promising that SAP will make all applications intelligent. Now the company is making good on this promise through a variety of technology and ecosystem initiatives.
First, the company will release the machine learning prototypes demonstrated at SAPPHIRE NOW as applications in 2017. This initial set will tackle time-consuming problems like:
- Matching incoming payments to accounts receivable. Learning from accountants’ past actions, the system can automatically process future payments on their behalf.
- Detecting logos in high-definition video, for example, identifying where and when branded material appears in televised events. This new technology called “computer vision,” lets brand owners understand the return-on-investment for advertising spend more quickly and precisely.
- Classifying and clustering customer service tickets. By evaluating the service professionals’ historical activities, the system can learn how to accelerate response times and improve customer satisfaction.
SAP has already begun work on its next wave of applications, using design thinking to engage with customers and business units within the company to find most valuable scenarios.
Second, SAP is creating a platform so developers and partners can more easily build machine learning applications. Each of the new applications mentioned above will have corresponding services delivering functionality like invoice matching or visual identification. These services will be open to developers as a set of APIs enabling them to simply embed machine learning capabilities into existing applications. As SAP and its ecosystem create new applications, this bank of services will increase.
“Our strategy is simple. We will make all enterprise apps intelligent. We’ll make that intelligence available to line-of-business business users at the point of consumption, right in the business app where they need it,” said Noga.
Which gets us to the third initiative. The SAP Innovation Center Network is engaging partners and customers. They are soliciting direct feedback about the best business scenarios for machine learning.
SAP also recently announced a program to help customers consume partner solutions based on SAP machine learning technology by promoting interface certification and platform compatibility. In addition, in order to address the deficiency in machine learning skills, SAP is offering a new massive open online course (MOOC), Enterprise Machine Learning in a Nutshell.
As the machine learning hype cycle is set to continue into 2017, and technology vendors evolve their offerings, SAP is well-positioned to help organizations figure out how to use it to improve their bottom line.