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How to apply AI for customer engagement

In today’s connected world, brands like yours are collecting and analyzing vast amounts of data to inform their business, and gain a competitive edge. To keep pace, you’ll need to consider using Artificial Intelligence (AI) as a means to improve operations, drive customer engagement, and optimize your customers’ experience.


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AI and improving the customer experience

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AI and improving the customer experience

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Below, we’ll share some examples of where AI technology is already being put to use in the market today, and delivering significant return on investment for some of the world’s leading organizations.

What are Artificial Intelligence (AI) & Machine Learning Applications?

Artificial intelligence focuses on making already “intelligent” systems capable of simulating human-like decision-making and execution – enabling those systems to perform functions traditionally executed by skilled human professionals – but do so at a much higher level, because of the speed and power available on modern computing platforms.

AI systems can be very simple, like business rules that replicate a simple human decision – or extremely complex – like executing real-time customer “conversations” in a call-center, while still providing a natural experience to the customer. Advances in data processing speeds, lower costs, big data volume, and the integration of data science into technology has made practical AI a reality for many organizations, and has brought it within reach of many more. Modern customer interaction systems now frequently include actual “learning” – the ability of the system to consistently improve performance through interpretation of historical patterns, actions taken, along and an understanding of what’s considered a successful outcome.

For example, Pega’s Adaptive Decision Manager applies a form of machine learning to continuously improve Next-Best-Action recommendations, by capturing a feedback loop so that the results of previous recommendations can inform future recommendations. Other examples of machine learning include text analytics and sentiment analysis. Pega’s sentiment analysis determines whether a tweet (or any other text) was “positive,” “negative,” or “neutral.” Rather than programming the system to understand the sentiment of a block of text through a complex set of business rules, the system is “taught” by being fed a large number of text blocks and the sentiment associated with each. The system then discovers and builds connections between words and patterns in the text and the sentiment, allowing it to discover the sentiment of new blocks of text.

What are the business benefits of AI?

AI helps organizations improve how they engage their customers by enhancing the performance and efficiency of the tasks it’s assigned to. Despite the recent marketing hype, organizations have been realizing benefits from AI for some time now – Pega has known for years that firms need deep analytical capabilities in order to find insights, then deliver them to business people and customers. Our entire platform, especially the Pega Customer Decision Hub®, is built to provide an “Always-On Brain” that is constantly using new insights to engage customers more effectively. Other providers will claim similar functionality, but end up stitching together disparate portions of acquired technologies that provide disconnected pockets of AI.

By contrast, Pega offers a single “brain” built into all our enterprise CRM applications to provide insights in real-time, at scale, and with continuity across the entire customer journey such as the results delivered at:

This technology is live and proven at some of the world’s leading organizations, enabling those organizations to:

  • Make highly-relevant recommendations. The machine-learning capabilities of the Customer Decision Hub (called Adaptive Decision Manager) power Pega.
  • Add intelligence to digital marketing. AI can make recommendations to agents or directly to consumers via digital channels with algorithms that use profile attributes & response behavior and learn in real-time, so next best offers are relevant and improve over time. For example, Sprint implemented Pega to make better offers and recommendations to their customers across all channels, starting with retention. According to Sprint CEO Marcelo Claure, “Pega has the brains to help an agent deliver the right treatment to the right customer at the right time.”
  • Predict the likelihood of a sales lead conversion. In Pega Sales Automation our AI predicts the likelihood of a lead to close, and suggests next best engagement and nurture strategies. At Telecom Italia, they “had systems based on silos dedicated to services, not to customers. With Pega, we are able to build a dynamic platform that adapts to the changes of the customer.”
  • Provide intelligent guidance to agents. In Pega Customer Service, AI goes to care agents, providing them the next-best-action to take that will solve a specific problem and lead to higher customer satisfaction.
  • Optimize a customer’s lifetime value. Our predictive analytics (another form of machine learning) estimates a customer’s lifetime value. Firms than use this to treat customers on a 1-to-1 basis and guide the business to both satisfy the customer while balancing the goals of the company.
  • Automate and streamline efficiency. In Pega’s BPM, case management and robotic automation eliminate workflow inefficiencies, automate desktop processes, and reduce repetitive tasks. As Allianz Health CEO Birgit Konig said, “It's our system of artificial intelligence in claims.”

Learn how Pega Customer Decision Hub leverages AI to deliver meaningful business value and can help your business do more, faster while keeping the context of your customers’ situation in mind.