From Digital To Intelligent: Making The Most Of Machine Learning

Dr. Markus Noga

Businesses are no longer just digital – they are becoming increasingly intelligent. A recent survey of 360 organizations by the Economist Intelligence Unit and SAP showed that, on average, 68% of them use machine learning to enhance their business processes. Now, businesses are moving beyond just improving performance across the existing business, instead moving towards developing entirely new business models, optimized processes, and value propositions.

For businesses, machine learning can enable software to adapt and improve the execution of tasks and processes autonomously. This saves time and money while empowering employees to focus on value-adding, strategic, and creative tasks. Businesses that have already benefitted from the power of machine learning are called Fast Learners, and they experience benefits from improved customer satisfaction and increased profitability. Some have improved customer support with machine learning chatbots, and nearly half of all Fast Learners expect revenue growth of more than six percent from 2018 to 2019.

But what sets Fast Learners apart from their competition? What makes them so willing to take the perceived – yet much lower than expected – risk of embracing this new technology? As I work with them in implementing machine learning across their businesses, five key traits become more obvious every day:

The five traits of fast learners

  1. C-level, strategic priority: Fast Learners’ senior-most management sees the strategic value of machine learning and fosters a workplace environment that is not afraid of change.
  1. Increased competitive differentiation: Fast Learners see machine learning as a pragmatic yet innovative way to stand out in a crowded market, not as a gimmick or fad.
  1. New revenue and profitability: Machine learning is a valuable source of revenue and profitability for Fast Learners. They look to bring about fundamental, rather than incremental, change and believe machine learning’s potential in business model innovation is enormous.
  1. Key processes close to home: Spending money on locally sourced business functions is important to Fast Learners – they spend more on local functions than they do on ones from low-cost regions.
  1. Enterprise-wide strategy: Fast Learners look at what machine learning can do for their business in a holistic way rather than forcing it into a purpose that may not be the best fit.

Of these traits, I believe that C-level strategic priority and enterprise-wide strategy are the most important. These two traits often go hand in hand – where senior management is aware of the opportunities and limitations of machine learning, they are more likely to look at what the technology can do for their business in a holistic way with enterprise-wide strategy. The other traits simply follow naturally.

Embracing the hype for improved business practices

Equally apparent are the reasons businesses do not implement machine learning. Most commonly, they lack those aforementioned traits. But often, there are also misconceptions about the effort and cost required to implement machine learning solutions. Many simply don’t know where to start or are afraid to fall victim to yet another technological fad.

But the machine learning hype is well-warranted. Fast Learners who began their machine learning journey before most people had ever even heard of the technology have since created a lasting impact across the breadth of their organizations that goes far beyond hype. For example, one Chinese shoe company used machine learning to enable customers to design their own shoes and wear them within one week. Your business can launch such lasting innovations, too.

As you embark on your own machine learning journey, I recommend taking a closer look at what other organizations in your space have done. Are they using it to better connect with customers through smart marketing campaigns? Are they better responding to customer concerns after integrating it with contact centers? You’ll soon realize that there are plenty of low-risk machine learning initiatives you can pilot as you test the waters.

Interested in learning more about the five traits of Fast Learners? Read the study here


Dr. Markus Noga

About Dr. Markus Noga

Dr. Markus Noga is vice president of Machine Learning at SAP. Machine Learning (ML) applies deep learning and advanced data science to solve business challenges. The ML team aspires to building SAP’s next growth business in intelligent solutions and works closely with existing product units and platform teams to deliver business value to their customers. Part of the SAP Innovation Center Network (ICN), the Machine Learning team operates as a lean startup within SAP with sites in Germany, Israel, Singapore, and the United States.