Contributed Article

The fast pace of emerging technology adoption is increasing pressure on telecom operators to meet demands and implement AI.

By: David Cottingham, CTO at IQGeo

The global AI market within the telecom sector is expected to reach $25 billion by the end of 2027, according to Valuates. But without high-quality network data, AI’s potential will remain out of reach for many telecom operators.

According to a report by McKinsey, AI can have a huge impact on growth and disruption in the telecom sector. As a result, many telecom operators are choosing to adopt the technology because of their promise to enhance efficiency and customer experience.

Without accurate, real-time network data, AI-driven automation and predictive insights will be unreliable—leading to costly errors and inefficiencies. To stay ahead, telecom operators need to prepare their data before kickstarting implementation. This will reduce any inaccuracies that could result in network failure or severe disruptions.

Why high-quality network data is the backbone of AI success

Accurate data about network asset locations, configurations, and physical connections are integral to both operational efficiency and technological developments. Therefore, inaccurate or incomplete network data results in operational inefficiencies. This leads to slow delivery service, wasted resources, and puts telecom operators behind their competition.

To stay ahead, telecom operators must have a reliable foundation of high-quality network asset data. They need to focus on strategies and technologies aimed at creating an accurate network model, including:

• Maintaining network documentation: network documentation needs to reflect continuous updates and changes in the infrastructure, helping to keep track of the regular updates and expansions within the industry. This will reduce the inefficiencies mentioned above and keep the system changes tracked in real-time.
• Asset management: this means accurately tracking the geospatial location and connectivity architecture of all assets within a network. As a result, each asset will be properly monitored and mapped.
• Prioritizing integrated systems: this is about managing the fiber network lifecycle. Integrated systems create a single source of truth by consolidating data. This leads to more efficient network management and better decision-making.

 5 steps to prepare for AI implementation

Telecom operators should consider these five main factors when preparing for AI implementation:

1. Authorize a data quality audit
   This is conducted to review the current state of network data and identify any factors that could impact telecom operators. Telecom operators can then make an assessment of how data flows through the network and flag any areas that need improvement. Future decision-making is then based on reliable, actionable information.
2. Make incremental improvements
   Telecom operators should focus on incremental improvements to their network data quality, instead of choosing expensive, high-risk overhauls. This enables continuous improvements without interrupting ongoing operations, leading to greater control. This is more cost-effective and allows telecom operators to remain agile.
3. Integrate documentation systems
   By investing in integrated documentation systems, telecom operators can streamline their network management processes, moving away from heterogeneous spreadsheets and tools. This means that every aspect of the network is captured in a single source of truth, improving the accuracy of data and enhancing collaboration across teams.
4. Choose reliable technology partners
   This is crucial for the long-term success of network data management, as reliable partners provide the tools and expertise needed to manage high-quality network data. By choosing vendors with a proven track record, telecom operators can ensure that their network management system evolves with industry demands, ahead of competitors.
5. Practice rigorous data management
   In doing this, the accuracy and reliability of network data is guaranteed, because any changes in the network will be immediately reflected in real-time. Telecom operators can then maintain consistency, reduce errors, and ensure that the network’s performance is accurately represented.

To stay competitive and maximize ROI, telecom operators must modernize their network management approach. Many established operators still rely on outdated GIS systems and siloed applications, which create inefficiencies and limit scalability. In contrast, operators who adopt integrated lifecycle management solutions are better positioned for long-term success.

A modernized network foundation is essential for leveraging AI’s full potential. Replacing spreadsheets and CAD drawings with intelligent fiber network documentation software enables a streamlined, data-driven approach. Beyond efficiency, AI-powered analytics enhance network reliability through predictive maintenance, identifying potential fiber breaks before they occur. Real-time anomaly detection also improves service quality, ensuring a more resilient network and better customer experience.

The power of strategic vendor partnerships

Managing multiple vendors creates complexity and inefficiencies. Choosing a primary technology partner streamlines operations and ensures seamless integration.

Establishing strong partnerships with a select few key vendors allows long-term management and ensures that integral software components are integrated seamlessly. This will help deliver significant ROI by simplifying management.

Overall, by prioritizing a strong data foundation, telecom operators are setting themselves up for future success. With well-managed network data, telecom operators can fully enjoy the benefits of AI integration and leverage AI’s potential, putting them ahead of competitors. Therefore, by making strategic investments in network data quality now, telecom operators are laying the groundwork for future technological progress and long-term growth.

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