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For many years, computers could play mathematical games like chess and the sheer computing horsepower was the key to their success. Last year, for the first time ever, a computer was able to play the complex board game of Go and win! “AlphaGo”, the artificially intelligent player developed by Google, won the game by defying millennia of human instinct and intellect. What is most interesting about this is that it was not preprogrammed to play Go: rather, it learned using a general-purpose algorithm which allowed it to interpret the game’s patterns and came up with entirely new ways of approaching the game that originated in China more than two thousand years ago.
Clearly, machine learning and the larger world of Artificial Intelligence (AI) are no longer the stuff of science fiction. They’re here—and many businesses are already taking advantage. For example, Gartner predicts that “by 2020, 30 percent of all companies will employ AI to augment at least one of their primary sales processes.” As a new breed of software that is able to learn without being explicitly programmed, machine learning (and deep learning) can access, analyze, and find patterns in Big Data in a way that is beyond human capabilities.
AI, machine learning, and deep learning are often used interchangeably, but they’re not the same. In a nutshell, AI is the broader concept of machines that can act intelligently. Machine learning and deep learning are sub-sets of AI based on the idea that given access to large volumes of data, machines can learn for themselves.
What is Machine Learning?
Machine Learning is basically the practice of teaching a computer how to spot patterns and make connections by showing it a massive volume of data. So, rather than programming software to accomplish a specific task; the machine uses Big Data and sophisticated algorithms to learn how to perform the task itself. Machine learning allows applications to “think” and independently make a determination or prediction–going beyond what predictive analytics and Big Data analytics can do, and often beyond what humans can do. A popular consumer example of machine learning is a recommendation engine in an ecommerce environment.
"Artificial Intelligence, machine learning, and deep learning are often used interchangeably, but they’re not the same"
Machine learning is not a new concept, but it has recently gained fresh momentum. Why? Processing power and storage are now more affordable than ever, and the explosion of Big Data from various sources—customer data, text, images; data from internal systems such as CRM or ERP; data from the internet or IoT devices—is making it easier for machines to “train” and learn. Machine learning can automate and prioritize routine decision making processes—so you can achieve best outcomes sooner.
Your data is constantly being updated, which means your machine learning models will be too—much faster than humans can currently develop them. This lets you quickly discover and process new insights to adapt to rapidly changing business environments. An “algorithmic business” uses advanced algorithms to drive process automation and improved decision making. Making the shift can accelerate overall knowledge harvesting and pave the way for innovative business models, products, and services.
One of the most exciting uses for machine learning is to understand patterns in Big Data in a way that humans currently can’t—and then trigger concrete actions. For example, it can predict potential sales opportunities and then recommend actions to close deals. With machine-aided business processes and faster overall workflows, you can optimize business operations and your product and service offerings—so you can do and sell more while lowering back-office costs and TCO.
Here are two key areas we think the technology will really shine. Machine learning shifts traditional rules-based processes to intelligent ones that can discover new patterns in large, unstructured data sets—and make strategic predictions all on their own. It can also take on highly repetitive tasks such as checking invoices and travel expenses for accuracy.
Here are Some Other Use Cases:
• Digital Assistants and Bots
Advances in AI technology suggest that self-learning algorithms may soon come to their own conclusions within certain parameters and develop context-sensitive behavior. Devices will be able to interact with customers, schedule meetings or follow-ups, translate documents, understand customer inquiries, provide customer support, and take on other routine business tasks.
• Customer Service: Anticipating Needs
Gather, analyze, and respond to customer inquiries at top speed: Efficiently tag and cluster inbound chat, social media posts, e-mails, and more–and automatically determine classifications, routing, and responses.
• Sales & Marketing: Loyalty and Retention
Get instant insights into customers’ transactional behavior using advanced machine learning to mine, predict, and capture lead conversion indicators and propensity to buy; recommend sales content or personalized product offers; identify untapped opportunities, segment and target customers and–most importantly–provide insight to take action.
SAP's vision for machine learning is to focus on solving real business problems that will have huge business impact. SAP touches more than 70 percent of the world’s business transactions, and we want to infuse them with even greater intelligence. Our goal is to build machine learning technology into all our software, across every line of business and industry we serve. And we’re doing it with SAP Clea–machine learning intelligence embedded into our cloud platform and applications such as SAP Hybris. The approach will open fresh new ways to leverage all your data, simplify everyday processes, empower employees to focus on your customers and engage with your customers like never before.
SAP Hybris and SAP Clea AI in Action
Even though machine learning is still in its infancy, companies are already using it to engage with customers, automate transactions, increase customer satisfaction, drive revenue, and save time and money. And when AI is built into cloud platforms and applications, you won’t need a costly custom build to get up and running.
At TUI, one of Europe’s largest travel companies, SAP Hybris and the power of SAP Clea machine learning help to automatically classify and cluster social media posts to ensure customer satisfaction. TUI customers expect a “highly personalized, super-fast, and super relevant experience.” For TUI providing the right product, through the right channel, at the right time is a very compelling way of delivering that kind of customer experience.
Man vs. Machine
Unlike the Go competition mentioned above the use of AI is not about replacing human interaction with chat bots and the likes. With new machine learning capabilities and advanced chat or messaging bots the lines between a fully automated response and human assistance are more and more blurring and the customer may not even know whether the response is coming from an automated system or a human being.
The true value of AI reaches far beyond the simplistic narrative of man versus machine. Instead, AI’s potential may be in teaching humans new ways of thinking for ourselves and engaging with customers like never before.