When Google’s AlphaGo algorithm beat the Go world champion in 2016, it became apparent that machine learning had arrived and would significantly shape the future.
As a new breed of software that is able to learn without being explicitly programmed, machine learning will be able to access and analyze structured as well as unstructured data at a level of complexity that human minds find difficult to grasp. Looking at the quality of today’s voice recognition and image recognition software, as well as at the capabilities of self-driving cars, we can already see how self-learning algorithms may influence our lives.
Computer scientists have been pursuing artificial intelligence since the 1950s. Now, thanks to recent advances in technology, including Big Data processing, increased computing power, and better algorithms, computers have begun to compete with, or even surpass, abilities once considered exclusive to humans. Machines are learning to write, speak, and find meaning in images and video. In the future, intelligent machines will increasingly support humans. We will enter the age of the intelligent enterprise.
Machine learning will benefit businesses in numerous ways. Organizations will be able to accelerate and optimize their business processes. Business leaders will gain greater ability to detect patterns in their operations and in customer interactions that will allow them to identify relevant insights. Machine learning can simplify user interactions with devices, reduce human intervention, and automate repetitive tasks, allowing people more time to focus on work requiring the creativity and complex problem-solving that they do best.
Where Machine Learning Shows Promise
Public attention to machine learning often focuses on consumer applications such as recommendation engines and smart devices. But it also holds great promise for business-to-business uses. In the B2B context, we envision that machine intelligence will be applied first in the following domains:
Intelligent business processes
Many of today’s business processes still run according to rigid rules and depend on human interaction. Often, these processes involve highly repetitive work such as checking invoices and travel expenses for accuracy or going through hundreds of résumés to fill a position. By letting self-learning algorithms find patterns and solutions in data instead of following preprogrammed rules, certain business systems will reach a new level of intelligence and efficiency.
Our economy depends on various elements of infrastructure, including energy, logistics, and IT, as well as on services that support society, such as education and healthcare. But we seem to have reached an efficiency plateau in these areas, just as we have done with our business processes. Machine learning has the potential to find better and more flexible rules to run the complex and fast-changing systems that provide the foundation for growth.
Digital assistants and bots
Recent advances in machine-learning technology suggest a future where devices running on self-learning algorithms will operate much more independently than they do now. They may come to their own conclusions within certain parameters, develop context-sensitive behavior, and interact with humans much more directly. Our devices—already able to react to our voices—will become interactive, continuously learning assistants that help us with our daily business routines by scheduling meetings, translating documents, or analyzing text and data.
The Starting Point
Data is the fuel for machine learning. To get their enterprises ready to embrace machine learning, business leaders must embark on a serious effort to eliminate data silos and gain access to data from their ecosystem of suppliers, partners, and customers. Doing so is a key prerequisite for success. Furthermore, organizations need to start identifying the sweet spots for machine-induced improvements, such as highly repetitive work.
In time, machine learning will be like electricity to us—we’ll find it hard to imagine the world without it. It will bring intelligence to business environments, uncovering new market opportunities and enabling humans to focus on work that adds value instead of spending time on tedious, repetitive tasks.
Juegen Mueller is chief innovation officer at SAP.
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