Mounir Ladki, Co-Founder, President and CTO at Mycom discusses how to drive enterprise growth CSPs must evolve beyond connectivity by delivering AI driven service-centric operations.
An AI-driven operational model for the age of differentiated Telco experiences
The telecom landscape is undergoing a profound shift. With mobile data traffic growth slowing down and consumer markets saturating, communications service providers (CSPs) are pivoting to the enterprise sector as a strategic growth engine. This shift demands far more than connectivity. CSPs must now deliver tailored, premium, SLA-backed experiences across 5G, fixed broadband, private networks, and digital services. To win in this market, CSPs need to:
This transformation is both complex and urgent, but the benefits are higher win rates, higher revenues from enterprise customers and overall reduced opex because of operational automation. All this requires a unified operational foundation powered by data, automation, and AI.
To deliver and monetize SLA-based enterprise connectivity services, CSPs must update their commercial and pricing models to enable transactional business and update their Business Support Systems (BSS) accordingly. Focusing on their operating model, they must implement 3 key evolutions: from network to service centric, from reactive to predictive, and from technical to business intent. Elaborating on this:
This operating model enables CSPs to model, monitor, and manage service performance end-to-end, from onboarding through to activation and usage. The service-centric process involves:
This operating process allows the automated detection and resolution of service degradation trends of all monitored digital services, and requires an innovative approach that involves:
The new operating model is not only about customer centricity and automation, but is also about speed, agility and simplicity. And this requires a new revolutionary way of accessing and interacting with data.
Generative AI capabilities can transform the way network intelligence is democratized across CSPs’ business and operations teams. By using natural language queries, CSP execs and staff as well as partners and customers can be empowered to “talk to the network and services” and get instantaneous insights enabling quick decision making.
A generative AI platform providing a natural language interface can support business operations by:
The three pillars of the transformation described above are dependent on the ability to maintain end-to-end visibility of customer, service and network experience in near-real-time and to automate at scale using AI. This cannot be achieved with traditional OSS/BSS approaches where data is organized in silos, inventory and topology information outdated and operations heavily manual.
CSPs need to build a new AI-native intelligence engine capable of generating these insights and commanding the automation actions. But AI efficacy, as we know, depends on data quality. This engine must come with a strong data foundation where high quality flattened, correlated and normalized data can ensure quality model training and model inference. Without this data foundation, an Intelligence Engine would struggle with noise, inconsistency, and false inferences.
So, the 3-phase approach to building the Autonomous Networks Intelligence Engine is: