Machine Learning: Where Thinking Big Doesn’t Mean Being Big

Meaghan Sullivan

Artificial intelligence, machine learning, and deep learning – these emerging technologies are making headlines with publicity stunts and preliminary breakthroughs for industry giants with deep pockets. While most CEOs and senior leaders are quick to dismiss the next level of predictive analytics as more parlor trick than business case, a growing segment of midsize businesses is beginning to prove them wrong.

Making the Most of Machine Learning: Lessons from 5 Fast Learners,” an SAP study conducted by the Economist Intelligence Unit (EIU), reported that small businesses (32%) and midsize companies (42%) are using machine learning for at least one business process. This finding is a stark difference compared to the adoption rates of large enterprises (26%), which traditionally have the resources (that their smaller competitors don’t have) to implement such intelligent technology.

Contrary to the hype surrounding machine learning, the progress that small and midsize businesses are making in this area is deeply rooted in future growth. As the EIU analysis indicates, this level of adoption is focused on changing business models to offer unprecedented value to customers and open competitive advantages that are reshaping industries.

Small and midsize businesses level the playing field with machine learning

While it’s true that big businesses have the scale, cash, and resources to gain some significant advantages with technology, investment in machine learning is trending in a different direction. In fact, small and midsize businesses have a more direct affinity for machine learning because, as the EIU report cited, they do not have the internal resistance against data sharing that most large enterprises have.

Take, for example, Automotive Resources International (ARI). The largest privately held fleet-management company in the world is bringing logistics to the next level by allowing customers to gather and analyze data on every aspect of its operations.

With sensor-based data gathering and real-time analytics, ARI offers new products, services, and compelling analysis to its customers so they can better understand how their fleets are operating. Customers can evaluate data, including driver activity, fuel usage, and repair cost, to pinpoint where they can cut wasted time and money and how they can adjust their business strategy to run better and reduce costs for their customers.

Another growing business that’s enjoying the fruits of machine learning technology is Markgraflich Badisches Weinhaus. This joint venture between a multigenerational family-owned wine-making business and a premier provider of sparkling wine and spirits is fusing heritage with high tech to more personally serve its refined, global clientele with customized, premium bottles of wine.

From vine to glass, the business is managing the entire lifecycle with future-ready ERP and advanced analytics – connecting wine growers and customers to the vineyard, cellar, and distribution channels. Viticulturists access data generated from vineyard sensors that monitor weather, fertilization, and harvest conditions to gain the transparency and insight they need to optimize the quality of their final products. Plus, consumers have an unprecedented opportunity to use that same information to learn the history of the wine they are enjoying.

Next-generation digitalization profoundly shapes the future of growing companies

With greater access to substantial computing power and data volumes in the cloud, midsize businesses, such as Markgraflich Badisches Weinhaus and ARI, are using machine learning innovations to close the gap with their larger competitors. They are not only gaining a simple, fast approach to improving the customer experience and operational efficiency, but also accelerating their response to market dynamics with decision-making precision and cost savings.

Change your perception of machine learning from business buzz to strategic imperative. Read the SAP study “Making the Most of Machine Learning: Lessons from 5 Fast Learners,” conducted by the Economist Intelligence Unit. Also, for a first-hand view into SAP solutions for small and midsize businesses, visit www.sap.com/sme and sap.com/products/business-one.html.


Meaghan Sullivan

About Meaghan Sullivan

Meaghan Sullivan is the vice president of Global Channel Marketing at SAP. In this role, she is tasked with accelerating global indirect revenue through channel marketing practices with a focus on VARs and Distributors. Sullivan focuses on Partner-Lead Demand Generation activities to provide SAP partners with innovative programs, campaigns and resources that enable them to more efficiently market their SAP solutions and services.