Machine Learning and AI: First steps into intelligent automation

What seemed pie in the sky only a few years ago has become reality: Machine learning (ML) is pervading economic processes and continually improving business procedures. By 2020, most decision-makers in IT and digitalization expect machine learning to play a growing role in the value creation process. To keep up with this development, you will need a keen understanding of data. Without a data-driven mindset, however, it’d be difficult to implement these techniques into your business processes.

Machine Learning as a core technology will prevail across industries. Here, I’d like to enlarge upon what machine learning is indeed about. Where is machine learning used? Where are opportunities for text and sentiment analysis in order to automate your customer communication and your case management?

What is machine learning?

Machine learning is a form of artificial intelligence. Software based on machine learning is designed to imitate human behavior and thinking. ML algorithms recognize patterns and categories in stored data and use those to predict developments. The larger the data volume, the preciser ML software works. Extracting knowledge from experience is the core competency of machine learning. This basically happens in two ways:

ML subcategories: Supervised and unsupervised learning

Supervised learning is when predictions for the future are derived from known data. In this case, algorithms are developed based on a defined case in order to resolve similar problems. For instance, this method of supervised learning is used to solve credit card fraud.
In unsupervised learning, conclusions are drawn from unknown data. To predict the weather, for example. Algorithms are developed for arbitrary scenarios that are supposed to fall into a structure within a data set. Supervised and unsupervised learning can also be combined in an application, which then creates new forms of machines learning.

Where machine learning is already in use

As these examples illustrate, we have already been working with machine learning for a while. Most of us without even being aware of it. In addition to predicting the weather and solving credit card crimes, this intelligent core technology is used:

  • in spam filters
  • to personalize content
  • in search engine rankings
  • in traffic jam prognosis.

What’s more, in speech recognition, machine learning is at work in the background, when we talk to Siri or Alexa.

Customer communication and case management with ML

As mentioned in the beginning, your customer communication and case management benefit from machine learning methods, too. Through the work of chatbots, for example: These virtual assistants work based on the principle of speech recognition and offer your customers 24/7 service on your website or via messenger. Additionally, machine learning software can be used to automate the processing of all your incoming cases and communication. ML algorithms extract incoming data, assign it to downstream processes and personalize customer profiles. They help to analyze the customer journey. Thanks to personalized customer profiles, your service agents are empowered to help your customers in a more relevant and individualized manner. The benefits of ML methods are evident. But what does it take to make machine learning feasible for your business? What resources will your organization require?

Machine learning — the road test

Machine learning methods require an up-to-date, flexible IT infrastructure. It is best to automate your processes and implement new digital processes based on an intelligent, cloud-based platform. You will need API interfaces as well as sufficient server and memory capacity — after all, you will be processing huge amounts of data. Not every business will have the financial and human resources to be able to realize these requirements. Alternatively, you can work with external cloud service providers. To do so, you...

  • must know exactly what problems you need to solve
  • be able to provide enough field data
  • format that data consistently
  • and clearly define the desired results upfront.

We are already familiar with machine learning methods in many processes. Due to the developments of recent years, intelligent technologies are increasingly pervading everyday business processes. For good reason: With the help of ML, you can operate in real time. And you don’t even have to provide all the resources by your own efforts. External providers can support you.

Do you want to delve deeper into this topic by exploring a real-world example? Then I recommend you read the case study from one of the TOP energy companies in Europe: download the PDF here for free.

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