CIONET Trailblazer: From Data to Impact: How Telenet Group Embeds AI to Elevate Customer Experience

AI and data have become essential tools for businesses, but how do you ensure they create real, lasting impact? At Telenet Group, data isn’t just about analysis: it’s actively shaping decisions, optimising customer journeys, and enhancing connectivity. Their approach recently earned them a Data Maturity Award, recognising their ability to turn data into tangible business value.

In this CIONET Trailblazer, we sit down with Kim Smets, VP Data & AI at Telenet Group, to discuss how AI is embedded into their strategy, what it means to treat AI as a product, and the challenges of scaling data-driven solutions in a complex connectivity landscape.

Your team recently won a Data Maturity Award. Why did you win?

The award recognised our relentless focus on business impact. For us, Data & AI are never goals in themselves, they are powerful tools to improve our business. 

At Telenet Group, we aim to distinguish ourselves by the quality of our relationship with the customer. Our Data & AI products are the key ingredients to make that happen.

This is not just an ambition; it’s today’s reality with a broad portfolio of Data & AI products across the company driving real impact. At Telenet Group, we use Data & AI to:

• Steer every decision with insights.
• Optimise every customer journey.
• Make every customer interaction hyper-personalised.

We were able to give a multitude of specific examples of how Data & AI is used to steer and accelerate the business.

What are some of those examples?

Here are three of our internally most famous examples:

• Segment of One boosts cross-selling and prevents churn by offering customers personalised proposals.
• Agent Assist (Gen AI) helps our advisors find answers and solutions to customer questions and issues faster.
• Friction Finder improves our NPS (net promoter score) by identifying and addressing customer issues in our journeys.

You see Data & AI as a Product, can you explain what this means?

To achieve continuous value at scale, we evolved our approach to treating “Data & AI as a Product”. This means doubling down to make sure every investment has a lasting impact.

A lot of companies still use data only to look back and understand the past. Some use Data & AI to deliver value proof points on how it could improve the business, but these often remain start-stop projects without a clear path to scale. For us, the name of the game is delivering continuous business impact at maximum scale. 

 For each of our key Data & AI products, we have :

• E2E product ownership, so business teams can rely on it.
• A prioritised product roadmap, ensuring our increments stay in lockstep with the business.
• Product adoption management practices to follow up on and maximise value.

How do you define the relationship between Data & AI and connectivity?

Telenet Group’s vision is to “excel in customer experience through innovative digital services and connectivity”. Connectivity is the cornerstone of our relationship with our customers.

The relationship between Data & AI and connectivity is two-fold. On the one hand, we use Data & AI as a compass. Where does the connectivity experience fall short for our customers? We use that insight to guide our improvement efforts and investment decisions. On the other hand, we look at Data & AI as our booster rocket: we use it to unlock new and better experiences for our customers, moving from reactive to proactive mode, and solving issues before our customers experience them.

Can you give examples of AI products that are enhancing the connectivity experience?

Here are three well-known internal examples of AI products improving the connectivity experience:

• Product Experience Enhancer proactively resolves connection issues, helping to prevent churn.
• Value-based network optimises (CAPEX) investments by supporting the smart rollout of mobile and fibre networks and improvements where customers need it most.
• Network Service Assurance optimises the return on our network maintenance efforts by considering the actual impact on the connectivity experience.

The impact of these AI products is not only driven by the data-intelligence but also by how the solutions are integrated and deployed. For example, with Check & Smile, a feature in the My Telenet app, we put the intelligence of the Product Experience Enhancer at the customer’s fingertips. They can run a check-up of their connectivity themselves. If we predict that something is off - due to a problematic modem, for instance -  the app will explain the issue,  show why a new modem is needed, and allow the customer to instantly order a new one. After one or two days, the modem is delivered, and they can once again enjoy excellent connectivity.

What data challenges arise specifically in connectivity-focused AI projects?

We have found that part of being successful in Data & AI is ensuring the whole company is on board. If you want to have an impact on the business, you need to make sure everyone fully understands what you are trying to achieve.

Data & AI is a complex world. A lot of effort goes into translating that complexity for a broad audience.

In connectivity-focused AI projects, not only does the Data & AI part contain complexity, but connectivity itself is also complex. The data people working on these projects have to put themselves in the shoes of for example a Network Engineer, understand core connectivity metrics, and translate that into data insights.

Finally, the work in both connectivity and data has to be translated back to the business to show how it drives value.

AI and data are playing an increasingly central role in decision-making, customer experience, and business strategy. At Telenet Group, the focus is on embedding AI into everyday operations in ways that deliver practical value, whether by improving customer interactions, optimising network performance, or guiding smarter investments.

Their approach highlights an important takeaway for digital leaders: AI is most effective when treated as a long-term capability rather than a one-off project. As organisations continue refining their data strategies, the challenge remains how to turn AI from an experiment into a lasting driver of business impact.

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