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KM and Customer Engagement in the Age of AI: Q&A with Ashu Roy, CEO, eGain

Video produced by Steve Nathans-Kelly

Marydee Ojala, Editor-in-Chief of KMWorld magazine recently interviewed Ashu Roy, CEO, eGain.

Ojala: Ashu, for those who might not be familiar with eGain, can you tell us a little bit about the company and what your history is with knowledge management and the whole AI space?

Roy: We at eGain have been the leaders in knowledge management for customer service and customer engagement for over 20 years. We target our cloud platform to serve large enterprise clients in the regulated sectors, compliance-heavy sectors, like financial services and insurance, healthcare, telco, government, and onward retail utilities, and so on. Our solution is used by Fortune 500 businesses, and what they're trying to do is to improve the service experience for their customers that do it in a way that helps both through agent assistance as well as through self-service, the automation of those interactions, as well as the assistance that the agents need in solving those customer queries. And so that's our platform, which has a core of knowledge and AI that we call the knowledge hub.

Ojala: Tell us a little bit more about why your clients select you for their AI knowledge platform.

Roy: As customer interactions become more and more complex because the simple ones are getting resolved through self-service, and especially with the Covid and post-Covid hybrid work models, agents are getting more and more into the gray economy kind of experience. How do you solve complex customer conversations? With the right kind of AI assistance and guidance. Businesses are really struggling with poor customer satisfaction, high levels of agent attrition, training, and the fact that they have to stay compliant in all this. So we tend to be brought into situations where businesses have deployed CRM systems or new contact centers, and they are looking to figure out how to improve customer experience now that the basic connection capabilities are in place.

Ojala: You're speaking just a little bit in generalities here. Pick two examples of why your clients selected you as their AI for the AI knowledge platform.

Roy: One example is a large insurance company, Liberty Mutual, one of the top 10 insurance companies in the U.S., a property and casualty insurance provider with over 20,000 agents. They had a knowledge solution in place in their customer service organization but struggled with the fact that the quality of responses that their agents were delivering to customers was just not good enough. They were getting wrong answers. They were getting incomplete answers. They were taking too long to find those answers, and they had a knowledge system that just did not have modern capabilities like artificial intelligence and reasoning and generative AI. That customer has seen a 47% improvement in their agent satisfaction scores after deploying eGain.

Another one is EE, a large European telco, part of British Telecom. They have over 10,000 agents in their contact centers and store associates across about 500 stores. They were struggling with variation in performance of those agents across different agent pools. Some agents were really good at some things, the others were good at others, but they just could not get everyone to be good consistently. We brought in our AI platform. Now they have been able to improve their first contact resolution from what used to be 62% up to 85%. At the same time, they've been able to cut down on their agent training time by over 50%.

Ojala: What we've been hearing so much about lately is generative AI. Why does a business need a KM system to be successful with generative AI?

Roy:  A lot of companies in the last year or so now have jumped into Gen AI since ChatGPT came along and became quite popular. We've been working with Gen AI for about 2 years now. Businesses are finding that generative AI is a great technology, no question, very easy to engage with. At the same time, it has challenges around, sometimes, quality of responses. It has challenges around how do you control it effectively. And at the heart of it, for generative AI to do a good job, it needs good content, correct content, which is brought to bear at the point of conversation. That's where knowledge management excels.

We see three capabilities that are crucial for GenAI to do well in an organization. Number one is the presence and availability of correct content, which is continuously refreshed and updated. The second is appropriate controls so that you can point GenAI to the right sort of tasks and subtasks, and do it in a way that you are constantly monitoring whether it is in fact working well or not. The last bit is analytics—to be able to see what, ultimately, is the operational impact of having GenAI in your environment, delivering some sort of assistance either to your employees or to your end customers, depending on your level of comfort and the type of use case. So those are the three things that knowledge really brings in and dovetails well with eGain AI capabilities.

Ojala: How do you go about reducing risk when you're adopting a new KM system?

Roy: Businesses are keen to move quickly with automation, which is really the prize here, while ensuring that the experience stays at least as well as it is doing now or even better. But in that journey toward more automation and higher productivity for employees, the risk is deploying platforms that end up not working very well. Businesses that do manage to develop and deploy a successful knowledge platform and roll it out look for three things. The first thing is a partner who has been focused on knowledge and preferably modern knowledge capabilities that are embedded and infused with AI.

Doing knowledge well requires focus, innovation, and comprehensive capabilities. That's the second thing. Talking to Gartner, their analysis on knowledge management is very interesting. They say that knowledge is an area where you need seven different capabilities or attributes around knowledge, and all seven need to be working well for the business value to be delivered. That's the comprehensive nature of knowledge that you need, including analytics, personalization, AI, and guidance.

The last thing an approach that is crawl, walk, run. They are working with partners who know what to do, but also taking small steps toward a large goal. Sponsorship in your organization ensures that that 3 year program, working with the partner, can be operationalized through a trial, a successful initial deployment, and then scaling up

Ojala: With all you've been saying about generative AI, Ashu, in your view, what is it that really matters with AI?

Roy: AI is a big catchphrase. We know it's very relevant for businesses. Where we see the opportunity to bring AI capability and technology into knowledge is around reasoning, around extractive capabilities of AI, around conversational capabilities of AI, and then around generative capabilities of AI. All four have to be orchestrated in delivering the right sort of experience, either for assisting agents or for customers.

In fact, interestingly, what we are finding now, with our latest product launch that we did around AssistGPT—we call it the focus areas for users—is around knowledge authors and knowledge managers first, because we see that as a tremendous opportunity to help improve the productivity of knowledge authors and managers by identifying gaps in conversations between customers and the business, which can be done very well with GPT and generative AI capabilities by suggesting draft responses or content for these answers using generative AI but still applying the principles and the process of knowledge management to ensure that you're getting the right kind of knowledge governance through this whole journey.

Next, improve existing knowledge content to make it more consumable, to make it more brand aligned, to make it more empathetic. So not just looking at generative AI, but bringing together generative AI with conversational AI, with reasoning AI and with extractive AI. So we see that kind of comprehensive AI capability is what makes knowledge so much more exciting for clients nowadays.

Ojala: Great. Well, thank you so much for your time today, Ashu. Appreciate it.

Roy: And likewise, thank you for having me on the program. Thanks, Marydee.

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