What if the most useful measure of workplace technology was not the number of tickets closed, but the amount of productive time returned to employees?
In this episode of Tech Talks Daily, I speak with Kelly Candler, Global Offering Lead for Workplace and Business Process Services at DXC Technology, about moving digital workplace services away from activity metrics and toward employee and business outcomes. Kelly has worked on both sides of the managed services relationship. Before joining DXC, she was a customer of the company and several of its competitors, giving her a practical view of what large enterprises expect from workplace technology and where support models continue to disappoint.
Kelly begins with a simple distinction. Traditional IT reporting often focuses on ticket volumes, response times, and calls answered. Employees care about whether they can do their jobs, how much time they lose, and how quickly they can return to productive work. When the outcome becomes the starting point, support is judged by its impact on the employee rather than the amount of activity generated behind the scenes.
That matters because many companies already have crowded workplace technology estates. Years of investment have produced overlapping tools, legacy applications, automation, device platforms, and service channels. Adding another AI product can increase cost and confusion if it replaces nothing and connects to little. Kelly argues that organizations should understand the technology they already own, identify the employee outcomes they want, and use orchestration to connect those investments rather than discarding them automatically.
DXC positions its Workplace Services offering as a people-centered, AI-native service enabled by DXC OASIS, its orchestration platform for Human+ agentic AI workflows. In the interview, Kelly describes Human+ as a partnership in which AI handles repetitive work while people retain creativity, judgment, and accountability. The intention is to make support more proactive, provide employees with help through familiar channels, and allow experienced teams to concentrate on work that requires human knowledge.
Device performance provides a practical example. In a reactive model, employees discover that a laptop has become slow, unstable, or unusable after productivity has already been lost. Kelly explains that operational data and automation can identify patterns such as declining battery health, storage problems, and application conflicts before the employee experiences a failure. The system may resolve the issue automatically or arrange a replacement before work is interrupted.
Kelly says DXC performs over 82 million proactive checks and remediations annually across over one million managed endpoints. These are company-reported operating figures, but they illustrate the scale at which preventative workplace support is being applied. She expects agentic AI to extend that model by learning from operational data and improving the speed and scope of proactive action.
We also examine DXC's reported outcome figures. The company says Workplace Services can produce a 40 percent reduction in operational complexity, 60 percent fewer service desk calls, resolve 50 percent of device issues before employees notice, and return over 15 hours of productivity to each employee every month. Kelly explains that the productivity calculation considers incident resolution, device availability, PC performance, and onboarding. The actual result will depend on the employee persona, environment, baseline, and method of measurement, so leaders should examine how each figure was calculated before applying it to their own workforce.
The discussion then moves from technology to adoption. Kelly says organizations often know where to run an AI proof of concept but struggle to turn the result into a production capability. DXC's response is an Exponential Blueprint that assesses the environment, maturity, culture, and potential value areas, then helps customers move from a smaller test toward wider use. The wider lesson is that a successful pilot needs a defined route into existing operations, ownership, governance, and measurement.
Governance becomes especially important when Human+ workflows begin making decisions at scale. Kelly notes that human service desk employees already make mistakes, so the standard for AI should focus on risk tolerance, oversight, and what happens when an error occurs rather than assuming perfection. DXC has introduced an AI code of conduct alongside its employee code of conduct. Kelly says humans remain accountable for the actions taken by AI agents, much like the accountable role in a RACI model.
Trust also depends on how leaders explain the changes to employees. Automation can raise concerns about surveillance and job loss. Kelly frames the opportunity as removing repetitive work so people can concentrate on decisions, creativity, and business outcomes. That promise will only be credible if employees understand how the technology is used, what remains under human control, how performance is measured, and where they can challenge an automated decision.
For CEOs deciding where to begin, Kelly recommends support and device management because both affect nearly every employee and can produce measurable results. She also suggests moving toward experience level agreements that connect workplace performance with employee outcomes. The longer-term value comes from understanding the broader environment in which employees work and improving how its parts operate together.
If workplace AI is intended to give people time back, should organizations retire ticket volume as their headline measure and report the employee impact instead? Listen to the episode and share your thoughts.
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[00:00:27] What is it that changes when workplace technology is actually measured by the time that employees recover rather than the number of tickets that IT close? Well, in today's episode, I'm going to be speaking with Kelly Candler, Global Offering Lead for Workplace and Business Process Services at DXC Technology.
[00:00:52] And together we're going to talk about designing AI-enabled support around employee outcomes. And Kelly will discuss how organisations can connect the tools they already own, use agentic AI to identify device problems before work is disrupted, and better measure productivity through resolution time, device availability, performance and onboarding.
[00:01:18] And we'll also examine the limits of eye-catching productivity figures. I think we've all seen some of those online. But instead, we're going to talk about the governance required when AI agents enter daily operations, and why humans must remain accountable for their decisions. So if your service test reports look impressive while employees are still losing hours to technology problems,
[00:01:43] I'm hoping today's conversation will give you a different way to measure progress. And on that note, let me invite you. Let me introduce you to my guest now. So thank you for joining me on the podcast today. Can you tell everyone listening a little about who you are and what you do? Thanks for having me. Kelly Candler, I lead our workplace and business process services offering for DXC Technology.
[00:02:12] I've been at DXC for a couple of years now. But prior to coming over to DXC, I was actually on the client side. So I was a customer of DXC, a customer of many of our competitors over the course of many, many years working in IT. And I would say over the past probably seven to 10 years, I've spent a significant amount of time focused on a workplace and honestly how to make the workplace better in terms of helping employees get their job done.
[00:02:41] So that's really been my focus for several years now. And I am based in the Richmond, Virginia area and have been here for about 10 plus years. Awesome. It's a pleasure to have you join me. Over the last, what, 11 years, I've spoken to so many different people from DXC. And you describe yourself as a people-centered AI native workplace service. Now, so many big changes in the world right now.
[00:03:06] But I've got to ask, from your viewpoint here, what is it that changes when things like employee outcomes rather than tools or ticket volumes, when that becomes the starting point? What changes there? I think the biggest change is we stop measuring IT success, right? Or success by an activity. And we start measuring it by an impact. And so if you think about it, right, traditionally organizations tend to ask questions like,
[00:03:36] okay, how many tickets did we close? How fast did we answer the phone? And at the end of the day, employees, all they care about is, can they get their job done, right? Can they get back to work faster? And so they don't necessarily care about how many tickets they close. They actually don't often even care about whether you, quote unquote, open a ticket for them or not. It's really about the impact that we can have on them. And so when employee outcomes become the focus and the starting point,
[00:04:06] that goal shifts from managing technology to really enabling them to be productive, which is key. Such a great point. And I think because for years, organizations have added more workplace tools in pursuit of productivity, I've got to ask, as this is a human-focused enterprise tech show, why do you think the future of work is less about adding technology, especially in this AI era that we find ourselves,
[00:04:35] and so much more about removing friction from the employee experience? Because this is exactly what this show is about. It was so refreshing to hear that you're passionate about this stuff too. Yeah, I think, you know, it's, again, when we think about most of the organizations today, and again, if we think about kind of over the decades, right, of providing these types of services to large enterprise companies, we've often kind of taken stepped changes, right,
[00:05:05] whether that's, you know, in outsourcing, outsourcing, whether that's in how we automate as much as we can, whatever the case may be. But through the course of many, many years, most organizations have really kind of, they have a pretty crowded, you know, workplace technology platform or stack, if you will, right, where they've got a lot of different pieces already in place. And so our goal is really to look at what companies have, understand what they're using,
[00:05:34] understand ultimately, right, what is the, what are these employee outcomes we're trying to achieve? And so our goal is to make those existing tools work better together and really make sure that we can capitalize on investments that the companies have already made and really leverage Egentic AI to be the orchestrator across those investments that have already been made. So again, it creates our ability to kind of leverage what's already there and honestly level that up.
[00:06:03] Yeah. And I've been to so many tech conferences this year to all have that heavy focus on all things Egentic AI. That's the one topic that's dominated this year, especially with the introduction of agents in the workplace. And of course, we already have one eye on what that workplace might look like in 2027. So I'm curious, what do you think the most successful workplaces of next year will be defined by their,
[00:06:28] will it be their AI tools or how they combine human expertise and AI agents to amplify employee potential? How do you see next year playing out? I can't believe we're talking about this already, but I'm curious on your thoughts. Yeah, I think it's a great, it's a great point because I think everyone right now is, to your point, AI, AI, what can we do with AI? How do we get it in here to really take advantage of it? And so I think as we look at next year,
[00:06:56] I still think there will be a focus on how companies are leveraging AI, but I think it's really, at the end of the day, the strongest will really focus on how they can balance that human expertise with the AI capabilities they bring to bear. And then tying to, you know, the point I just made earlier around, how do we then maximize those investments that actually already exist in their environment? So it's really,
[00:07:25] I think those pieces coming together, right? Take those existing investments, leverage AI to really connect those dots, but then it's how do you put that human element into it? Because humans should always remain accountable and in the loop of the processes. And so it's how do you make sure that we blend that in there seamlessly? And before you joined me today, I was reading our Enabled by DXC Oasis, the intelligent orchestration platform
[00:07:55] that is helping to transform managed services through human plus agentic AI workflows. Workflow services helped customers anticipate employee technology needs, reduce productivity disruptions, and accelerate issue resolution. And one of the things that big really stood out to me was I think it was a 40% reduction in operational complexity, 60% fewer service desk calls, and 15 plus hours of productivity
[00:08:24] returned to every employee every month. Incredibly striking stats there. But just for anyone that's not seen that, how was it measured and what should leaders examine before applying a figure like that into their own workforce? Because there's such a big focus on all things, business outcomes, measurable value, etc. And it seems you guys are ticking all the right boxes here. Yeah, no, I appreciate it. It's a great question because I think a lot of times, you know, if we pick on like productivity,
[00:08:53] it's always kind of this like squishy nut trick of, you know, there's not a lot of confidence in how to measure it. But one of the things that we've done, you know, over the past couple of years is we really drilled in to understand all the different pieces that go into an employee kind of, one, losing that productivity through various issues. And then how do they, how do we help them gain that back? And so the way that we measured it is
[00:09:23] what we would find, I would say typical customers are going to expect between 18 and 15 hours per employee per month of recovered productivity, kind of depending on what that persona profile looks like. But essentially, we took different pieces, right? We measured how fast was the incident resolution. As I mentioned earlier, right? Ultimately, employees, yes, they care that their issue was resolved quickly, but how can we do that in a way that's simple for them as well? So we really measured that piece.
[00:09:52] The other piece we measured was just, frankly, PC availability. It's one of the biggest pain points, particularly in large enterprises, is managing PCs in a way that ensures that they're constantly productive for the employee. They're not having to take downtime. And so we measured that. And then we coupled that with a measurement around PC performance, right? And really targeting at least 5% better PC performance.
[00:10:21] And what did that look like? When did we see that go up? What were the changes made? And how do we do that? And then ensuring that we can measure that outcome. And then I would say the kind of a fourth piece was faster onboarding. And that was really looking at as employees take on, as companies take on new employees, do we have a seamless way to ensure that they're getting onboarded? Because that's been one of the biggest pain points that I think in many large enterprises is just how do you ensure that an employee can be productive on day one?
[00:10:51] And I would say, honestly, we're, all of us, right, are still working to solve that problem and help our customers to solve that problem because it's constantly changing based on the environment. But I would say those are some of the key things that we measure to get to that 15-hour kind of target, if you will. And it's really getting into the details and putting strong visibility data behind that versus, again, kind of like I said, that squishy metric that sometimes, you know, is hard to measure,
[00:11:20] but we can really back that up with strong data. And another striking standout for my research on all things DXC and the workplace is the evolving relationship now that we're seeing between employee experience and business performance. How do you see that continuing over the next few years? Do you think productivity will be measured differently in this AI era we find ourselves? How do you see this continuing? Yeah, I think it's a great point because, you know,
[00:11:50] I feel like for years, for many years now, right, there's been a conversation around employee experience at our clients. But what I find is, you know, oftentimes, you know, many companies will say they want a better experience, but they struggle to invest in it. And reason being, right, ultimately, you've got to make business decisions. And sometimes employee experience is the one that gets kind of pushed down in terms of when they've got a budget to manage to and where they want
[00:12:19] to make their investments. And so I think what we're going to see over the next few years is the natural progression of employee experience getting better and better because of the investment in AI and that direct link to business outcomes. And so, again, it's not just can I make the experience for the employee better, but I'm actually creating a more consistent capability for those employees to ultimately
[00:12:49] get their job done. And if you can link that in a more streamlined way to business outcomes, now you've got that kind of connectivity. And one of our main goals is to make sure that we don't compromise, right? We look at how can we save the business cost? How can we ensure that we drive those outcomes for the business? And if we do that in the right way, your employee experience will get better and better over time. So I think that's really where I see those coming together and kind of being different than maybe the way it's been looked at
[00:13:18] over the past several years. I don't expect you to name any names here, but just to bring to life everything that we're talking about, are you able to share maybe a customer example where anticipating an employee's technology needs actually work better than waiting for a service desk request? Yeah, absolutely. Look, I think and I mentioned earlier PCs and managing PCs in very large enterprises is, sounds super easy to do, but it's pretty complex
[00:13:48] based on the different legacy applications and types of PCs and types of work and all of that good stuff. And so the most simple example is device performance. And I think in many organizations, employees are the ones that discover the issue on their laptop, right? It either becomes slow or it crashes or it's just not helping them anymore, right? They no longer can get their work done. And so at that point, the employee has already lost
[00:14:16] their time, right? They're already not being productive. And so with an agentic approach, you know, agentic AI approach, this system and what we put in place from that perspective can start to detect patterns, right? We've got tremendous amount of data and tremendous amount of automations. We can detect patterns and we can see that problem starting to creep up before it starts to impact the employee. So if you think of things like
[00:14:45] declining battery health or maybe storage issues or honestly, the biggest one we often see is application conflicts, right? Where you've got a lot of legacy applications in the environment. So we use that to really get ahead of it, right? And we can resolve that issue automatically or if we need to, right? Schedule a replacement before that productivity is affected. And I would say that's one of the biggest areas where I see agentic AI playing a massive role and it's something
[00:15:15] that's to me a low-hanging fruit that we can start on now. Right? Because it's really our focus is making sure that the best support interaction is one that actually never has to happen. It's incredibly cool and I suspect we will have many people listening from organizations that are still operating in reactive support models, constantly firefighting, et cetera. So how close are we to reaching that point where technology issues are largely resolved before employees even noticing them?
[00:15:44] Because listening to you there, it sounds like we're very close if not there already with the right tools. I do think we're there but I think we're there at kind of the foundation, right? So for DXC, we record around 82 million plus proactive checks and remediation annually and that's across about a million plus managed endpoints. And because we've taken time to really focus on how do we put the automations in place to get ahead of that.
[00:16:14] So I think we're there in some of those basic things where I think you'll start to see things really leapfrog is as you then blend in more agentic AI capabilities and that agentic AI can drive continuous improvement, right? It can use that data to be able to even get faster at those proactive fixes and really I think take it kind of further than the device example I share. The device example again is a critical one but as you take that further and think about
[00:16:44] all these legacy applications that companies are really struggling with I think that's where we can really start to see significant change over the next, honestly really over the next 12 months. Yeah, 100%. I completely agree especially around those incremental steps get it? right and then move on to something else and we have mentioned agentic a few times today. It's the theme of 2026 but at the same time again we'll have people listening inside organisations that have a crowded
[00:17:13] workplace technology stack shall we say to be diplomatic. So how can people in those organisations introduce agentic AI without that fear of just adding another layer of complexity for employees and IT teams? Because very often it's not even about the technology it's ensuring the culture and everything is right too, right? Yeah, I think it's a great question. It's a common one that we often hear and again I think it ties back to what I shared earlier around helping, like we want to help our clients
[00:17:43] how to maximise the investments they've already made and then use those AI capabilities to really orchestrate across those investments versus bringing in something completely new and different that supersedes investments that you've already made. So it's really how to bring those two worlds together. I think the other thing that DXC has put together, noticing that that has been a real struggle of where do I start and how do I take things
[00:18:14] to scale? We have put together a blueprint, we call it our exponential blueprint, but it is really focused on how we help our clients to understand the environment that they're in, understand the maturity of where that environment is, understand culture to your point and how we're going to bring that into the environment or bring AI into the environment in the best way, but then really start small, figure out where those
[00:18:43] key value impact areas are, address those value impact areas are in a small way, right, in more of like a prototype POC type way, but then the blueprint helps us to define how you then take those positive results and you scale that quickly. So it's a key capability that we've put together from a DXC perspective to really bring that to light because I would say that's one of the biggest conversations we have with our customers is,
[00:19:13] hey, this is all great, but I can't seem to get out of POC mode, right? I can't seem to scale using that exponential blueprint really one, helps our customers understand where to start, but then two, how to approach it in a way that gets you from a proof of concept to an actual scalable production outcome. Incredibly cool and I love the unique vantage point you have here. So on that side of things, I love to ask
[00:19:43] you a two-part question here. The first part is what do you think will be the biggest difference between today's digital workplace and the workplace employees will experience next year, but also are there any workplace trends that maybe everyone's getting carried away with this year but could become irrelevant next year? So a bit of a both sides of the table here, but what do you see next year? I would say over the next year, and I've kind of shared this from a workplace perspective,
[00:20:14] you know, to me, support is like a thorn in everyone's side, right? You need it, but it's not always the easiest, right? Again, in these large enterprises, it's not always the easiest to get that support and at the end of the day, we all don't want to call to get an issue fixed. And so I think the biggest difference we're going to see between today's digital workplace and then maybe what employees will experience a year from now is that
[00:20:42] shift to one more proactive support and more than they've seen today. And then I would say the second is if they do need support, honestly, a simpler way for them to get that. And what I mean by that is if they want to continue to pick up the phone and call someone, they can still do that. But I think what they're going to experience on the other end is an agentic voice agent that's going to answer that call and be able to
[00:21:11] address their issue quickly in a more consistent way. If they want to continue to chat for that help, they can do that as well. But again, I think on the other end is going to be an agentic agent that's going to really reduce their downtime and help them get back to work faster. And we, you know, DXC has invested in that focus and we've got a product that we launched this summer that is really focused on exactly
[00:21:41] that to make that transition because we know there will be continued needed support, again, based on these very large legacy complex environments that our customers are operating in. And so I think that's going to be a key difference for employees is not just how they get the support but the path by which they receive it. And then I would say I predict a very strong increase increase in that proactive support which ultimately is my endgame, right? You shouldn't
[00:22:11] need that support desk right in a few years from now. You should simply get your issue fixed before you even know it's happening. And that's really our vision and our goal. And I would say to the second part of your question around kind of trends receiving a lot of attention today that maybe will shift moving forward, you know, I think we see a lot of, I would say probably one of the biggest things we'll see is really that shift around how to elevate capabilities
[00:22:41] in the different chat AI assistant supports that we get today. And what I mean by that is we've got a ton out there. Every employee is using something different. And so I think what you're going to see is how do you manage them in a more clear and governed way? But then also how do you blend that in further into the employees' work environment to not just make them like a little bit productive, but really dramatically change how they actually work? And I think
[00:23:11] that's going to be kind of a, it's such a huge opportunity and I think we're just still learning. We're, you know, for a lot of employees, they have to change their behavior. And so it's how do we help them through that process? So I think it's a complex one, but I think it's one that could dramatically change how employees work. And that's why I think, quite honestly, the workplace is ripe for change with the agentic AI capabilities that we have today.
[00:23:40] And that we're going to see as we move forward. And there will be people listening, leaders listening, that scroll on LinkedIn and presume that every competitor seems to have everything working with AI. There's public displays of AI theater going on. Yes, comparison is a thief of joy and very few ever post about their struggles and we all know there are many. So based on the conversations you're having plus what you're seeing and hearing out there, what are the hardest implementation problems
[00:24:09] when human plus AI workflows move from controlled pilot phases into day-to-day operations? Anything you're seeing here? Yeah, I would say the biggest thing is probably ensuring that companies have the right governance in place to operate at that scale and they have the right kind of security structure in place to operate at scale. Because the change is happening
[00:24:39] whether your cybersecurity team wants it to happen or not, the change is happening. So it's really partnering to understand how do we govern this in a way that makes sense and how do we manage from a security perspective properly? And, you know, I'll just give you a quick example. If you think about, again, I talked a lot about the support like the service desk. If you think about the service desk, many people will say, well, when I move to an agentic service desk capability,
[00:25:08] they should never make mistakes. But again, if you think about in today's world, you have humans running that desk and they make mistakes. And so it's how do you balance that and what's your level of risk tolerance, what's your level of governance to ensure that those mistakes are less, but you've got the right structure in place to manage if something does occur in a way, again, that doesn't have a negative business outcome. And so I think that's going to be a key piece for businesses
[00:25:37] as they scale is having that right balance without it impacting the ability to do its job. Another word we've got to cover when talking around AI in the workplace is trust remains one of the biggest barriers to AI adoption. Some employees hear automation or AI worry about over monitoring or even job losses, etc. So where should a human remain responsible when an AI agent diagnoses
[00:26:07] a problem, takes action or makes a mistake? And possibly even more important, how can leaders ensure that they earn that trust when they're talking with their workforce? Yeah, I think this is a great question because I think there's often an assumption, right, that, you know, to your point, right, of job loss because AI is taking over. But I think if you really start to get into the details of how AI can help the business,
[00:26:36] it's really removing, you know, as we talked about earlier, it's removing that friction, right, that slows employees down. And so it's taking AI and bringing it in and saying, hey, AI is going to handle the repetitive work so you can focus more on the work that matters to deliver on the business outcomes we're trying to achieve. And so from a DXC perspective, we really look at it as augmenting people and not necessarily replacing them, but really we have a,
[00:27:05] we put in place an AI code of right? We have a people code of conduct and many companies do, employee code of conduct. We've put in an AI code of conduct because again, we're going to be working in partnership with these agentic agents in our workforce. And so really making sure we've got the right structure in place and we make, with that code of conduct, we make it explicit through our human plus commitment where the technology is amplifying human expertise, create,
[00:27:35] you know, creativity and judgment and it's not replacing. And so I think it's going to be super important, you know, again, as leaders talk to their employees to help them understand that distinction as we see these changes. Because again, the changes are coming, we know they're coming, so it's how do you ensure that you've got the right approach and structure in place to bring that human plus commitment, as I mentioned. I'll just share the other thing that I was chatting with a colleague the other day on
[00:28:05] is, you know, when you think about a typical racy, right, when you think about roles and responsibilities, responsibilities of different people when you're working on an effort. In the world of AI, at the end of the day, humans are that A, they are that accountable person or group of people that ensure that AI is doing what it needs to do. And that I don't think is ever going to change. And so it's really how do you bring that into the mix,
[00:28:35] but again, create that human link and blend those properly to kind of maximize your output. And finally, if we have a CEO listening, maybe they're planning for 2027 right now and they want better employee experience, they want to lower support costs, but also have that ability to show very real, tangible, measurable improvements too. What should it change first? Which metrics would show that the work is helping people rather than just closing tickets faster?
[00:29:04] I appreciate this is probably a question that is worthy of an entire podcast episode on its own, but any quick tips for that CEO? Yeah, I would say one, if we really shift to measure what we call experience level agreements, right? So it's shifting from those, as I mentioned at the start, right, those IT metrics to true employee outcomes. And that's what DXC does best is we will sit with you and we will help you understand your environment and
[00:29:33] how to define what those are because I think it's unique to every environment. But so I think to the question, I think that's going to be important to show that value impact. But to me, I think one of the biggest areas of investment is I think your support space is your low-hanging fruit, right? And if you can get the ability to bring that orchestration in, it's an area to really start and show true value impact quickly. And I think you combine that with your device space. And to me,
[00:30:03] those are the two most basic things an employee needs in the workplace, you can show some really quick impact by focusing on those two areas. But what I think a CEO would want to do is start there, but then really understand that broader ecosystem that an employee works in and then start to define where do you take it from there? Because that ecosystem is where you start to really get the benefit of how all of those pieces work
[00:30:33] together in unison. That's where that employee's productivity gets dramatically better. That's where their experience really levels up and that's where your business outcomes start to really increase because you have all those pieces working together. There are so many big takeaways for me and so much I love about what you're doing here. Yes, it's an enterprise tech podcast, but I love how DXC's workplace services are designed around people. I think that often
[00:31:02] gets lost in the technology conversation and also orchestrated across existing investments, which will be music to the ears of many CEOs and CFOs, but also this ability to be powered by human plus agentic AI. So many big takeaways. Anyone listening wants to find out more about anything that we've talked about today? Where should they go? Hey, I think our best place to learn is on our DXC website. We've actually taken some time to really reshape
[00:31:30] kind of our, you'll see our new, our strategy and our focus on everything that I've talked about here today. And then you can also follow us on LinkedIn. We share regular customer stories and insights and developments around the transformation that we're driving because it, you know, it never ends. And so those are kind of some key areas that we'll be continuing to share all the great work we're doing with many of our customers. Well, I think over the last few years there's been more than a few stories around the struggles with return on
[00:32:00] investment from some of those expensive AI projects, but we named a few stats earlier. I'll just repeat some of them again. 40% reduction in operational complexity, 60% fewer service desk calls, 50% of device issues were resolved before all their employees even notice. And then we've got the 15 plus hours of productivity return to every employee each month. There's some metrics we can all get behind. So everything that I've mentioned there included those stats and the research behind that, I'll include links to
[00:32:30] everything. I urge people to check that out. But more than anything, Kelly, thanks for bringing all this to life today. Really appreciate your time. I appreciate your time. This has been a great discussion and thank you so much for having me. I think Kelly's argument today gives leaders a useful test for every workplace AI project. Does it reduce friction for employees and does it improve business outcomes? Or does it simply produce another impressive IT dashboard? Starting with support and device performance
[00:33:00] makes sense though because they affect almost everyone. And provide measurable signals such as downtime, resolution speed, availability, and onboarding time. So the harder work begins when pilots enter daily operations though. And yes, governance, security, risk tolerance, and human accountability. None of these things can come later on down the road. And I appreciated Kelly's reminder that existing tech investments should work together
[00:33:29] before another product joins an already overcrowded stack. So a big thank you to Kelly for joining me today. Remember, you can find more about DXC workplace services, DXC Oasis, and that Human Plus approach through the links in the show notes over at techtalksnetwork.com. But over to you, which employee outcomes would tell you if workplace AI is genuinely improving work? What do you
[00:33:59] measure? Again, techtalksnetwork.com. Remember, I'm at lots of tech events and conferences up until Christmas. If you are attending any of them, they are taking me from Orlando, Vegas three times, I think, and San Francisco. I'd love to hear from you. But that's it for today. So thanks for listening as always. Bye for now.