How organizations can make the most of machine learning

At SAP SAPPHIRE NOW 2019, TechRepublic Associate Staff Writer Macy Bayern sits down with Markus Noga, senior vice president of machine learning at SAP to discuss which companies should use machine learning, and best practices for execution. The following is an edited transcript of the interview.

Macy Bayern: In what ways are organizations most using machine learning currently?

Markus Noga: There’s three main ways that organizations use machine learning today, and we like to group them into the areas of automation, conversation, and intelligence. If you think about automation, things like factory robots come to mind, and the same repetitive tasks that robots have automated in factories, automation technology for software can automate in desktop environments. This category of software is called robotic process automation. SAP happens to be launching its robotic process automation solution called SAP Intelligent RPA this week. And we enable users to transcend the bounds of repetitive clicks and actions in systems, in order to focus on higher value tasks with IRPA.

The second big category is about the conversation. Not the conversation that you and I are having here, but repetitive large scale conversations around customer service, around procurement inquiries, around parts or order inquiries, where a small number of domains account for a large conversation volume. And by putting in the chat box and conversational agents, we enable the humans to focus on the challenging, the difficult to serve, or the value-adding parts of the conversation and letting bots take care of the routine elements and the repetitive tasks of this. SAP has a great product for this app called SAP Conversational AI, and it’s out for a free trial at SAP with a community of more than 60,000 active developers already.

Last but not least, people are using AI to bring actual intelligence into business processes. This is about training little machine learning modules that interface with the data, that interface with the decisions, and that keep learning in the business process. This kind of model brings the biggest value whenever decisions are at stake and whenever you need to decide anything from which banner ad to serve, all the way to whether to offer a line of credit to this customer. You want the best model and that can help supplement the decisions humans are taking in the process with AI.

SEE: Managing AI and ML in the enterprise (ZDNet special report) | Download the report as a PDF (TechRepublic)

If you look at that continuum from automation to conversation to intelligence, AI is really making a difference across many, many businesses already today.

Macy Bayern: And so, how can an organization know if they are the right candidate for this kind of technology? For machine learning and those three things that you mentioned.

Markus Noga: I’d say that AI organization are a candidate for these technologies in parts or pockets. I would paraphrase it as which areas of their business are most ready for using the technology today and, which areas would most benefit from using the technology today. Ask yourself questions like, do I have the data? If I don’t have the data, it’s very hard to learn from anything because you lack that historical record.

And if parts of your business already have say, customer interaction, customer service, customer service quality data today, you can start learning from them right away to be even better in future. If you need to collect this, to begin with, you’re going to be half a year to a year behind the curve when it opt-ins. So, do I have the data is the first and biggest question.

The second question is, where is the value? If you have a large issue in quality of product, if you have a large issue in quality of customer service, if you have a large opportunity is predicting sales opportunity better. Or a large downside from risks and certain types of customer engagement, then there might be potentially large benefits from using AI technology in this basis.

And if you pull the two together, readiness and data, as well as the business opportunity to serve, you’re well equipped to make the right decisions about AI deployment today.

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