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Center-out: The different forms and uses of the brain

Center-out™ business architecture informs proactive solutions that improve customer experience and workflow. Don Schuerman explains how Pega’s industry-leading business rules engine uses forward- and backward-chaining to establish problem urgency and customer context.

This is part 2a of the "Building a business architecture" video series. Watch part 3: “Case management."


Let's dig in a little more to that brain that's a part of the center of business architecture, and more specifically, the forms that that brain can take. And the most common of this is the form of business rules. At the core of the Pega platform is an industry-leading business rules engine. That's things like decision trees, which are nested if-then-else statements. Or decision tables, which take those if-then-else statements and put them into an Excel-like format of columns and rows. And we use business rules in Pega to do all kinds of things, to figure out who needs to approve a particular step of a process. Or in Pega Customer Service, we use business rules to guide an agent through troubleshooting a problem. Or maybe if a customer has an open case, we can use a business rule to recommend to the agent that they might want to talk to the customer about that case. You don't need fancy data, predictive AI to do that, you just need business rules. And the business rules in Pega are really powerful. They can do what is called forward and backward chaining. And forward and backward chaining is about navigating the logic of the rule to deliver incredible results. So, if you think about it, every Pega case has an urgency.

This is how important on a scale of zero to 100 is that case. And we figure out that urgency from a variety of factors, you know, things like how old the case is, who the customer is in the case, the dollar value associated with the case, have there been any manual escalations of the case thus far? Et cetera. With forward chaining, if any of these factors change, the system automatically recalculates the urgency of the case. I don't have to manually tell Pega to recalculate something. If the information changes, Pega will detect the change and automatically recalculate anything that is impacted. That's a feature we call a calculated field. It's forward chaining technology under the covers, it's really powerful and no other business process tool or case management tool has it. Pega can also backward chain. So, if you want to know the urgency of a case, but you don't have all the data, Pega can dynamically prompt you for just the data you need to get to the answer.

This is really useful for things like figuring out the reason code of a credit card dispute, where there's a whole lot of things that we could know, but we only need to really know five of them most of the time. And rather than try to map out that logic tree every time and hard code it, we use backward chaining to dynamically figure out what's the next piece of information that we need. Another form that that intelligence in Pega takes is natural language processing. This is using AI to figure out unstructured text, like what's in an email or a tweet or a chat message, to figure out the intent. What we meant by that, you know, what the customer's trying to do, the sentiment, are they happy, sad, angry? An entity extraction, pull out information like a name or an account number so that I can map it into the case for processing. This is how the email bot is able to understand emails and connect them up to a case in order to automate them and get them done.

All of this technology is available in the Pega platform, and in things like Pega Customer Service. I don't need to license Customer Decision Hub in order to do this. When I do license Customer Decision Hub, I get some advanced features. I get predictive analytics, which mines large sets of data and find patterns that humans probably wouldn't find on their own. And adaptive analytics, which is machine learning based on a feedback loop. If I make a recommendation to a customer, do I change that recommendation based on whether or not they said yes or they said no? So, in Customer Decision Hub, I apply these to the very specific problem of improving my customer engagement and my customer value in a B2C, highly interactive experience. So, things like banking and telecommunications. And Customer Decision Hub is explicitly tuned for those use cases. So for those clients and those opportunities, it's a great product to go sell. But for a lot of other clients, you can drive all of the value that you get in Pega, the ability to make decisions, the ability to have intelligence and a brain, from the business rules and the natural language processing that live in the Pega platform and in Pega Customer Service. So that brain that we put in our center, our business architecture, can be a part of every client's Pega solution.


Topic: AI and Decisioning

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