Recently, AI Business had a Q&A-style interview with SAP’s Senior VP of Machine Learning, Dr. Markus Noga, to talk about how enterprises can get started with successfully deploying artificial intelligence and machine learning in 2019. This blog is adopted from the original interview, which can be found in the article SAP: AI & RPA to Become Business Critical in 2019. Here are some key takeaways.

How to Start the Machine Learning Journey

Machine learning can be an unfamiliar concept to companies new to recent intelligent technologies. They might see the benefit behind it—but are not exactly aware of where to start. However, there are many resources available that can help bridge the gap and make the transition easier. According to Noga, there are two methods to starting the machine learning journey.

“The first and most easy way is to implement ready-to-use, intelligent applications with in-built machine learning capabilities… For more specific needs, machine learning platforms offer pre-trained or re-trainable algorithms for image, document, text, or tabular data processing that serve as machine learning building blocks.” – Dr. Markus Noga

By incorporating these applications, companies can expect to see many improvements in their business operations. These can include identifying trends and patterns in the company’s data, finding new efficiencies in current processes, and predicting future outcomes.

Why You Need Machine Learning to Become an Intelligent Enterprise

With the surge of IoT and big data in recent years, companies have more data than they know what to do with on their own. There is simply too much data for humans to sift through manually. And without filtering through to find the important information, companies can find themselves falling behind the competition.

In order for businesses to become intelligent enterprises, they need to leverage this data effectively and draw meaningful insights from it. Insights that can suggest next-steps for moving forward. Machine learning and predictive analytics can provide this.

“Making enterprises intelligent requires developers use machine learning platforms to consume, design, and scale machine learning according to their individual needs. These platforms deliver easy deployment of machine learning models into production and at scale, simplifying the lifecycle and integration of machine learning.” – Dr. Markus Noga

Intelligent robotic process automation combines (IPA) can also help. It combines RPA with machine learning and conversational AI to autotomize repetitive tasks and reduce manual intervention. These tasks can include placing purchase orders, clicking through workflows, and updating spreadsheets—ultimately allocating more time for more demanding tasks.

What Kind of Data Do You Need to Make Machine Learning Work?

Sure, companies have lots of data—but is it all useful? When it comes to effectively using machine learning, Noga believes that the biggest obstacle is the availability of high-quality data. Without quality data, the algorithms cannot be trained properly—leading to less accurate models and an inefficient use of resources. Aside from the quality, the data also needs to be accessible and available in a digital format.

“Obtaining the right data is key to solving problems with ML. The new reality is that your data strategy needs to be a key component of your digital transformation strategy.” – Dr. Markus Noga

How Should Companies Respond to the Underlying Concerns of AI & ML in Society?

As artificial intelligence and machine learning are becoming more prevalent, societal concerns for how it will be used grow as well. Some people still feel uncertain about the fair and responsible use of these technologies. However, the ultimate goal of these services is to augment people’s lives—reducing repetitive and tedious tasks, so that we people can be more creative

“I recommend every software vendor look at the ethical and societal implications of the latest advances in technology and contribute to the public debate about this subject. Because in the end, our objective is to carry on creating software that augments humanity to use their intellectual potential.” – Dr. Markus Noga

The Outlook for Machine Learning in 2019

Artificial intelligence and machine learning aren’t far-fetched ideas anymore—they’re here to stay. By adopting machine learning technologies, businesses can be more efficient and effective in their operations and ultimately transform into intelligent enterprises.

“I predict the rise of intelligent robotic process automation that will emerge as business critical…Additionally, conversational AI will take automation a step further to automate businesses’ customer support with more intelligent chat bots.” – Dr. Markus Noga

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