Clarence Reynolds, TelecomTV (00:07):
In today's telecom landscape, AI powered automation is driving significant transformation. Joining me with more on the topic are as Sandeep Phadke, senior Vice President and head of Europe Communications Media Entertainment and Technology Business at Tech Mahindra, Paul Bosch, CIO of B2C at KPN, Dr. Mariam Kaynia,, director of Mass Market at Telefónica Germany, and David Link, CEO of ScienceLogic. Welcome to all of you. I'd like to get some insights on how integrating AI into customer journeys and other processes is working and what that feels like. Now,

Paul Bosch, KPN (00:53):
That's an interesting topic. I think we look at it today at KPN. That is actually about three things we always look at. It's indeed the customer process, the front end processes. How can we actually make the customer journey and the interaction with the customer more convenient using ai, getting to a higher customer experience. The second thing that we're always looking at is getting AI more involved in our telco processes, in our operational processes to make them move more smoothly. And the third topic I'm always looking at is how can we utilize AI capabilities for our development community to actually become more productive and be more, create higher time to market in developing new code and new software for our customers?

Dr. Mariam Kaynia, Telefónica Germany (01:36):
Wonderful. Happy to build on that. So at Telefonica, we are currently going through a major transformation where we are transforming the entire BSS landscape so that we can bring together data in a much more efficient manner. And obviously the data is used in order to serve the customers better. And as you rightfully said, the customers do want services in a very different way. Their needs and their demand is changing continuously. And if we don't use the data in an adaptive way and use AI for that, we are always behind. And that's one of the major things that we are changing. Also with our transformation, we are bringing the AI capabilities and new solutions in order to define the next best action for our customers. The next best offer we have digital cognitive agents, which helps our agents to actually improve the work that they do. So when the customer is calling, they can actually get the best response. We are automating a lot of the operational processes that also gives us efficiency and that allows us to also become better for our customers. So there's a lot of dynamic in this space and we are trying to do that end to end from the channels all the way down to our core BSO systems.

David Link, ScienceLogic (02:53):
We see really this is a once in a lifetime. The adoption of gen AI is the fastest growing technology we've seen in the history of technology, and there are some profound opportunities to change the customer experience to enhance it in ways that we've never been able to before and enhance the operational effectiveness where we give the level one engineers, level three sophistication, level three insights so that they can solve problems today and faster in kind of the seconds and millisecond timeframes versus sometimes longer timeframes for complex problems. The promise of AI is really has an intersection point right now with operational outcomes that the teams here are delivering each and every day of the week.

Sandeep Phadke, Tech Mahindra (03:43):
In Tech Mahindra, one of the things that we are doing is we have created a completely dedicated AI competency. And one of the things that we saw in a lot of our telco customers is there are AI teams that are there in different parts of the businesses, and if there are multi opco kind of operators where they have multiple countries in there, all of them are trying to design the same thing and create the same set of use cases all over again. So one of the things that we have done is whether it is customer experience or networks or engineering, we have brought all the practices on AI together in one single integrated team. We are trying to make sure that get the people who are associated with AI together and make sure they are multi-skilled as we go forward. So that's one of the things that we are doing at Tech.

Clarence Reynolds, TelecomTV (04:38):
I'm interested to get your views on how you might think about leveraging AI ops in the near future and what Next Gen telcos should be prepared for.

Paul Bosch, KPN (04:51):
So shall I take off again? Yeah, sure. No, I think that is, as I said before here, it is a once in a lifetime transformation and the steps that we are still having to make, and it's not about only AI or autonomous networking, but it is really about autonomous operations of the full telco stack. And it really goes about the end-to-end processes from the customer all the way to the network and back that can be fully automated. It can be automated with AI capabilities to a unique customer experience that is actually faster that it is today, which means that it adapts to the needs of our customers every day in such a way that we are becoming more and more proactive. And I think that will really change the world already and that we can actually adapt to your needs even before you already know what your needs are. And I think that is an amazing, amazing task that is lies in front of us.

Dr. Mariam Kaynia, Telefónica Germany (05:46):
I see AI ops, look, I am going to be honest. I think AI ops is one piece of the puzzle in a bigger puzzle piece or puzzle pattern I mentioned about the transformation that we're currently doing. First, why are we doing it is because we do want to get faster at deploying new functionalities and new products and services for our customers. Now, you can't do that by just changing the landscape or you only get bits and pieces of it if you don't adopt, for example, CICD, if you don't adopt DevOps, if you don't adopt the AI and data usage in order to be able to do these autonomous deployments, even going beyond automation, right? So it's really autonomy. You only get a certain part of that impact. So to me it all goes hand in hand. First is the, lemme say the technology infrastructure where we need to make sure is independently deployable and is microservices and we can go on and on about all the technological advances there, but this goes just hand in hand with the process automation. It goes hand in hand with DevOps, which then is reliant on CICD. CICD is reliant on the right data. It relies on monitoring end to end. If you can't even monitor end to end, how can you even do your operation optimization? So there are so many puzzle pieces that goes together. And of course the tagline, AI ops is one critical component in this whole thing in order to make sure we get the value to be faster, better for our customers.

Sandeep Phadke, Tech Mahindra (07:13):
One of the things that we are seeing, and that has been a common thread that a lot of customers are telling us is a lot of them are doing consistently use cases. And why are we doing AI? Because we want to achieve a business objective and a business outcome, and can a player like Tech Mahindra come in and deliver those outcomes to us and not just be reliant on use cases? I think that's one of the things that we are doing in Tech M where we are saying, can we take everything that we are building in the company, put it all together and commit to our customer outcome that is going to have a tangible benefit for them and is able to drive their business transformation way faster than it usually would've. So that's one of the things that we are busy with.

David Link, ScienceLogic (08:01):
I would say there is a common theme and AIOps is a catalyst for transformation. It's almost as much of transformation of people and process and tools and technology all coming together. We have a unique moment in time where we can manage very, very large sums of data in very efficient timescales unlike ever before. And that gives us some unique opportunities. First bid order for us is getting the data right. If you don't have really, really good high quality, high fidelity data, the rest of the operational enhancements and optimizations will fall flat. But the next is processes and looking at that end to end process from the beginning of a user experience all the way through the transaction and back to the user and how that ecosystem of technologies works at that one moment in time every time. And how do you optimize that session over session? Over session?

Paul Bosch, KPN (09:06):
Yeah. Maybe still to add to that, I think in the end for all of us in the telco industry, it is connecting that end-to-end processes because any technology, also AI or generative AI in our processes, it is in the end about our customer and we have to make the life of our customer and the services he experienced that has to be better. That customer journey has to be better. And if he can achieve that, then there's a lot of value in obtaining actually autonomous operations or AI ops. But if we come only from the technology side and start applic AI to that, I think we will fail anyways because a customer in the end is waiting for something that is better and is serving him better than it is today.

Clarence Reynolds, TelecomTV (09:48):
Miriam, your company introduced a program called Right a few years ago. What's your vision for that program?

Dr. Mariam Kaynia, Telefónica Germany (09:56):
We introduced this program about three years ago. It stands for radical architecture and IT transformation. We are transforming our entire BSS landscape and even though the name does refer to it, it actually is broader than that. It's a company transformation. We are completely transforming our business processes in order to prepare ourselves for the future future in terms of the agility that is required so that we can bring products and services faster to market for our customers future in terms of being able to offer new offerings for our customers. What we talked about in the beginning in terms of new AI services and better self-control on the app for the customers, more digital journeys. And at the same time, we also want to become more efficient in our stacks so that we are able to serve customers at lower cost. So all of that goes hand in hand in what we are doing as part of our right transformation. And it includes everything from stack consolidation. We're doing a move towards microservice architecture, we are moving to cloud, so public cloud we have for the past three years not had a single non-cloud native deployment at all. So all our core BSS systems are cloud native. And as mentioned, we are also doing a business transformation as part of this. So it's a major transformation that goes across the entire landscape from the channels to core BSS systems and all the satellite systems jointly with all of our company functions, business, legal, hr, everyone's involved.

Clarence Reynolds, TelecomTV (11:32):
And Paul, I'd like to know about the AI integration when it comes to B2B and how that differs from B2C.

Paul Bosch, KPN (11:42):
Sure, sure. So what we did in the B2B area in the past years was also a huge transformation and we came mainly in the B2B space. We come from a lot of customization and customer has actually a specific setup of his technology that is delivered by KPM. So we went through a massive portfolio transformation, actually standardizing a lot of the portfolio, making it more simple and making it possible for the processes to be automated and be further actually with AI developed to have good service and delivery processes for a standardized portfolio to our customers. We did that very well in the SMI segment. That's where we started because with small and medium enterprises, you can easily more or less do that also with your legacy base. We created also an open platform for partners and other parties to plug into to be able to make a richer portfolio with plug in other capabilities for our customers.

(12:38)
And then we started extending that way of working towards our LCE and our corporate segment, standardizing the portfolio. And I think the big change there towards the B2C segment is that in our B two Cs and our mass market segment, you are already much more on the way in a standardized mass market portfolio. It is about volume. It is about really an enormous amount of transactions of already standardized products and portfolio you deliver to the market. And that angle of already running a fully standardized volume based mass market deployment where you can easily start using automation or artificial intelligence to optimize your processes versus this environment where you have to work with a lot of micro products that you have to attach to each other to come up with customer delivery and customer services makes the B2B market much more complex to find it in a transformation to also become fully automated and fully autonomous.

Clarence Reynolds, TelecomTV (13:34):
One of the most important components of this entire transformation is going to be people. And how do you foresee having a future ready workforce for this AI powered telco future that we have?

Sandeep Phadke, Tech Mahindra (13:50):
Listen, I mean, people are going to be fundamental in doing this transformation towards ai. And we at Tech Mahindra for every technological shift, we have a very industrialized process already in place for several years now where we look at the technological changes and plan ahead for anything that is coming up. In this case, it's slightly different. We are looking at AI not only to upskill and cross skill the workforce, but also bring in new set of capabilities and competencies. One of the other areas that our CHRO and the management team is really focused on, can we look at AI to increase the diversity in the workforce? This is one of the key areas that we are focused on. Besides that, we have a very clear strategy to train our workforce. We have a geni studio that we have. There is a lot of work that is happening where we call a telco LLM that is being developed within TE Chem. And we also have something called as the Makers Lab where we trial a lot of new edge technologies as we go forward. But I think making sure that we are creating a shift in the diversity cushion of the organization and use AI as an opportunity is something that we are very passionate about.

Clarence Reynolds, TelecomTV (15:14):
David.

David Link, ScienceLogic (15:16):
Well, we think about it from a tools perspective, and one of the things that is interesting, we've been experimenting and working with gen AI for the last four years, and those models we found are very good for human generated content chat. GPT really took the world by storm over the last two years, but it's human generated content for many years, 30 plus years, we've been dealing with performance data numbers, numeric content for the utilization of different parts of the technology we use to deliver telecommunication services. And we found that machine reasoning is a better, more cost effective business outcome for that. But what's happening now is those two are marrying together and for the tier one

(16:07)
Help desk person, they really want, and what we're trying to deliver a copilot experience where the technology helps empower a tier one resource to be a tier three knowledge worker, and that helps with diversity. It helps people get up to speed faster. It also leaves the tier three team members to not be interrupted when they're coding with an escalation. So that's really good for all of us. And that I think is what this technology coming together, that traditional machine reasoning and gen AI coming together as one system is going to afford some amazing opportunities for those entry level workers to get up to speed a lot faster.

Dr. Mariam Kaynia, Telefónica Germany (16:50):
May I build on this because I see this as super critical, as part of our DevOps journey as well, because a lot of the highly qualified DevOps engineers cannot spend time on routine monitoring routine work in order to make sure that, let's call it the basic operations is running. And that's where AI ops is helping significantly to bring all the data together to whether it's language model or other AI ops or autonomous networks related, even bringing it to the next level, it really takes some of that routine work out so that they can focus on the real tough job that the AI cannot model. That's, and a lot of the resources attention goes into creating the next models to develop that next algorithm. So it's a bit of a shift in terms of the workforce and what we need to develop for the next generation so that we can keep developing the next generation of AI ops and AI models.

Clarence Reynolds, TelecomTV (17:49):
I so appreciate your input, your insights on these topics, and thank you very much for being here. Thank you. Thanks having us.