Is your contact center ready to become a profit center?
Agility requires not just adopting new technologies like AI, but also fundamentally rethinking how we structure our teams, measure success, and interact with customers. It demands a willingness to experiment, learn, and adapt quickly in a constantly evolving landscape.
Today, we’re going to talk about how artificial intelligence is revolutionizing the contact center, transforming it from a cost center into a driver of customer loyalty and revenue growth.
To help me discuss this topic, I’d like to welcome Chang Chang, Senior Director, Product, Cloud CX Solutions at Cisco’s Webex Customer Experience Solutions.
About Chang Chang
Chang Chang, Senior Director, Product, Cloud CX Solutions, Cisco’s Webex Customer Experience Solutions.
Chang is a senior director of product management in the Webex Customer Experience Solutions business at Cisco. With over 14 years of product leadership experience, Chang has held key roles at Intuit and Mighty Audio (an early-stage startup), as well as a management consultant at PwC. Chang holds an MBA from UCLA Anderson.
Chang Chang on LinkedIn: https://www.linkedin.com/in/changjonathanj/
Resources
Cisco’s Webex Customer Experience Solutions: https://www.webex.com/
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Transcript
Greg Kihlstrom (00:00)
Is your contact center ready to become a profit center? Agility requires not just adopting new technologies like AI, but also fundamentally rethinking how we structure our teams, measure success, and interact with customers. It demands a willingness to experiment, learn, and adapt quickly in a constantly evolving landscape. Today we’re going to talk about how artificial intelligence is revolutionizing the contact center, transforming it from a cost center into a driver of customer loyalty and revenue growth.
To help me discuss this topic, I’d like to welcome Chang Chang, Senior Director, Product Cloud CX Solutions at Cisco’s WebEx Customer Experience Solutions. Chang, welcome to the show.
Greg Kihlstrom (00:42)
Yeah, looking forward to talking about this with you. Before we dive in, though, why don’t you give a little background on yourself and your role at Cisco?
Chang Chang (00:48)
Sure thing. I’ve been with Cisco for a little over three years, currently leading our cloud customer experience solution products. Prior to this, I also led the Webex app and messaging product. Prior to Cisco, I worked in product at Intuit and Mighty Audio, which was an early stage startup company.
Greg Kihlstrom (01:05)
Great. So yeah, let’s dive in here and talk about a few things today, but want to start with just the current state of AI and contact centers. We hear a lot about AI’s potential, but what are some tangible examples of how AI is already improving efficiency in ROI and contact centers?
Chang Chang (01:25)
Absolutely. Lots of improvement already. We’re seeing AI already handling routine inquiries, reducing call volume for human agents, and shortening wait times for customers. Additionally, AI assistants are actually helping human agents in their role by providing summaries to reduce after-call work or context during any agent-to-agent transfers, as well as suggested responses or agent answers to provide the right information from knowledge bases to the agent to provide to the actual end customer as well.
A good example, one of our large customers right now that is using our WebEx AI agent is already seeing 66 % reduction in incoming calls and reducing human intervention at the end of the day, which is dramatically lowering operational cost. At another customer, we’re already seeing the work processing time going from a 24 to 48 hour turnaround down to just a few hours. They’re quoting a 90 % reduction in work time in some use cases.
Greg Kihlstrom (02:22)
Wow. Wow. Yeah, that’s and you you you mentioned the the cost reductions. I think, you know, that that’s definitely ⁓ something that’s talked about quite a bit is is cost savings. But, you know, some of the other things that you mentioned, too, are there’s a customer experience angle here as well. So can you talk a little bit about, know, that impact on on customer experience? And, you know, are there any surprising ways that is being used to enhance satisfaction and build customer loyalty?
Chang Chang (02:53)
Absolutely. I think AI is really changing customer experience in terms of what can be experienced and delivered. I think everyone wants the always available type of experience. And by that, what I mean is the instant, no wait, 24-7 support anytime you want it ⁓ on any channel, any time zone. so AI is starting to really enable that experience for end customers and what really is even more so than that is the personalization aspect as well. To the point where AI will start to know me better than I know me. We’ve seen this already, right? Think of social apps, right? You can think about your TikToks or your Instagrams where they are using AI to serve content and you’re kind of hooked, right? And so they already know you so well that they know what will keep you in. I think we’ll start to see that also with AI across customer experiences, the same way brands can pull you
cool end customers in, in a way that they didn’t realize, providing recommendations they may not realize they wanted at the first place, but that totally, you know, hits the note with end customers, being proactive, right? Getting that text message or an alert before you even knew that you had a problem, right? And so I think a lot of that personalization is also coming that will change the customer experience as well. To your second question around what’s surprising, I think some of the things that I’ve seen that surprised me is the actual real time adjustment to sentiment and emotion. And so even with our AI agent, for example, if you ask it to slow down and break up a multi question, ⁓ a difficult question, it will slow down and actually go through discrete steps to go walk you through step by step something. And so I think that’s really unique. I’ve also seen great ⁓ examples in the broader industry around real time translation, right? For multilingual support, which is amazing. And you know, for me, know, not growing up in a full English speaking household, just, it’s just unlocked so much in terms of the possibility. And so I’ve been constantly surprised by how much AI can solve and how fast it’s changing and how adaptable it can be also in real time.
Greg Kihlstrom (05:02)
Yeah, yeah. And so, you know, lots of impacts from the that end customer perspective. So lots of impacts on humans that way. But also, let’s talk about the evolving role of the the humans on the other side of it. You know, the the agents with with AI taking over a lot of those routine tasks that, you know, and let’s face it, that agents haven’t necessarily loved doing. You know, it’s not it’s not the the most enjoyable part of the job either.
But how do you see the role of the human agent evolving and what new skills are going to be essential for agents in this future where they’re working alongside AI?
Chang Chang (05:41)
Yes. One thing that we’re definitely seeing and hearing is that a lot of people aren’t talking about replacing human agents. Really human agents role has really been elevated and transformed. Like you kind of alluded to moving from a transactional role to more of a trusted advisor, handling more complex and emotionally nuanced interaction. Even with our customers today, we aren’t talking about how do we reduce human agent and how do we replace human agents we’re actually see, and we’re not even seeing them in the requirements now or the outcomes that our customers are looking for. They’re actually trying to see and do more with the current resources they have, right? Actually going from providing life support from nine to five, all the way to providing life support 24 seven as well. And so we’re just trying to, we’re really seeing this evolution and hearing about human agents becoming more of relationship managers, solving complex problems.
And that leads to your second question around what new skills? think first and foremost, think EQ and empathy will become even more important as these ⁓ more transactional and easier problems get solved by AI, the tougher ones, at least for now in the short term, the tougher ones will go to the human agents. And so they’re going to be handling much more complex problems and having that empathy and being able to problem solve.
with the end customers is going to be super paramount as well. The other thing that I also see is just more AI fluency. I do think that human agents will probably get even more involved with the AI and the AI agents that are out there in terms of training them, helping curate information for them as well. And so there’ll definitely be a transformation and an elevation of their role.
Greg Kihlstrom (07:31)
Yeah. Yeah. Well, and you know, some of the, the numbers that you, you mentioned at the beginning, you know, the cost reductions, the, you know, time to solution, you know, those things, those, those massive in those cases reductions. And I would imagine some increases in customer satisfaction as well. Obviously the, takes successful integration of, all this stuff. What are some best practices that you’ve seen that have led to some of those great results when integrating AI into existing content, contact center teams and workflows.
Chang Chang (08:05)
Yes, I would maybe break it up into kind of two parts here. One is just the overall approach. I think it’s super critical or best practice is super critical to start with the actual experience you want to deliver. Making sure the handoffs are seamless and actually following it from end to end. At the end of the day, it’s a brand’s end customer that’s going to be ultimately affected by how good these AI solutions are going to deployed. so really understand the end-to-end flow is really critical.
The other good best practice on the approach is think of replacing, not replicating. think too often times companies will try to take an existing process and just try to replicate it using AI. But I think AI actually gives us the opportunity to think of it differently and provide the same outcome in a different way. And so that is ⁓ one other best practice that we’ve seen work. In terms of some tangible steps, I would say four tangible steps in terms of what we’re seeing work well with our customers.
One is establishing some sort of cross-functional AI committee. And this would include stakeholders, even human agents themselves, And supervisors and everyone along that, along the end-to-end journey to get involved and to weigh in on the approach and what use case to solve and actually testing out the experiences itself. That’s the first thing. The second tangible step is doing internal testing, right? Prove the tech in a very controlled setting, gain all the stakeholder buy-in with that cost functional committee. And then third is using the learnings, right? Very ⁓ agile nature, right? Take those learnings, build upon it, iterate, design, implement, and then do a phase rollout. Start with the pilot, expand the more use cases, and then get in at more widespread full scale. And so this is kind of the tangible steps that I absolutely see that are, you know, what we’ve seen with customers work well. That said,
a few pitfalls just to call out. Yeah, yeah, definitely. ⁓ One thing is don’t underestimate the change management process, right? I mean, we all know change is hard. And so underestimating this ⁓ will kind of, you know, always just anticipate and be aware that it is a longer process in terms of change. Second is not getting all the buy-in, right? And this is why that cross-functional committee is super critical at the beginning is you want to make sure you get the buy-in from all the key stakeholders if you really want this to be successful.
Third is be clear on the outcome and objectives at the end of the day. And the fourth thing, and maybe the most important thing is don’t forget about the data. AI is only as good as the data that’s feeding it. And so you really need to have good data going in as well.
Greg Kihlstrom (10:45)
Yeah,
yeah, definitely. Yeah, the data piece if if you don’t address that you will be addressing it at some point early on. So, yeah, definitely glad you brought that one up.
I also want to come back to something that you briefly mentioned before, but I want to, I want to dive into it a little bit more. And, know, this, this idea of always on customer engagement, you know, I think we, as we’re all, cause we’re all consumers when we’re not working, right? So it’s like, you know, we, know our expectations as, as a customer, but you know, from a, from a brand’s perspective and from a, you know, a customer service and, infrastructure stem. You know, what does that look like in practice and how does AI enable it?
Chang Chang (11:32)
Yeah. Always on engagement means customers can interact with brands anytime, anywhere, any channel. And it’s just seamless. The best way that I try to describe it is kind of like interacting with a friend or family member, right? You interact them through email, text, phone call at any time. You can switch mediums and the conversation picks back up where you last off. have the full history and context of who you are.
to the point and you know, when it’s, you know, close friends and family, they know you and can anticipate what things you will like, what things you won’t like. And so I see the same in terms of how an AI will enable this for customer experiences, right? A brand will start to know their end customers in the same way of, you know, you reach out to the brand website, you get distracted, you come back, you text them later, then you get distracted, but then it may text you back and say, did you, did you still want to follow up? Then you’ll call and you may reach a human agent who actually knows that you had already visited the website earlier. You already texted, they have all that context. so that’s what I think an always on engagement is. It really is like working with someone that you know, ⁓ and that knows you very well. One of the things, you know, in terms of, you know, how AI really enables this is the real time context around the different touch points is super critical around channels. At WebEx, we have our AI agent and AI assistant that are built on top of our journey data service, which basically captures all the rich data of interactions and active data points that happen between a brand and an end customer. And so this can help feed the AI, right? The AI agent or the AI assistant with all the past context so that when a end customer is interacting with the brand, they have that that rich experience of knowing who that end customer is as well.
Greg Kihlstrom (13:29)
Yeah, yeah. Talking about personalization, certainly, you know, that’s a key component. mean, I like the I like the that idea that you mentioned as far as, know, you should kind of think of it as a that experience as, you know, a conversation that you may pick up from channel to channel or whatever. But your family member, your friend, whatever remembers the the last cover, at least in most cases, they remember ⁓ what you said. ⁓ Maybe maybe not always as much as you wish they would. But
What role does personalization play in this always on engagement? how can AI be used to deliver those experiences at scale? Because I think that’s the tough, or at least one of the tough pieces. And let’s throw in the consumer privacy part as well. How does all that happen?
Chang Chang (14:18)
Yeah. Personalization to me is that magic moment in terms of always on engagement. Always on is great if you can always reach out 24 seven, but the second that the brand knows who you are, why you reached out, or even, even, even, even better before you even reach out proactively resolves your issues for you. That, that to me is, is a true personalized experience before I needed this, you already sent it to me, right? Or before I had an issue here, you’ve already done it for me and taken care of it for me. And so I think this is where that always on engagement isn’t just about a one way direction of, and then customer reaching out to a brand. It goes both ways as well. And making that a two way relationship.
Greg Kihlstrom (15:02)
Yeah. Yeah. And so, you know, I, we’ve talked plenty about the, human customer service agents. I want to talk about agentic AI now a little bit more. And, you know, certainly that’s everybody’s talking about, I feel like it’s, it’s like the, the chat GPT of, of 2025, you know, like three years ago or whatever it was, it was all, you know, that stuff. And now everybody’s talking about agentic. Certainly not only is it being talked about, but it’s, know, it’s gaining traction. I mean, there are, there are, there are, our use cases and their implementations. What are your thoughts on, you know, it’s, it’s a more autonomous approach, not always completely autonomous, but it’s, it’s more autonomous approach. And you know, what are some potential maybe benefits and risks?
Chang Chang (15:46)
Yeah, it really, agentic AI really is about autonomous, right? It’s the ability of AI to process information, reason, plan and act and start to handle even more complex tasks, make decisions and even take actions. And I’m excited by it because it will free up humans to do more higher value work, right? And, ideally take care of some of the things that you don’t want to spend time doing.
I think even more exciting for me is actually this notion of the multi-agent ecosystem where you have all sorts of AI agents interacting with one another. I mean, to the point where you can have an AI agent on one brand interacting with an AI agent for another brand and taking care of an issue for you. Right. And so I think there’s so many use cases where we all as end consumers spend time on things that we just don’t want to spend time on.
And this is where the autonomous nature of AI can take care of those things. Right. And what’s even more exciting is how the broader industry has enabled that. you see what we’re doing now with A2A and MCP, just the unlocking and the ability for all these AI agents to actually communicate with one another and complete tasks is I think a ⁓ step in a, what I would hopefully describe as a
benefit of getting more complex done more automatically so that we all can enjoy the things that we want to do. Right. And so I think ⁓ that’s the exciting part and the benefit part. Obviously that comes with some risks. I think the biggest one that comes top of mind is, you know, the loss of control, right. And mistakes made by AI agents can happen at large scale. And so this is where all of the correct guard rail security as absolute paramount, being able to monitor it and test. even here at Cisco, like this is like first and foremost, right? Making sure things are secure. We take our AI and being very responsible with AI and have added multi-layered guard rails just to ensure that there’s always control over these agent tech ecosystems and that they’re secured by nature and that they’re in a way that can be monitored and tested ongoing as well. so you know, those are some of the risks, but I think the benefits here are just so, ⁓ there’s so many different use cases where we can use agentic AI to solve things. ⁓ I’m very excited about it.
Greg Kihlstrom (18:18)
Yeah,
yeah. And I mean, it’s it’s I think it’s moved very quickly. mean, things, you know, things are always moving quickly, of course, but I think it’s moved very quickly, very, you know, very recently. You you mentioned the MCP stuff like all of that is just I think is rapidly changing how we’re approaching things. But let’s maybe look out, you know, a couple of years, two, three years. I know that may feel like forever looking out at this at the rate that things change. But.
You know, what’s your vision for the future of customer service? And, you know, what are the biggest opportunities and maybe the biggest challenges that lie ahead?
Chang Chang (18:56)
I will say I get this question a lot and it’s really interesting because if I think back three years ago, we definitely not would have imagined where we are today. Right. Or at least, you know, pre-chat GPT and generative AI days. And so it’s going to be really interesting to see how much will evolve and change over the next two, three years. In terms of an experience, I really do think that brands will be really empowered to engage with them customers and very hyper
personalized journeys, very proactive at anticipating needs to the point where as an end consumer, every interaction you have a brand just seems almost what you would call perfectly timed. like right, just at the right time, it’s very intuitive to who you are and what your needs are and what your purposes are. And that every time you interact with a brand, it’s even better than the last interaction as well. And so I think the technology and advancements are going to happen and it’s going to be limitless. And I do think we’ll get to a point where the, hopefully the days of bad customer experiences will no longer be a thing.
Greg Kihlstrom (20:05)
I would love that. Well, Chang, thanks so much for joining today and sharing your insights. One last question before we wrap up here. What do you do to stay agile in your role and how do you find a way to do it consistently?
Chang Chang (20:18)
thank you as well, Greg, for having me. It was a pleasure. In terms of staying agile in my role, I will kind of give a slight different answer in terms of how I leverage agile concepts in my own to-do list and how I manage my own action items. I actually have a Kanban board set up with all my action items and notes, and I just move them between the columns and reprioritize the cards. And so that’s how I’ve been leveraging the agile concept just in terms of how I manage my daily to-do list.
