4 AI-Powered Steps for Network Integration in Wholesale Networks

In 2023, nexfibre, a partner of Virgin Media O2, acquired UK altnet, Upp. Fast forward seven months to June, and we witnessed another major merger: Netomnia and Brsk. Setting a bold target of reaching three million premises by the end of 2024, this merger highlighted the continuing consolidation of the wholesale network market.

While altnets are challenged to scale by high capital expenditure demands, operating in a crowded market, larger telcos’ race to acquire promising altnets brings its own complex integration challenges, including:

Getting to ROI quickly.
Preventing churn from an acquired customer base.
Ensuring network quality throughout the integration process.
Clarity around roles, organisational structure, and operating model.

To speed up and make the integration process a success, we’ve identified four essential AI-powered steps to achieve seamless integration in wholesale networks.

1. Use AI to optimise virtualised network functions and manage scalability

Virtualisation, specifically Distributed Network Functions Virtualisation (D-NFV), is critical in integrating wholesale networks into a larger telco environment. However, without a structured approach, deployments can quickly become complex, especially when dealing with multi-vendor scenarios typical in wholesale networks. AI can play a crucial role here, automating key processes such as configuration, dynamic scaling, and real-time fault detection to minimise integration friction.

2. Align technologies across systems early

Ensuring Business Support Systems (BSS) and Operational Support Systems (OSS) compatibility will be step one for telcos looking to provide an uninterrupted service for new customers. The next step should be a well-designed, unified data architecture, which means telcos can focus on delivering reliable services, rather than constantly firefighting integration issues.

Equally important, they can avoid major overhauls of legacy systems by integrating API gateways and middleware. This approach bridges the gaps without the need to rip out and replace existing infrastructure - saving time and budget.

AI offers even more potential for streamlining this process. With the right AI tools, telcos can automate the identification and resolution of compatibility issues between BSS and OSS, freeing up valuable time and resources. For example, AIOps can automatically identify data discrepancies or mismatches between systems and suggest real-time solutions, ensuring interoperability. By prioritising compatibility, telcos can better align their BSS and OSS systems, creating a more agile and integrated operational ecosystem.

3. Proactive maintenance to ensure network quality

Given how important it is to maintain network quality during integration, particularly when handling differing architectures and operational models, this is an area where AI’s strengths in predictive analytics, anomaly detection, and real-time optimisation come into play. AI-powered monitoring tools allow telcos detect and resolve potential issues before they impact customers, ensuring optimal performance throughout the integration process.

For example, machine learning models can analyse historical network data to predict likely points of failure during periods of high traffic, allowing operators to address them before they cause disruptions. Also, self-healing network technologies can automatically reroute traffic and adjust configurations in real-time when problems arise, reducing downtime and maintaining service continuity during the transition.

4. Seamless customer onboarding to minimise churn

AI tools can also address onboarding issues for telcos by automating complex processes and reducing human error. For example, an AI-driven platform could handle data migration between legacy systems and new architectures by employing machine learning algorithms to detect inconsistencies and clean the data before transfer. This ensures accurate, real-time data synchronisation across multiple systems, eliminating delays and reducing the risk of billing or activation errors.

By analysing historical customer data, such as usage patterns, interaction history, and demographic information, AI algorithms can generate tailored onboarding messages, schedule service updates, and send timely notifications about service issues.

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