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Zero Ticket: Myth or Reality?

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Notes

As organizations race to adopt AI-driven operations, many IT leaders are asking whether “zero tickets” is a realistic future or simply an industry myth. In this episode of The Hitchhiker’s Guide to IT, host Michelle Dawn Mooney sits down with Jaro Tomik, Chief Technologist and Digital Enablement Expert at CDW UK, to explore what zero-ticket IT operations really mean—and how enterprises can balance automation, human oversight, and business outcomes.

With years of experience helping global organizations implement AI, automation, and digital transformation strategies, Jaro brings a pragmatic perspective to the hype surrounding AIOps. He unpacks where automation can truly eliminate repetitive tickets, where human-centered design remains essential, and how IT teams can position themselves to deliver business value rather than just firefight.

Key topics include:

  • What “zero tickets” actually means—and why it’s more of a journey than a destination

  • The role of AI and automation in reducing manual workload while improving user experience

  • How to integrate human-centered design into AIOps initiatives for long-term success

  • Why IT leaders should focus on outcomes over ticket counts when evaluating automation success

  • Common misconceptions about zero-ticket IT and what realistic goals look like for enterprises

  • The importance of aligning service management, operations, and business priorities

Whether you’re a CIO planning your automation roadmap or an IT operations manager evaluating AI tools, this episode provides an honest look at the myths, realities, and opportunities of zero-ticket IT.

Transcript

Welcome to the Hitchhiker’s Guide to IT, brought to you by Device42. On this show, we explore the ins and outs of modern IT management and the infinite expanse of its universe.

So buckle up and get ready to explore the ever changing landscape of modern IT management.

Hello, and welcome to the Hitchhiker’s Guide to IT, where we explore the latest innovations shaping IT operations and infrastructure. Today, we’re talking how many human centered designers does it take to implement AI ops? And we have a great guest to have this conversation with today. Jaro Tomik is chief technologist and digital enablement expert at CDW UK.

Jaro, thank you so much for being with me today. Really appreciate you being here and taking the time because, I will share this personal little, snippet that I found out about you. You are a soon to be new dabb, so I know things must be especially crazy for you. So I know it’s it’s separate from business, but I am happy that you’re here nonetheless.

Yeah. Thank you very much for having me.

Yeah. And I wanna learn a little bit more about you, Jaro, and would love for our audience to learn a little bit more about you as well. So can you give us a brief bio before we start off, please?

Sure. Absolutely.

Yeah. So so, yeah, like you mentioned, yeah, Tomik. I’m, chief technologist for what we call digital enablement at CW UK, which ultimately covers two key areas, enterprise service management and intelligence automation. And my job is to continuously research the market, identify the vendors that are sort of up and coming where we’re seeing a lot of value, in our customer environments and also speaking to those customers and and trying to, kind of see if, all the things that marketing are publishing about those products are actually reflected in the real world environments and then sharing that information back both internally and with our customers. And that way, hopefully, helping our customers to navigate the market faster so that they can get to, their time to value in a in a quicker way.

Perfect. Let me start here. When we talk about data quality and process optimization, why is data quality still a stumbling block for many organizations trying to deploy AI?

Yeah. I mean, if you don’t want your AI to hallucinate, most listeners, I’m sure, are already aware.

You know, you you kind of have to have the right data to back it. Right? That that’s the key key building block.

The difficulty we’re seeing in the market right now is that organizations still are not really sure what’s out there. They simply just don’t know, what devices they have, what is happening across their infrastructure, what are their people doing, and, all of this data that’s missing or it’s not visible or it’s not connected to the right sources, is then causing a potential for, not just AI to hallucinate, but even security vulnerabilities.

And it also makes it much more difficult should something happen to bring things back to, be working again. And and that’s that’s where data is kind of the the new oil as they call it, right, or gold or whatever you want to whatever, equivalent you want to use there. It really is becoming the key asset that the organizations have, that they need to really cherish and try to get the most out of.

So in your experience, and you have quite a bit, what process gaps need to be closed before automation or AI ops can really be effective?

What you’re what you’re having there really is, the process gaps are not necessarily something to do with, for example, ITIL as a best practice or with DevOps or with agile. It’s it’s more to do with the process potentially not being updated for a very long time. I would actually say it has a lot to do with not having proper accountability assigned to managing that process and also to not having a continual improvement in place. What that means is that organizations have had the same processes for a very long time. And even when they try to change it, there is a lot of cultural resistance to it, which then ultimately means that they, they people go back to their old ways of working, and, again, they keep data in their heads, all their knowledge in their heads. They’re not sharing it on the platforms, and they’re encouraged to share the data.

They are, you know, not using the data available to them if it’s out there. If we’re creating a knowledge in the organization, quite often is somebody who’s very enthusiastic about it, and their knowledge articles might be just a tad too long. Or for others, it’s something they have to do, they’re forced to do, and they’re making them too short or that so there’s kind of lack of standardization of the language and everything else. And, at the end of the day, there is stats like only seven percent of knowledge articles ever get read by humans.

Right? And what’s exciting about the world that we live in right now is that, with the with the, entrance on, of AI onto the IT scene, what you’re seeing is, is is quite a change in the way we can now leverage that data. Okay. Only seven percent of humans ever looked at it because let’s face it.

You know, if you have some sort of, like, issue, do you always go to frequently asked questions or, you know, you just try to find the fastest way to do it. And usually, that’s just restarting your laptop like you may have done just before this podcast. Right? And, you know, and you try to resolve it yourself.

You didn’t necessarily go into your if you using, you know, some sort of Freshworks or ServiceNow or some sort of other service management tool, didn’t go in there, look into the knowledge base, and be like, okay. Let’s follow these steps. That’s not naturally human thing today. So what you try to do is is you you try to resolve it yourself.

And then at the end of the day, that that means that IT might not even be aware that you have restarted that laptop. It might never get reported that you have to do that every single day, and they’re unaware. They don’t have that visibility.

But nowadays, what’s changing is there’s now more and more modern tools that are able to capture all of this different data, bring it together, and it’s no longer just the humans that have to process it, which tend to be very long and complicated and and, and boring in many cases because it’s just too much data to process. So what we’re looking at is is the systems doing all of that processing for us. And that’s where we start talking about applying artificial intelligence onto operations. We’re using those large language models, train them, on our organizational data, and then they’re able to pretty much predict, in the end what is about to happen before it even impacts the end users. And like with you, Michelle, right, if your if your laptop needs continuous restarting, it’s able to capture that data and say, hey, Michelle. You’ve had your laptop now for four years.

How about a change? Right? Would you be open to getting a new device, or do you want us to have a look at it? Which means you didn’t have to do something yourself.

And this is where we’re getting towards what’s beautiful called zero ticket future, where it’s not zero ticket from the point of view of the agents. In fact, they’re probably getting more tickets, but it’s from the point of view of the person benefiting from the service. So as an employee, you don’t have to raise a ticket in an ideal world anymore because the systems know you’re having issues, and they’re solving it for you.

And you’re so close here. I’ve actually had it for five years, so I probably should be getting one of those messages.

It’s a build CDW supply chain, so, you know, we can all insert you out.

Well, I wanna dive a little deeper. You talked about processing, and that is so key with what we’re talking about here today. Can you share a real world example where poor data or process readiness hindered success?

I I guess, you know, you you find that you find that those examples kind of all over the place just because what previously has been perceived as a challenge with with process, as a humans doing their work, now is seen as hindering the technology doing its work.

And and I think that shift has suddenly uncovered a lot of areas where we’re where we’re seeing problems. So even if you look at something like joiners, nevers, leavers processes, so you’re trying to onboard somebody in an organization.

And, traditionally, you know, if we look at the way things have been done and hopefully are no longer done this manually anymore, but, you know, in the most manual way, you would have a recruiting manager, they got a verbal accept from, from the the candidate, and now they’re trying to onboard them. And they have to go manually, so send an email or call up IT to get their laptop organized, to get their phone organized. They need to reach out to facilities to make sure they have got a dedicated desk in an office. They need to reach out to HR to make sure they’re set up on the system. They need to reach out to finance to make sure they get paid, through their payroll. They need to potentially reach out to legal to make sure every all the contracts and everything is is correctly signed and, and validated.

And, this is where this is where, you know, of so much value leakage happen as we call it. So any value leakages, any sort of unnecessary, utilization of resources that can be human resources, money, time, whatever else. Right? So there’s value leakages happening because we’re not only does the recruiting manager have to reach out to these people, get it sorted, but more often than not, it doesn’t get sorted straight away, and they need to call back and or email back and say, hey.

How is it going? This person starting tomorrow. I still didn’t see they have their laptop ready. I didn’t see that they have all the applications that they need to have.

Didn’t see them set up on active directory or whatever else. Right? And they are almost responsible for making sure that happened or their their new manager is doing that. Right?

As their the the the new manager of the person being recruited. So to now that we have much more systems driven, way, we’re able, through that recruitment manager, filling out a single form that then automatically sends multiple tickets that are being monitored on the system. And that recruiting manager is then able to see that overall Gantt chart pretty much of where things are, who is holding things back. And not only that, if we scale this up and we look at, let’s say, organization hiring thousand, twenty thousand employees a year depending on their size, right, and churn, suddenly you’re sitting on a lot of data, and you can very clearly see where the bottlenecks are.

And and I would like to think that joiners, movers, leavers because of its complexity and its cross departmental.

It it when once you start investigating that process, you very quickly identify how good or bad the organization is at, dealing with their data and communication, across the different departments.

I wanna talk about zero ticket future. For those unfamiliar, what does zero ticket really mean in a practical sense?

Yeah. Like I mentioned earlier, what we’re really talking about is that future where, the end user does not have to raise a ticket. And this is quite interesting because many, years ago, that still meant it meant that serve we’re trying to have as few tickets at service desk as possible. So what the service desk departments were trying to do is to set up various portals and, like, your own set up various chat bots. And, of course, if you as an end user go onto a portal and you have an issue with your laptop let’s just come back to that again.

So your five year old laptop is, you know, at the at the brink of collapse. So you’re like, okay. I’m desperate now. I need to reach out to IT.

And you try to call them, and there’s no more phone lines in some organizations. Right? You try to email them. There’s no more email lines to IT.

So unless you’re in the office and you can grab that person by their color, you know, what you’re what you need to basically do is to go into a portal, company portal, where the idea is that, you know, the the end users will be, will have twenty four seven access to a service center where they will be able to log their issues, and it will get resolved in a timely manner, which gives the service desk time to organize themselves, time to monitor everything, time to prioritize everything, and, and deal with it in in the right way. The challenge with that is you as an end user, Michelle, you go onto that portal, and it it asks you, do you have an incident or do you have a request?

And you’re like, what does that even mean? Well, kind of both. I’ve got an incident because my if if you even if you know what it means. Right?

I’ve got an incident because my laptop’s not working properly. At the same time, I really want a new one because I feel like this one is five years old and I kind of do a new one. Right? So where do you click?

And then once you click through that, and you’ve kind of decided which pill you want to take, whether the red one or the blue one, you know, then then you suddenly get more and more questions. And it’s like half of those questions don’t even make sense to you as an end user. And that’s because typically no one’s even spoken to the end users. Do you know what that means?

Do you know? There is no human centric design, really. This is what we’re lacking. So, you mentioned the theme, at the beginning.

Right? If we if we don’t really communicate with the with the end users and we don’t validate with them that what we’re providing them as part of our service actually makes sense to them, we’re, again, back to getting the wrong data. People who wanted new laptops did not realize that that’s request, and instead they clicked on incident. It’s already wrongly categorized, and it’s taken the agents extra time to recategorize, and they will just try to fudge the fields where they don’t know what to put in.

They will just type in random characters, and and we’re getting more and more RAM data. And it all stems from the basic process not being communicated well with the end users. With that human centered design not being applied, meaning we’re getting wrong data. That data is then absorbed by the automation tools, by the AI tools, and we’re getting more and more hallucination.

And and especially on an organization where this happens at scale, it’s very difficult to get towards that zero ticket future. What we really want to see instead is that solid base of data and be able to not just, shift the end users left, in the sense of deal with it yourself on a portal. But instead, what we want to see is the IT being much more proactive or even predictive in the sense of utilizing the technology around them, being able to see what’s about to happen, what’s about to break, if Michelle is about to have a breakdown, right, and try to prevent that from happening. That really is what what zero ticket is about.

It’s applying that human centered design and being as predictive as possible to make sure that the end users are as productive as possible. Because at the end of the day, that’s the value IT should be bringing to the organization. That’s why they get paid. That’s why they’re there.

That’s why they haven’t been completely outsourced. Because ideally, they know about the organization, so they should understand the culture. They should understand the technology, and they should be able to support it adequately and ultimately provide as little frustration through the delivery of their services as possible.

And when you said Michelle’s having a break, I honestly didn’t know if you were referring to me or my computer, but it could be both. I mean, as we we’ve all been there, and it’s definitely a par for the course when you have the the emotions of dealing with IT. So you mentioned predicting.

That was a kind of a keyword that I pulled out there. So a follow-up to this. What role does predictive self healing play in achieving that goal?

Yeah. We we’ve kind of for a while, since I would say the eighties, really, the industry’s been trying to since since ITIL has been, written for the first time in the late eighties. Since then, we’ve been trying as an industry to go from reactive, so waiting for things to happen and then we deal with them, to proactive, meaning that we were trying to be kind of ahead of the end users and try to fix things and try to prevent reoccurring incidents, from happening and so forth. But in the end, the IT departments ended up being typically so lean and under resourced, which is the other way to just talk about being, that they usually were so consumed by the work on the reactive stuff.

They did not have the time to do things like problem management and start preventing incidents from happening altogether. They were not able to do root cause analysis. And even if they were, they were not able to action it. And and this is this is so we kept going back to the reactive state.

This is where we’re still seeing a massive amount of organizations in a very reactive state when it comes to their service delivery.

What we’re now looking at, though, is the potential to utilize the technology available to us, like automation, AI, data we have in our organization to our benefit so that we can ultimately, look at the, the different datasets that we have from all these different places within our IT infrastructure.

So we can have it from our servers, from our networking, from our end user compute, our end user feedback, the the ticket data I was talking about earlier. So all of those different sources of data, And we we bring them together and let them be processed by by the AI machine. What we’re then getting is correlation of the data. And and the AI can look very quickly for compared to humans, very quickly at various patterns.

And once it’s identified once it identifies the patterns, not only is it able to tell us and predict ultimately, predict, what is about to happen, but also because it now knows that our organization has a certain rhythm, it’s able to also identify, oh, this thing is slightly out of rhythm. Your heartbeat is not the way it should be. Right? Your blood pressure is not the way it should be, if we if we translate that into more like a health care, metaphor.

So and these systems can then raise a ticket. And this is where I was talking about the, services agents suddenly having more tickets. It’s because these AI, tools are able to raise a ticket. But the the huge difference is it raises one ticket and that ticket that hopefully gets resolved.

If we don’t deal with the problem, if we don’t have the AI tools, if we don’t have the data, this challenge, this problem, this incident trickles down through to the end users, and then suddenly you have five thousand Michels raising tickets.

And that is really the challenge we’re facing right now in IT. It’s it’s just drowning in tickets because things are not getting result proactively and definitely not predictably in vast majority of businesses. So I think there is a huge opportunity in the market to to make that shift and utilize AI to our advantage.

So I’m sure this is something that you get asked about a lot because there is that kind of fear of AI taking over when it comes to human roles. So what’s your take here? How can human centered design help ensure automation enhances rather than replaces human roles?

Yeah.

We we hear that a lot. It’s massively discussed, right, all over the place, not just at conferences that we have as technology people, but also in the media. And and, of course, with any change, the natural state of human is to be on alert or even go as far as fear.

And I think I think there’s, you know, there’s obviously AI optimists, and I’m probably more on that side, to be honest. And then there is the AI pessimists of like Skynet will happen and we’re all gonna die because the Terminator will eliminate us all. Right? There is there is those extremes, but majority of people are somewhere in between either waiting it out and see what happens or tinkering about with the technology and and kind of seeing seeing what’s going on. I would say that the key thing there to realize is it’s not AI that’s going to take our jobs.

It’s the people who use AI effectively that will take our jobs.

And I think that’s the key mindset shift that needs to happen is, there’s this thing called prompt engineering, which all that means is we should really encourage all of the different people in our organizations, in our families, our kids. Right? We should encourage them to to play about with these tools, to really try things, throw things at it, challenge it, try to break it really more than anything else.

That’s the fun bit. Right? If you do, so so I think that way you start learning, okay, what is the language I need to use with the AI that helps me get the results that are most relevant to what I’m trying to ask about.

And through that prompt engineering, we’re not only making the systems better because we’re providing them with more queries and data, and they can therefore train on that. But also we are learning a very important skill that will be absolutely key at least in the next five years and probably longer, which is the ability to understand how I talk to these machines and how will I be able to control them ultimately. Because if you don’t have that language, it’s like going to China and and trying to get by. You can get by, and, you know, you’ll still be able to point that stuff and and get your water organized or, you know, get tablets or whatever else.

However, it will be difficult for you. It will take you a lot of time, and you will just really struggle to to get by. And this is where a lot of people who are not going to play about with this technology are going to learn the language, who are not going to learn things like prompt engineering. They will start becoming less relevant in their roles because they will be too slow to deliver results.

They will be put on performance improvement programs in the worst case. Right? And they will have to join another organization that’s still few years behind or retire or change jobs entirely. Right?

It it really is kind of catching up, but I think we’re still at a very positive stage, which is people still have time to learn. Yeah. We’re still at the stage is not too late to start learning. And this is why I’m still positive about this.

You know, we still have the chance to as as as workers to to really, learn how this can benefit us, to learn how our role is potentially going to get phased out or parts of our role, and where will I be able to bring value to my organization.

And even better, it will hopefully make us think about what is my role in the organization, what value am I bringing, what is the organization even trying to do, why is it paying me money, you know, so without getting too philosophical, obviously, you know, it will hopefully help us to to think a bit better about what is the the value that humans bring into the workplace and and how we can stay relevant.

Jaro, we’re seeing business units grow tired of IT self reporting. Why is this shift happening?

That’s a good question.

So what we’re seeing in the industry is that, the businesses have relied on IT self reporting for a very long time, marking their own homework. And more often than not, the IT departments have been hiding behind their big technology works and words and abbreviations, and that ultimately created this language barrier between the technology and the business. So as long as IT were able to justify, oh, yeah. We do this so that we don’t end up on the front page of the newspapers or otherwise, everything will fail, and nobody will be able to do their job.

You know, the the value of IT has not been properly, I think, evaluated.

They were just left to their own devices with their own budget, and it’s only when they asked for more money, they were kind of asked why and how to properly support it. I think what’s changing is now with the with the technology advancing, we’re getting things such as NLU, natural language understanding generative AI powered bots, agentic AI, really. And, what what we’re now seeing the business, playing about with is plugging those, those AI agents into their operational data, into their business data, and be able to query it in their own way. Therefore, they don’t have to rely on the IT self reporting.

Instead, they’re able to ask what was the availability of our services this year, and how does it compare to last year? And the systems know because they know the performance data, and they’re able to give the answer to them. What is the cost of such and such system? Again, it can have that, those answers.

So what we’re really what what I think is going to happen in the very near future, is that, the the power will sort of shift.

And, and the, the the reporting that IT has been providing so far, which is primarily based on what we call SLAs, service level agreements. So things like how, I pick up a phone within thirty seconds, or I resolve a ticket within six hours, or any of similar metrics. And they’re very, very static, and typically very old, ways to measure things. So just to give you an example, on average, an SLA, remains unchanged in organization for twenty years.

So if you think about this, twenty years ago, so that’s we’re talking two thousand and five, I don’t oh, no. I would have had a mobile phone already. Right? But the but the difference between the phone then and now is is massive.

And it’s the same thing for computers. Right? The laptops, for example. The the power of the laptop nowadays is completely different.

And but yet, we haven’t changed our SLAs. How did that happen? And I think this is where the business is starting to ask you more questions. Do I just outsource you guys because you don’t make my beer taste better, and you’re not really necessarily improving my product if that’s the case?

Or are you, you know, are you still relevant? You’re providing enough value, and and should I keep using you, to to support all of my, all of my functions across the enterprise.

So yeah. So I think that the shift that we’re really seeing is the move from the IT self reporting and using just SLAs to the business having access to the right tools to be able to query the performance data themselves and going into meetings over prepared, and ultimately holding the IT teams accountable for, the results that they’re having. And that also enables that more human centered design, more end user centered design, where we’re able then to, to understand, okay. Well, the business is unhappy about all of these different things.

How can we as IT help it? Because on the surface, we have hit all of our SLAs. We’re picking up phones fast enough. We’re responding to tickets fast enough, but the business is still unhappy.

It’s called the watermelon effect. Green on the outside, red on the inside. Right? So how do we work with, how do we collaborate with the business to turn that into a line?

We are almost out of time. I have so many questions for you, but we need a couple more podcasts. Let me kind of round things off here going back to zero ticket.

What are one or two practical steps our listeners can take this week or next week to start moving toward this future?

Yeah. I think the first one is just to generally learn what that means and just try to see how that could potentially apply in your organization.

I think a lot of the technologies are still thinking too narrowly within their department, within their current realms of technology that they have. Try to think bigger picture. Right? That would be my number one. Like, try to think bigger picture on the back of your learnings about how far this can actually go, where where the industry is going. And the second tip would be, learn about the components that are required in order to achieve that zero ticket future.

So your data, obviously, the technologies that are involved, the process change that are involved, the cultural change that’s involved. And it’s it’s not a small thing. Right? I appreciate that.

At the same time, if you, as a tech leader listening to this podcast, do not have that vision, who else has it? Right? Where is the organization going? How is it going to stay relevant in the market?

Or if you’re in health care in in public health care, how how are you going to help deliver better and better services to the to the, citizens? So, really, that that’s the two things I would I would point out there.

Any final thoughts, Jaro, and call to action if people, they’re like, you know what? We have more questions too, Michelle. Where where can we go? Where can we find resources?

Yeah. I’m very active on LinkedIn. So you can find me on LinkedIn under under my name as you see on the screen. So j I r o t o m I k. I believe I’m the only one there, so it should be fairly easy to find. And then, obviously, you can reach out to us through through the CW website as well and, and our great colleagues, at, Freshworks and Device four to two, obviously, have got I’ve got a good contact on us as well. So more feel free to to reach out to us that way as well.

Alright. Thank you so much, Jaro, chief technologist and digital enablement expert at CDW UK. Clearly, you know your stuff. As I said, we could talk for a few more hours on this, but I appreciate your time today. Thank you for being here.

Thank you so much for having me, Michelle.

And I wanna thank all of you for tuning in and listening to the Hitchhiker’s Guide to IT. Of course, if you enjoyed today’s conversation and would like to hear more engaging conversations like this, you can be sure to subscribe to the podcast. I’m your host, Michelle Dawn Mooney. Thanks again for joining us.

We hope to connect with you on another podcast soon.