Key Takeaway
Operational excellence is crucial for fostering innovation at Sunrise, balancing rapid change with customer reliability. The company implements thousands of network changes monthly, guided by an integrated quality framework that measures impacts on user experience. To manage complexity, Sunrise invests in intelligent automation, AI, and machine learning for faster problem resolution. Looking ahead, the focus is on proactive service quality management through data-driven insights. Challenges include effective organizational adoption of new technologies while maintaining a clear goal of enhancing customer experience and operational efficiency. Ultimately, AI aims to improve network monitoring and maintenance, ensuring seamless service for customers.
Operational Excellence: The Engine Room of Innovation
A culture of bold innovation flourishes only when built on a solid foundation of operational excellence. For Sunrise, this means finding the right balance between the pace of change needed to stay competitive and the stability and reliability that customers expect. The environment presents a significant challenge, with teams executing thousands of network changes each month.
An integrated quality framework guarantees that every action, from major technology rollouts to routine maintenance, is evaluated based on its impact on the end-user experience. “We always ensure that every single intervention in the network, whether it stems from innovation and development or from maintenance and incident management, is assessed for its effect on user experience and satisfaction, for both residential and B2B customers,” Fabrizio states.
To navigate the complexity at scale, Sunrise is increasingly investing in intelligent automation. “We’re investing in automation, AI, and machine learning to enhance human expertise. This enables quicker anomaly detection, smarter root cause analysis, and more efficient resolution. It’s about scaling innovation without sacrificing stability,” he adds.
A Pragmatic Journey into AI and a Vision for the Future
Looking forward, the next frontier in enhancing customer experience lies in the intelligent use of AI and machine learning. Sunrise has embarked on this journey with a clear and pragmatic focus: utilizing data to transition from reactive problem-solving to proactive and predictive service quality management.
Sunrise’s initial pilots have already yielded valuable insights. “One of the most significant lessons from our early activities has been the power of telemetry, extracting anonymized technical parameters from customer devices, correlating them with optimal values, and automatically enhancing mobile connectivity or in-home setups without any customer interaction,” Fabrizio notes.
However, he acknowledges that the real challenge is not in deploying the technology itself but in achieving effective organizational adoption. “The true challenge with AI and machine learning is not merely the introduction of a new tool but its effective adoption. The focus must remain on the objectives: enhancing customer experience, strengthening network reliability, and driving operational efficiency, without allowing the tools to become distractions,” he explains.
Fabrizio cautions against what he terms the “reality-hype gap,” emphasizing the importance of keeping the ultimate goal in focus: tangible improvements in both customer experience and operational efficiency. This pragmatic approach will guide how Sunrise leverages its extensive data resources to create a more intelligent and responsive network.
So, what does this mean for the typical Sunrise customer in the coming years? Fabrizio envisions a seamless and reliable future: “AI will enable us to observe service performance more deeply, uncovering patterns of hidden defects in network services or hardware that would otherwise remain subtle or lost in the noise of telemetry. It will make fixes and improvements significantly more efficient.”
Sunrise is also enhancing its network monitoring and the correlation of alarms and issues. The aim is to detect interruptions and identify root causes much more quickly and in a more automated manner, proactively maintaining and repairing infrastructure before outages or degradation lead to service interruptions.
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