This program close presents a concise summary of key insights from an artificial intelligence summit focused on contact center automation, governance and workforce transformation. This session is hosted by Bob Laliberte of theCUBE Research and Zeus Kerravala of ZK Research. It reviews vendor perspectives from Cisco, Talkdesk, Zoom and Five9 and delivers practical guidance for buyers on evaluation criteria, phased implementation and governance.
The discussion covers resolution quality, context and connected data, platform integration, continuous testing and governance and methods to evaluate AI deployments. The hosts frame action-oriented advice for measurable phased implementations that emphasize observability, baseline measurement and vendor selection factors.
Kerravala emphasizes that resolution quality rather than bot count becomes the primary unit of value while Laliberte stresses starting with bounded high-volume and high-value customer journeys and establishing baseline measurement. They emphasize that context and connected data are foundational and that governance, continuous testing and observability must operate as ongoing disciplines. Both recommend a blended human-digital workforce and phased deployments to build trust and achieve measurable return on investment.
Subscribe for additional analysis on contact center AI governance, customer experience, workforce transformation and vendor strategies.
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Program Close with Bob Laliberte & Zeus Kerravala
This program close presents a concise summary of key insights from an artificial intelligence summit focused on contact center automation, governance and workforce transformation. This session is hosted by Bob Laliberte of theCUBE Research and Zeus Kerravala of ZK Research. It reviews vendor perspectives from Cisco, Talkdesk, Zoom and Five9 and delivers practical guidance for buyers on evaluation criteria, phased implementation and governance.
The discussion covers resolution quality, context and connected data, platform integration, continuous testing and governance and methods to evaluate AI deployments. The hosts frame action-oriented advice for measurable phased implementations that emphasize observability, baseline measurement and vendor selection factors.
Kerravala emphasizes that resolution quality rather than bot count becomes the primary unit of value while Laliberte stresses starting with bounded high-volume and high-value customer journeys and establishing baseline measurement. They emphasize that context and connected data are foundational and that governance, continuous testing and observability must operate as ongoing disciplines. Both recommend a blended human-digital workforce and phased deployments to build trust and achieve measurable return on investment.
Subscribe for additional analysis on contact center AI governance, customer experience, workforce transformation and vendor strategies.
>> Well, welcome back. I hope you enjoyed those sessions. I know Zeus and I enjoyed doing them. Really want to talk about, the fact you saw 4 different perspectives, but the conversation consistently moved beyond basic automation towards end-to-end governed resolution. And I think that's really what's going to be important for organizations as they're moving forward and adopting this technology. Zeus, what do you think?
Zeus Kerravala
>> Yeah, I love the sessions, Bob, and I think it's solidified in my mind that we're all rowing to the same lighthouse, right? We're converging with that common destination, but we're taking much different routes to get there, right? There's data management issues, there's platform integration issues, there's multi-agent automation, there's security concerns and implementation discipline, operational transformation. There's a bunch of things that have to happen for this to work. We know where we're going. And what's good though, I think, is that there's different paths to get there. No two customers are alike. They're going to want to take different journeys. And what we saw was a pretty good variety to help customers choose whatever path they want to go down. So, with that, let's drill down on some of the takeaways, Bob. I think the first one that I took away is that resolution really here and resolution quality is the new unit of value, right? Bots were often measured on containment or deflection. Agentic systems should be judged on whether the customer's needs were completed, right? And completed accurately across the full journey.
Bob Laliberte
>> Yeah, absolutely. I couldn't agree with you more. And I think this actually ties into maybe a little bit of a broader scorecard. You mentioned, not just resolution, but the quality of resolution has to be there. The customer effort to get to that, the CSAT, the employee productivity, and you had mentioned earlier cost, all those things really matter. And so the growth use cases, retention, recovery, qualification, and proactive service, right? All these things help to expand the business case beyond just savings.
Zeus Kerravala
>> Yeah. And I think it highlights the fact though that this industry desperately needs new metrics with which to measure success by, right? We've used the same metrics for success for the last 30 years. Every contact center manager I talk to does want a better way to measure AI success. But I think that's something for the industry to work on. It's 'cause it's so inaccurate right now.
Bob Laliberte
>> Yeah, absolutely.
Zeus Kerravala
>> Yeah.
Bob Laliberte
>> And my second takeaway would be that really it's that having that context and connected data are really foundational for success. If you look through the videos Cisco made, you know, context, the control point. Zoom emphasized connecting interactions and knowledge. Talkdesk focused on orchestration across their system. And Five9 reinforced integration as a condition for time to value. So, all of these are really just variations on the same lesson that needs to come across.
Zeus Kerravala
>> Yeah. And they are, but again, the journey to them is slightly different. So, one of the interesting byproducts of the context and connected data being foundational is that, and I've heard other people talk about this, AI is gonna make you do things faster, right? But if you've got a broken process, you're going to get to that bad destination faster. That's all it's gonna do. In fact, we've got this for our data scientists. We say good data leads to good insights, right? But silos of data or fragmented data leads to siloed or fragmented insights. And we don't want that, right? So, that combined with stale knowledge, really this industry filled with a lot of inconsistent workflows. All of that limits accuracy. It forces customers to repeat themselves and it grows customer frustration. And so, I think it's critically important, Bob, that context exists across channels, different tools, different systems, and most importantly, that human-digital agent handoff.
Bob Laliberte
>> And it really speaks to the need to ensure that you've done the homework prior to deploying the technology so you know where that data is and you know how to connect it and integrate it all.
Zeus Kerravala
>> Yeah, I know. And it's measure twice, cut once, right? Yeah. I think one of the great takeaways we got from this is how to get started. And I ask companies that have implemented AI in the contact center all the time, how do you get started? And the answer is what we got out of this. You just have to get started right now. With that being said, don't try and boil the ocean. I've described this journey as chip shots, not moonshots. If I set a moonshot to create this AI-driven contact center, tomorrow, it's gonna be so overwhelming that I won't get it done. So, the practical advice that we heard was consistent across speakers. Choose a bounded high-volume, high-value problem with a clear owner, implement it, manage it, measure to the baseline, measure the value, and then use that as a success metric to go forward. So, I do think that it's tempting to try and transform the entire customer journey with your 1.0 release, but it's very risky. And so start small, learn some lessons, and then expand from there.
Bob Laliberte
>> Yeah, and I think one of the other key takeaways from that is the fact that when you're doing your first deployment, think about you don't want to become so focused that it just becomes another silo. So it's important to make sure that you're selecting an architecture that can connect different data and systems. That has the ability to reuse governance that you've put in place to save you from having to do the same work over and over again. And really thinking about it, again, it's, so this is, I think I might refer to this more as, you've heard me say this before, is you're thinking about writing the book, but you're starting with chapter 1. Right? When you're thinking about that architecture and so forth, you need to be thinking about, while you're going to start with chapter 1, think about how you're going to be able to expand. And in this case, that might be from just AI assistance to workflow completion as that confidence grows. So start small, gain success, but make sure that as you're going to expand, that you've chosen the appropriate architecture that's going to enable you to expand and continue to grow. All right. Fourth takeaway for me, talked a lot about governance just now, but testing and governance really need to become operating disciplines. So as these AI agents gain access to all the various tools and have transactional authority, just doing pre-production testing is probably not going to be enough, right? Organizations need to be able to continuously evaluate, observe, enforce policies, have traceability, and really retest when models or workflows change. So I think that's going to be so critical as you start deploying more and more agents that you've got that observability, you've got the ability to evaluate what they're doing, ensuring that they're complying with the policies and so forth. And you're right, it's going to be kind of a continuous testing mode as far as I can tell.
Zeus Kerravala
>> Yeah, Bob, I was at an event last week and I was doing a session with the CFO on governance, and I asked people in the audience for a show of hands who likes talking about governance, and surprisingly no hands went up, right? And I think with AI, governance has an opportunity to change the way it's thought about. Historically, governance and security gets in the way, right? We're ready to roll something out, but we can't because We've got all these rules and regulations we gotta comply with. With AI, if you have the proper governance in place, you can actually move faster with your AI initiative. So it should be something that enables adoption, not holds it back, right? But with that being said, it's gotta be baked into the design. It can't arrive as kind of this late stage thing that we then worry about once we've started some of the adoption. So I think, when you're planning, right, you have to think about what the agent could do, what data it needs, and what evidence it needs, when it needs to escalate, and then who's accountable for the outcomes. And I think if you do that, then it's really about measure twice, cut once, right? That you want to have that proper foundation in place as you design these things. And that's historically where we get bogged down because we bring it in after the fact, right? So for me, takeaway 5 is the workforce model changes, right? And so we're gonna have digital agents, we're gonna keep people, right? We talked about that extensively throughout this, but human agents will increasingly have to handle the exceptions, the complex tasks, the emotionally sensitive moments where human empathy is required, where judgment needs to take place. AI can do a lot of things. It'll help you reset your password. It can help you check your balance. It can help you book a flight, right? But those are standard processes, right? And so I think in some regard, while we're all worried about AI taking jobs, right, in the context of customer experience, it raises, not lowers, the need for training, knowledge management, knowledge access, agent assistance, and quality measurement. And I think those things, when we think about this world of what it looks like when we blend our humans and machines, the people play a really important role, but we've got to think about them differently.
Bob Laliberte
>> Yeah, and I couldn't agree with you more. And I think when you think about it from that perspective, and this has been talked about a lot, and Larry Ellison has talked about this a lot, right? Supervisors must manage a blended workforce of people and digital workers. And so, you're thinking about the fact that you're going to work today, you're not just managing people, but you also have those agents and digital workers that you manage. And so there's going to be a combination of skills that are going to be required for that and how to handle that. And, organizations are going to have to really look at how are they going to be doing their forecasting for resource need, right? Quality management is going to become really critical. I think, coaching is going to be another aspect of it. How do you coach your supervisors to manage an agent? Right. And how is that going to be done? Right. So it's a whole different skill set that organizations are going to need to adapt and be able to bring into it in order to ensure success for these organizations. And then obviously, the capacity planning, right? All these things need to reflect how the work is going to be moving between AI and humans. And like you said, there's still, a lot of good reasons why you need to have that empathy, you need to have that human judgment. And so trying to determine that blend of driving that optimal efficiency between AI agents and humans, because I know a lot of times when I get on and I'm talking through something, at a certain point, I'll just be like, I need to speak to a human, please. Please, please just get me to a human. And so the ability to recognize that and redirect people so that they can have a positive experience is going to be super important.
Zeus Kerravala
>> Yeah, you brought up an important point too with the supervisors, because a lot of these tools are built for the agent and helping them be more productive and helping them be smarter and more accurate. Right. But what about the supervisor? Right. So make sure that as you're rolling these tools out, the managers and supervisors understand what's happening so they can coach better, right? That they can understand where AI is working, where it's not working, right? And I think that's something that doesn't get talked about enough.
Bob Laliberte
>> Yeah, absolutely. And I think there was a lot of great points that we just brought out, a lot of great similarities between the organizations that we heard from today. I think there were also some areas where they differed. So I thought we'd chat about that for a minute or two. Looking at Cisco, you mentioned they're a very large organization, right? Their lens tends to be more architectural, tends to be more governance-led. Organizations like Talkdesk, as you mentioned, right, brings up that end-to-end and industry-specific automation in a lot of cases. Zoom and its partner, right, really emphasize that integrated tools and testing and pragmatic deployment. And, you know, Five9 framed the maturity journey and really provided a lot of customer proof of the value it's delivering for organizations.
Zeus Kerravala
>> Yeah. And while the difference in these companies maybe can, you might think, make things confusing, it's actually a good thing. So I think it gives buyers a context and evaluation criteria that they can use to make a better decision. So the right choice for every organization will be unique to them, right? A lot of it has to do with what you currently have installed, where you're coming from, where you're going, what kind of workflow complexity you need. There's simple, you have a lot of simple interactions, a lot of complex ones, high net worth individuals, low net worth, right? Different industry requirements, what kind of resources you need to implement, right? Does it need to be partner-led or not? And whether the priority is the service efficiency, or is it, customer experience improvement, or is it just growth of the brand, right? Different brands have different needs, and I think it's good to see, a different variety of vendors like this approach it through different lenses, and that's obviously good for everybody, so it creates a bigger pie.
Bob Laliberte
>> Yeah, absolutely. And so, to kind of bring this home, Zeus, what do we recommend for organizations? What's their action plan coming out of this?
Zeus Kerravala
>> Yeah, I think first, and this came through loud and clear, it's choose a journey, right? Don't boil the ocean. Choose a journey, baseline it, document it, and then measure it. And so this includes volume of calls, transfers, handle time, resolution, customer effort, you know, quality measurement. There's a bunch of different things you could measure. I'm saying pick a journey, find the key metrics for that and baseline it and then measure against it.
Bob Laliberte
>> Yeah, I think that's a great way to start. And then I think next what's really important is for organizations to be able to map out those workflows and the processes they have in place, right? Understand the data knowledge, the different systems, any policies they have, where the human escalation points are that are required to resolve that journey from beginning to end. So really document that and have that ready to go so they can work with their provider to incorporate that.
Zeus Kerravala
>> Yeah. And lastly, Bob, I'll say it's important to run a controlled deployment. Don't let this get out of hand. Don't let the scope get too big. I think you want to be able to see where you've been, know where you're going, but then also importantly test a lot of the edge cases. We know a lot of the core business, as you think of the 80/20 rule, right? That most of our interactions are gonna fall in the 80. We'll test the 20 as well, right? And test those continuously. Compare results to the baseline and then expand only when trust and quality hold up. And I think if you do that, you'll have a successful deployment.
Bob Laliberte
>> Yep, absolutely. Look, to wrap this thing up, AI ROI in CX won't be determined by the number of bots deployed. Really clearly, it's going to come from getting to better resolutions, being able to have more capable employees and more efficient operations and responsible execution at scale.
Zeus Kerravala
>> Yeah. And I wanna, Obviously, thank all the guests that we had. It was a great summit, Bob. I wanna thank everyone for watching this. And I'll leave you with this. I think sometimes the new technology, we think, "Well, that's risky to do that." Right now we're in a period of time where I think it's riskier to not use AI than to use AI because the world is moving that way. So move and move with urgency, but do so in a measured, scaled way that can provide you the data, the evidence, and the discipline to have success and ultimately build customer trust.
Bob Laliberte
>> Yeah, I think that's a great way to close. And I think I'll paraphrase Jensen at Cisco AI Summit when he said, "Everyone's talking about human in the loop. It's time to make sure AI's in the loop." And we need to make sure AI is in the loop now in all the processes. Nice.
>> Well, welcome back. I hope you enjoyed those sessions. I know Zeus and I enjoyed doing them. Really want to talk about, the fact you saw 4 different perspectives, but the conversation consistently moved beyond basic automation towards end-to-end governed resolution. And I think that's really what's going to be important for organizations as they're moving forward and adopting this technology. Zeus, what do you think?
Zeus Kerravala
>> Yeah, I love the sessions, Bob, and I think it's solidified in my mind that we're all rowing to the same lighthouse, right? We're converging with that common destination, but we're taking much different routes to get there, right? There's data management issues, there's platform integration issues, there's multi-agent automation, there's security concerns and implementation discipline, operational transformation. There's a bunch of things that have to happen for this to work. We know where we're going. And what's good though, I think, is that there's different paths to get there. No two customers are alike. They're going to want to take different journeys. And what we saw was a pretty good variety to help customers choose whatever path they want to go down. So, with that, let's drill down on some of the takeaways, Bob. I think the first one that I took away is that resolution really here and resolution quality is the new unit of value, right? Bots were often measured on containment or deflection. Agentic systems should be judged on whether the customer's needs were completed, right? And completed accurately across the full journey.
Bob Laliberte
>> Yeah, absolutely. I couldn't agree with you more. And I think this actually ties into maybe a little bit of a broader scorecard. You mentioned, not just resolution, but the quality of resolution has to be there. The customer effort to get to that, the CSAT, the employee productivity, and you had mentioned earlier cost, all those things really matter. And so the growth use cases, retention, recovery, qualification, and proactive service, right? All these things help to expand the business case beyond just savings.
Zeus Kerravala
>> Yeah. And I think it highlights the fact though that this industry desperately needs new metrics with which to measure success by, right? We've used the same metrics for success for the last 30 years. Every contact center manager I talk to does want a better way to measure AI success. But I think that's something for the industry to work on. It's 'cause it's so inaccurate right now.
Bob Laliberte
>> Yeah, absolutely.
Zeus Kerravala
>> Yeah.
Bob Laliberte
>> And my second takeaway would be that really it's that having that context and connected data are really foundational for success. If you look through the videos Cisco made, you know, context, the control point. Zoom emphasized connecting interactions and knowledge. Talkdesk focused on orchestration across their system. And Five9 reinforced integration as a condition for time to value. So, all of these are really just variations on the same lesson that needs to come across.
Zeus Kerravala
>> Yeah. And they are, but again, the journey to them is slightly different. So, one of the interesting byproducts of the context and connected data being foundational is that, and I've heard other people talk about this, AI is gonna make you do things faster, right? But if you've got a broken process, you're going to get to that bad destination faster. That's all it's gonna do. In fact, we've got this for our data scientists. We say good data leads to good insights, right? But silos of data or fragmented data leads to siloed or fragmented insights. And we don't want that, right? So, that combined with stale knowledge, really this industry filled with a lot of inconsistent workflows. All of that limits accuracy. It forces customers to repeat themselves and it grows customer frustration. And so, I think it's critically important, Bob, that context exists across channels, different tools, different systems, and most importantly, that human-digital agent handoff.
Bob Laliberte
>> And it really speaks to the need to ensure that you've done the homework prior to deploying the technology so you know where that data is and you know how to connect it and integrate it all.
Zeus Kerravala
>> Yeah, I know. And it's measure twice, cut once, right? Yeah. I think one of the great takeaways we got from this is how to get started. And I ask companies that have implemented AI in the contact center all the time, how do you get started? And the answer is what we got out of this. You just have to get started right now. With that being said, don't try and boil the ocean. I've described this journey as chip shots, not moonshots. If I set a moonshot to create this AI-driven contact center, tomorrow, it's gonna be so overwhelming that I won't get it done. So, the practical advice that we heard was consistent across speakers. Choose a bounded high-volume, high-value problem with a clear owner, implement it, manage it, measure to the baseline, measure the value, and then use that as a success metric to go forward. So, I do think that it's tempting to try and transform the entire customer journey with your 1.0 release, but it's very risky. And so start small, learn some lessons, and then expand from there.
Bob Laliberte
>> Yeah, and I think one of the other key takeaways from that is the fact that when you're doing your first deployment, think about you don't want to become so focused that it just becomes another silo. So it's important to make sure that you're selecting an architecture that can connect different data and systems. That has the ability to reuse governance that you've put in place to save you from having to do the same work over and over again. And really thinking about it, again, it's, so this is, I think I might refer to this more as, you've heard me say this before, is you're thinking about writing the book, but you're starting with chapter 1. Right? When you're thinking about that architecture and so forth, you need to be thinking about, while you're going to start with chapter 1, think about how you're going to be able to expand. And in this case, that might be from just AI assistance to workflow completion as that confidence grows. So start small, gain success, but make sure that as you're going to expand, that you've chosen the appropriate architecture that's going to enable you to expand and continue to grow. All right. Fourth takeaway for me, talked a lot about governance just now, but testing and governance really need to become operating disciplines. So as these AI agents gain access to all the various tools and have transactional authority, just doing pre-production testing is probably not going to be enough, right? Organizations need to be able to continuously evaluate, observe, enforce policies, have traceability, and really retest when models or workflows change. So I think that's going to be so critical as you start deploying more and more agents that you've got that observability, you've got the ability to evaluate what they're doing, ensuring that they're complying with the policies and so forth. And you're right, it's going to be kind of a continuous testing mode as far as I can tell.
Zeus Kerravala
>> Yeah, Bob, I was at an event last week and I was doing a session with the CFO on governance, and I asked people in the audience for a show of hands who likes talking about governance, and surprisingly no hands went up, right? And I think with AI, governance has an opportunity to change the way it's thought about. Historically, governance and security gets in the way, right? We're ready to roll something out, but we can't because We've got all these rules and regulations we gotta comply with. With AI, if you have the proper governance in place, you can actually move faster with your AI initiative. So it should be something that enables adoption, not holds it back, right? But with that being said, it's gotta be baked into the design. It can't arrive as kind of this late stage thing that we then worry about once we've started some of the adoption. So I think, when you're planning, right, you have to think about what the agent could do, what data it needs, and what evidence it needs, when it needs to escalate, and then who's accountable for the outcomes. And I think if you do that, then it's really about measure twice, cut once, right? That you want to have that proper foundation in place as you design these things. And that's historically where we get bogged down because we bring it in after the fact, right? So for me, takeaway 5 is the workforce model changes, right? And so we're gonna have digital agents, we're gonna keep people, right? We talked about that extensively throughout this, but human agents will increasingly have to handle the exceptions, the complex tasks, the emotionally sensitive moments where human empathy is required, where judgment needs to take place. AI can do a lot of things. It'll help you reset your password. It can help you check your balance. It can help you book a flight, right? But those are standard processes, right? And so I think in some regard, while we're all worried about AI taking jobs, right, in the context of customer experience, it raises, not lowers, the need for training, knowledge management, knowledge access, agent assistance, and quality measurement. And I think those things, when we think about this world of what it looks like when we blend our humans and machines, the people play a really important role, but we've got to think about them differently.
Bob Laliberte
>> Yeah, and I couldn't agree with you more. And I think when you think about it from that perspective, and this has been talked about a lot, and Larry Ellison has talked about this a lot, right? Supervisors must manage a blended workforce of people and digital workers. And so, you're thinking about the fact that you're going to work today, you're not just managing people, but you also have those agents and digital workers that you manage. And so there's going to be a combination of skills that are going to be required for that and how to handle that. And, organizations are going to have to really look at how are they going to be doing their forecasting for resource need, right? Quality management is going to become really critical. I think, coaching is going to be another aspect of it. How do you coach your supervisors to manage an agent? Right. And how is that going to be done? Right. So it's a whole different skill set that organizations are going to need to adapt and be able to bring into it in order to ensure success for these organizations. And then obviously, the capacity planning, right? All these things need to reflect how the work is going to be moving between AI and humans. And like you said, there's still, a lot of good reasons why you need to have that empathy, you need to have that human judgment. And so trying to determine that blend of driving that optimal efficiency between AI agents and humans, because I know a lot of times when I get on and I'm talking through something, at a certain point, I'll just be like, I need to speak to a human, please. Please, please just get me to a human. And so the ability to recognize that and redirect people so that they can have a positive experience is going to be super important.
Zeus Kerravala
>> Yeah, you brought up an important point too with the supervisors, because a lot of these tools are built for the agent and helping them be more productive and helping them be smarter and more accurate. Right. But what about the supervisor? Right. So make sure that as you're rolling these tools out, the managers and supervisors understand what's happening so they can coach better, right? That they can understand where AI is working, where it's not working, right? And I think that's something that doesn't get talked about enough.
Bob Laliberte
>> Yeah, absolutely. And I think there was a lot of great points that we just brought out, a lot of great similarities between the organizations that we heard from today. I think there were also some areas where they differed. So I thought we'd chat about that for a minute or two. Looking at Cisco, you mentioned they're a very large organization, right? Their lens tends to be more architectural, tends to be more governance-led. Organizations like Talkdesk, as you mentioned, right, brings up that end-to-end and industry-specific automation in a lot of cases. Zoom and its partner, right, really emphasize that integrated tools and testing and pragmatic deployment. And, you know, Five9 framed the maturity journey and really provided a lot of customer proof of the value it's delivering for organizations.
Zeus Kerravala
>> Yeah. And while the difference in these companies maybe can, you might think, make things confusing, it's actually a good thing. So I think it gives buyers a context and evaluation criteria that they can use to make a better decision. So the right choice for every organization will be unique to them, right? A lot of it has to do with what you currently have installed, where you're coming from, where you're going, what kind of workflow complexity you need. There's simple, you have a lot of simple interactions, a lot of complex ones, high net worth individuals, low net worth, right? Different industry requirements, what kind of resources you need to implement, right? Does it need to be partner-led or not? And whether the priority is the service efficiency, or is it, customer experience improvement, or is it just growth of the brand, right? Different brands have different needs, and I think it's good to see, a different variety of vendors like this approach it through different lenses, and that's obviously good for everybody, so it creates a bigger pie.
Bob Laliberte
>> Yeah, absolutely. And so, to kind of bring this home, Zeus, what do we recommend for organizations? What's their action plan coming out of this?
Zeus Kerravala
>> Yeah, I think first, and this came through loud and clear, it's choose a journey, right? Don't boil the ocean. Choose a journey, baseline it, document it, and then measure it. And so this includes volume of calls, transfers, handle time, resolution, customer effort, you know, quality measurement. There's a bunch of different things you could measure. I'm saying pick a journey, find the key metrics for that and baseline it and then measure against it.
Bob Laliberte
>> Yeah, I think that's a great way to start. And then I think next what's really important is for organizations to be able to map out those workflows and the processes they have in place, right? Understand the data knowledge, the different systems, any policies they have, where the human escalation points are that are required to resolve that journey from beginning to end. So really document that and have that ready to go so they can work with their provider to incorporate that.
Zeus Kerravala
>> Yeah. And lastly, Bob, I'll say it's important to run a controlled deployment. Don't let this get out of hand. Don't let the scope get too big. I think you want to be able to see where you've been, know where you're going, but then also importantly test a lot of the edge cases. We know a lot of the core business, as you think of the 80/20 rule, right? That most of our interactions are gonna fall in the 80. We'll test the 20 as well, right? And test those continuously. Compare results to the baseline and then expand only when trust and quality hold up. And I think if you do that, you'll have a successful deployment.
Bob Laliberte
>> Yep, absolutely. Look, to wrap this thing up, AI ROI in CX won't be determined by the number of bots deployed. Really clearly, it's going to come from getting to better resolutions, being able to have more capable employees and more efficient operations and responsible execution at scale.
Zeus Kerravala
>> Yeah. And I wanna, Obviously, thank all the guests that we had. It was a great summit, Bob. I wanna thank everyone for watching this. And I'll leave you with this. I think sometimes the new technology, we think, "Well, that's risky to do that." Right now we're in a period of time where I think it's riskier to not use AI than to use AI because the world is moving that way. So move and move with urgency, but do so in a measured, scaled way that can provide you the data, the evidence, and the discipline to have success and ultimately build customer trust.
Bob Laliberte
>> Yeah, I think that's a great way to close. And I think I'll paraphrase Jensen at Cisco AI Summit when he said, "Everyone's talking about human in the loop. It's time to make sure AI's in the loop." And we need to make sure AI is in the loop now in all the processes. Nice.
