Machine learning (ML) is a type of artificial intelligence (AI) that can help businesses understand, organize, and act upon company and customer data with greater speed and accuracy, and with significantly fewer costs. In doing so, ML helps empower business units to be proactive, innovative, productive, and customer-centric: hallmarks of a successful digital transformation.
In this article, we provide a definition of machine learning, and explain why it’s important for businesses and their digital transformation strategies. We’ll share examples of some benefits of machine learning DevOps, as well as ML for customer service, operations, and sales and marketing strategies.
What is machine learning?
With traditional artificial intelligence, specific, usually rule-based outcomes are written directly into a program.
For example, rudimentary AI-based chatbots can have simple conversations with a human based on a set of rules that has been provided to it. Somewhere in the software, a human has supplied the rule that when the chatbot sees the word accounting, it should provide a list of predetermined answers relating to accounting. The signal (accounting), rule, and outcomes were provided by a human. They are hardwired in.
Machine learning, on the other hand, enables a computer program to develop its own set of rules from data that has been provided to it.
The developers of a more sophisticated ML chatbot might provide it with millions of real-world conversations between clients and customer service representatives (what’s known as training data). The chatbot would then search for patterns in the conversations and develop its own set of rules based on them. There’s no need for a human developer to provide the chatbot with rules or direction.
The more conversations the chatbot has, the more sophisticated and accurate its responses. An important caveat is that ML is only as accurate and unbiased as the information it’s given, which is why data quality is important when training ML software.
Why is machine learning essential for digital transformation?
Digital transformation can be defined as the re-conceptualization and restructuring of business practices, operations, and culture through the reasoned integration and adoption of digital tools, typically with a highly customer-centric focus.
Machine learning excels at rapidly organizing, analyzing, and making intelligent decisions and recommendations based on large quantities of data. It empowers employees and business leaders to make full use of company data, and to do so much more quickly, easily and comfortably than ever before. This frees team leaders and members from mundane, repetitive or complex and time-consuming tasks, enabling them to focus on meeting business objectives and provide outstanding customer experience.
In addition, there are examples where machine learning now outperforms humans. When it comes to understanding evolving customer needs, for instance, determining the next best action in a sales experience, or ensuring resources are allocated in response to business needs, machine learning is often better able to manage these tasks.
Digital transformation is about empowering human workers to be highly creative, fully engaged and productive, and laser-focused on the customer experience. Machine learning tools can help make this possible by providing employees with the right information at the right time, in formats that make sense for their role, and in a way that ties together business units.
How businesses are benefiting from machine learning
Here are a few examples of how to use machine learning successfully across business units.
- Understanding customers. In recent years, the quantity of client data available to businesses has increased significantly. As a result, data has become more difficult to analyze and understand without the help of machines. A variety of ML-based tools and platforms now exist that help businesses gain a more profound understanding of their customers. This has led to better customer experiences, especially for teams in marketing, customer services, and sales.
- IT, DevOps and incident management. Most businesses use a wide range of technologies and digital tools for day-to-day operations. IT and DevOps teams keep everything running smoothly. Yet, as digital tools become common and their interactions more complex, the number of technical incidents increases. Machine learning can be used to reduce noise, gain a better understanding of relationships between incidents, and reduce time to resolution.
- Supply chain, production, and logistics. This is one area where the adoption of machine learning has led to significant cost reductions for many businesses. BMW, for example, uses ML-based algorithms to move 31 million individual parts around its factories in response to supply and demand. Freed of this logistical nightmare, BMW’s employees can focus on customer relations, design improvements, and more.
- Human resources and people management. Many of the most popular HR and people management platforms leverage machine learning to streamline this complex task. Importantly, ML enables smaller, leaner HR teams to manage a larger number of employees. ML-based chatbots can respond to most typical employee queries, which saves time and reduces the chance of human error.
- Understanding market trends. Given the right data, machines can be quite accurate at predicting human behavior and spotting the emergence of new market trends. In fact, there are a variety of ways machines are now outperforming humans and traditional AI in forecasting, be it for hurricanes, earthquakes, or business markets.
A successful digital transformation strategy should focus on empowering employees and business leaders. It should equip them with digital tools that simplify business processes, encourage innovation and creativity, and provide a fuller understanding of the customer experience.
Machine learning, by virtue of its ability to rapidly and intelligently analyze company and customer data, can do just this. Businesses are already using machine learning to augment a variety of business units and successfully undergo digital transformations.