How AI transformed ABB customer support in just 90 days

Share this post:

When most manufacturers think of using IT solutions to support their business, they generally focus on augmenting their physical assets, or upgrading their inventory or supply chain management systems. ABB is different. With our unified, cross-industry ABB Ability™ digital offering, ABB is transforming into a data-driven manufacturer. We want to use data to drive all our business outcomes.

Getting a handle on customer satisfaction data

ABB, valued at USD40 billion, is a global pioneering technology leader. We produce power generation, distribution, automation and consumption equipment. We also generate an immense volume of data. For example, we had an enormous number of customer comments, in multiple languages, stored across numerous customer care systems. We needed a solution that could not only collect, analyze and translate the comments into English to identify trends at scale, but also to understand how our customers were feeling. If we could somehow capture their sentiment and emotions, we could use the information to improve our manufacturing process and avoid future problems.

Using Artificial Intelligence (AI) to drive better customer service

We realized we had a great opportunity to implement an AI customer service solution and have IBM analyze our data. In just 90 days, Watson Explorer has helped us change the way we support our customers by saving us time and improving our manufacturing process. For example, we may see a pattern in comments about one of our products, but they are written in natural language sentences and in many different languages. With Watson’s Natural Language Processing (NLP) we can analyze the comments and translate them into English. At the same time, we apply Natural Language Understanding (NLU) to analyze the text to extract metadata from the content such as concepts, entities, keywords, categories, relations and semantic roles to learn and understand sentiment, emotion and tone.

We can now link this information to our structured data to map an issue to the root cause. For example, we might discover that a specific manufacturing process can cause a potential issue for customers. We can now build analytical models around the cause-and-effect analysis for better decision making and improved workplace productivity. We consider it collaborative analytics – or a convergence of analytics – when we bring the structured and unstructured data together to help us make better decisions.

This analytics tool is very helpful to our quality engineers. In the past, they were required to pull the unstructured customer comments, translate them into understandable language and manually review and correlate them. With Watson Explorer they become more efficient while better understanding the root cause of problems. In addition, if the solution helps engineers in one country identify a problem, the same solution can be applied in other countries; we can create a global knowledge base for the whole company. As we progress, we want to teach our systems to avoid problems in the future. Already, we know enough from our data to start predicting specific patterns. Now, we can teach our systems to address the behavior or patterns immediately when they arise.

Smarter manufacturing creates happier customers

If you’re considering AI solutions for your business, I would say: understand the business problem first. Don’t start with the solution. AI is just another tool to solve the problem. Be flexible, open-minded and start on a small project. We started with a small use case that showed the viability of AI technology for ABB. Next came the proof of value. Yes, this technology would fit, but how could we get value from it? We found it was viable and could deliver a significant outcome. Only after that could we scale up for mass production.

Watson Explorer can make our business smarter by helping employees make smarter decisions. And employees making smarter decisions results in smarter processes and improved productivity. Only then will you have smarter factories. This is not about one plus one equals two; it’s not transactional. It’s about having a tool that unlocks insights from your data and assists you in your decision-making processes. I’m excited when I think about extending these capabilities to other areas of the business.

Watson Explorer is helping to elevate our thinking process, our decision-making process, our collaboration process and helping us do things that we never tried before. It’s making us a smarter ABB.


Improve your manufacturing process, workforce productivity and make better decisions with powerful text analytics and AI capabilities:

Listen to Babu Kuttala talk about ABB using IBM Watson Explorer for smarter manufacturing:

About ABB

ABB (ABBN: SIX Swiss Ex) is a pioneering technology leader in electrification products, robotics and motion, industrial automation and power grids, serving customers in utilities, industry and transport & infrastructure globally. Continuing a history of innovation spanning more than 130 years, ABB today is writing the future of industrial digitalization with two clear value propositions: bringing electricity from any power plant to any plug and automating industries from natural resources to finished products. As title partner of Formula E, the fully electric international FIA motorsport class, ABB is pushing the boundaries of e-mobility to contribute to a sustainable future. ABB operates in more than 100 countries with about 135,000 employees.

Group Vice President of Advanced Analytics Solution Delivery, ABB

More AI/Watson stories

AI insights from Behr help consumers pick their paint palette

Behr Paint Company offers more than 3,000 colors in our paint collection. We find that consumers often get confused when it comes to picking the right color for their project. They’re overwhelmed with choice, causing a kind of analysis paralysis. Often, people don’t take on or complete a painting project because of their struggle to […]

Continue reading

AI helps companies meet new data protection challenges

In an ideal world, rules should be based on principles—on what’s right, not what’s easy. In Europe, a good example of that maxim in action is the General Data Protection Regulation (GDPR), a set of rules adopted in 2016 designed to protect privacy and personal data for citizens living in the European Union (EU) and […]

Continue reading

How AI helps Japan Airlines personalize the travel experience

For airlines, the sheer volume of flights and travelers can sometimes make it difficult to provide a personalized customer experience. When airports are busy and flights are full, passengers sometimes feel that the airline simply sees them as objects to be transported from point A to point B. In response, Japan Airlines decided to set […]

Continue reading