with Lukas Lhotsky and Courtney Deinert

Bell is one of the first telcos in the world to use ServiceNow Otto for TSM in production. This session delves into their approach working with Ateko to plan, implement, and integrate AI-driven workflows to make the most out of their 38 million annual customer interactions.

Transcript:

Courtney Deinert:
Thanks for joining us. We hear a lot in the telecom industry about what AI can do, the demos, the pilots, the proofs of concepts. And a lot of it stops right there in pilot mode. Today, you’re going to hear from a company who’s actually doing the work, not just testing ideas, but putting AI into production, changing how their teams operate, and seeing real results. And who better to tell that story than Lukas Lhotsky, who sits at the center of Bell’s AI strategy. Lukas, thanks for being here.

Lukas Lhotsky:
Nice to be here, Courtney.

Courtney Deinert:
So, Lukas, before we get into the work, give us the picture. You’ve got a pretty broad remit at Bell. What does your world look like day-to-day?

Lukas Lhotsky:
Yeah, I’m excited to play two roles at Bell. So, my first role is working alongside our CIO, Chief Customer Officer, and many of our technology teams to embark on a very ambitious digital transformation where we’re really reinventing the way that we work with our customers and delivering exceptional outcomes through technology to our customers. And then a secondary role I play is I’m also the president of a company called Ateko, and we are Bell Canada’s SI and MSP, and we work very closely with ServiceNow to deliver the same kind of enterprise outcomes we’re delivering at Bell to other enterprise customers in North America.

Courtney Deinert:
Fantastic. So, dual role, so then you’re going to be able to give us some good perspective there. So, let’s talk about AI then. Let’s, because this is where it gets interesting for your dual role. A lot of companies right now are talking about what a lot of people are saying “throw it at the wall” approach, spinning up pilots hoping something lands. Bell didn’t do that. You were very intentional about how you brought AI to life, and specifically in customer service, and we can double-click on that. So, walk us through that thinking. What made Bell take a different approach?

Lukas Lhotsky:
I think we, like many other companies, did try some of the pilots, and I think the pilots were useful because they kind of helped us give a boundary to where AI could have impact in the organization. But we very quickly recentered ourselves on one of our core strategic priorities, which was how can we continue to deliver these exceptional customer outcomes and improve the way in which our customers communicate every day. And that happens at the interface between our business and the customers. And so that’s where we really started to look at the AI use cases more closely.

And as we started to look at AI, we realized that it was going to enrich a lot of the existing processes that we already had. People were looking for information, and AI could help surface that information. People were sometimes drowning in some of the many different channels and ways in which they could communicate with us, and we thought that AI could make some of that channel noise a little bit more straightforward. But most importantly, I think we felt that we could arm our human workforce with AI and help them deliver better outcomes to our customers by ensuring that the AI was there as a steward, as a guide, and as really a co-pilot, if you like, on some of the ways in which they were interacting.

Courtney Deinert:
I love that. I love that answer, and it’s actually the perfect segue and where I was hoping to take this conversation. AI being part of that customer experience journey at Bell, and that’s a big statement. So what does that mean for the people on the front lines? You, I think you’ve started to kind of allude to that, but really, how has it changed the day-to-day work for Tier 1 agents? And not only the agents, by coupling the AI with the agents, but also talk a little bit about the experience.

Lukas Lhotsky:
Yeah, I think overall it’s been incredibly positive, right? I think a lot of organizations, whether it was ourselves at Bell but others with whom we work, have a certain amount of reluctance at the beginning with respect to is this going to transform the way in which we do work and is it going to change some of the fundamentals. And the answer is it does, but not in a disruptive way. I think it’s in many ways just an incremental way, and the humans adapt very naturally to being the humans in the loop in these AI processes. And that’s certainly what we’ve seen.

So, you know, for us, the outcome has been a huge amount of time saved. When you think about it, we want our folks to deliver the best possible experience they can to our customers. And to deliver that experience, they need to be on top of a lot of information at times, right? We offer a lot of different products, we work in a lot of different geographies, we have a lot of different kinds of customers, and we want to be able to meet our customers where they reach us to, and to do that sometimes means filtering through a lot of different material. And so we’ve been able to use AI to solve some of that signal-to-noise problem. We’ve been able to use AI to surface the right information in the right context at the right time.

So really the folks that are on the phone can deliver better information, more information more quickly, but also sometimes just avoid having to have the conversation at all because the AI can answer the question right away. It can plug into our ServiceNow ecosystem and simply answer the question because the answer is already there. So, we see a huge gain of productivity. We’re excited about what it means, and we’re still convinced that there will be a very significant amount of folks working with AI, alongside AI, but using systems like ServiceNow to be the systems of record and action that allow that information to be surfaced.

Courtney Deinert:
Right. And I liked how you positioned it. It’s getting the information but also being able to act on it. So sometimes you can avoid that, you know, unpleasant conversation with a customer altogether because AI is actually helping solve. I love that. Um, so it’s not, it’s not only about the results though in terms of efficiency. Can you talk to a little bit about the adoption side of AI and how your teams are feeling about having that co-worker, that AI co-worker right next to them and any reaction that you’ve seen there on adoption?

Lukas Lhotsky:
Very positive overall. You know, I think the adoption story for us has been a quite a simple one because it is just results-driven, right? And so our folks want to have an impact. They want to deliver that customer experience that we’re looking for, and one of the ways that they can do that is having the right information at their fingertips, being able to, to your point, act on it and have the, you know, drive the outcomes that they’re looking to drive. And so in that sense, we’ve seen it be very positive, particularly when you get into some of the more complex cases, right? Because in that complex case management, you can imagine there are a variety of different departments or different functions or technologies at stake, and the ability to use AI to federate some of that and pull that some of that together means that we’re really giving our folks a better opportunity to answer the right way the first time.

Courtney Deinert:
Absolutely. No, I love that answer. So here’s, here’s what stands out to me. A lot of CSPs, and a lot of enterprises, honestly, are still stuck in this proving value in one or two AI use cases, really struggling to how do you move from pilot to production. I think the last number I heard, and you can correct me if I’m wrong, Bell has something like 300 use cases in production.

Lukas Lhotsky:
We do.

Courtney Deinert:
That’s a lot of pilot program. That’s a completely different operating model. So how did Bell get there and what was the approach that let you scale like that? And what would you say to folks listening to us are still trying to figure out and get that first handful of use cases past that finish line?

Lukas Lhotsky:
Yeah, I think there’s a few lessons learned for us, right? I think one of them, and it’s an important one, is that you need a robust underlying dataset. You know, at Ateko, we spend a lot of time working with enterprise customers, and one of the regular observations we make is is that if you don’t have a robust data pipeline, you don’t have robust data operations, you don’t have some federating system of action like ServiceNow, it becomes really difficult for agents to actually do things. And so, one of the things that we set out to accomplish from the very beginning was to shift the paradigm from having an agentic conversation to having an agentic outcome. And what that really meant was, how do we ensure that some of the agents can be tied into these underlying systems of action to get work done? And I think that as soon as we started to see that get-work-done part of this, that’s where we saw its true potential. And when you see that potential, the others use cases sort of surface quite naturally.

I think the other learning for us is, you know, at the beginning we were, like many other organizations, almost paralyzed by the sheer potential of AI, right? And so we thought, wow, there it can do all of this. And that that almost requires a pause and you think where do we even start? And you, I’ve seen a lot of organizations just not know where to even begin. And, you know, one of the ways we started to think about it is, yes, there is this incredibly powerful tooling and yes, we can create enterprise-wide chatbots and all of these sorts of things.

But, you know, an analogy I like to use is, we all have these really complex computers, but at the same time, we have a backup camera in our car and it does one thing and it does it really well. And so we started to look at our agentic use cases and our AI use cases in this very functional way. You know, what do we want an AI to do, and on what system of record, with what underlying dataset, and what is the specific outcome? And that outcome-driven approach has really helped us surface these interesting use cases that we think are really productive.

Courtney Deinert:
I love that. That’s great. And it’s such an honest answer, right? Because, you know, a year ago we were all still meddling in this, what’s going to work, what’s not going to work. So being very intentional and putting some guardrails around that, if you will, around really what the outcome is that you’re looking for, what a great, what a great way to move forward and move forward quickly. So last question. AI is now a part of the customer journey at Bell. That’s a big statement. So help me understand, Lukas, what does that mean for the people on the front lines and how is that changed the day-to-day for them, for your Tier 1 agents? And what surprised you?

Lukas Lhotsky:
You know, I think what surprised us is in many ways just how straightforward and easy it was to get all of our frontline folks to adopt some of this technology. You know, we’re we’re really proud that after rolling some of this out, we’re seeing 90% positive feedback from agents. They’re they’re really trusting these AIs and they are working alongside them. And to me, that means that these agents are surfacing the right information in the right context, in the right time, and really augmenting the people we have that are doing what they do best is help our customers and serve our customers. And so we’re really proud of that outcome.

Certainly, they’ve also saved us a lot of time, right? We we are close to almost 10,000 hours saved in terms of some of these agent outcomes with our frontline staff. And again, what’s happening I think and what we’re seeing is is that we’re solving some of this signal-to-noise ratio, right? We are we’re getting the signal out of the noise and we’re having these agents draw our attention to the right things to be able to deliver more impact more quickly and tie things together for our customers.

Courtney Deinert:
Yes, we all have our own specialists, that’s for sure. So, thank you so much, Lukas for joining us today. I think what makes Bell’s story so compelling is it’s not just a vision deck you’re pitching in a boardroom. It’s operational, it’s measurable, you’ve seen how it changes your teams, how it’s changed the customer experience, and how it’s elevating Bell’s brand every day. So, Lukas, thank you for being so open about the journey. Really appreciate it.

Lukas Lhotsky:
Yeah, thank you for having me. Nice to chat.