The digital transformation of industries and the proliferation of connected devices has changed the communication service providers, or CSPs, space. Because of the growing pressures from customers, providers are inclined to expedite the transition toward autonomous networks.
Autonomous networks promise to alleviate the burden of manual work by incorporating technologies such as AI and machine learning. Autonomous networks can unlock novel business models by enabling CSPs to offer differentiated services, expedite time-to-market for new offerings and provide personalized experiences tailored to individual customer needs.
Autonomous networks can also help keep customers satisfied with reliable connectivity across devices and locations. By reducing service disruptions and providing consistent quality, CSPs can foster customer loyalty and attract new subscribers, thus bolstering their market position.
On paper, all of this looks great. No doubt it should be the end goal for CSPs, but an end goal never comes without any obstacles.
According to new research commissioned by Nokia and conducted by Analysys Mason, CSPs face hurdles that impedes their ability to effectively deploy AI, or telco AI to be more specific.
Telco AI refers to the application of AI in the telecommunications industry. It involves using AI techniques like ML to improve various aspects of telecom operations and services. Telco AI is used for tasks such as optimizing network performance, enhancing customer experiences with AI-powered tools, predicting customer behaviors, detecting fraud, ensuring accurate billing and managing virtual network functions.
Among the many hurdles the report found, there is one big hurdle – the limitations imposed by legacy systems with proprietary interfaces. These outdated systems were designed in an era before the surge of AI and advanced data analytics. As a result, they lack the capacity to provide access to high-quality data sets that are crucial for making informed and precise decisions.
The problem stems from the fact that legacy systems were not built with interoperability and data sharing in mind. Many of these systems have closed and rigid architectures that don't easily communicate with other components or external sources of data. This confinement obstructs the flow of data between various systems and hampers the CSPs' ability to gather the diverse and comprehensive data needed for effective AI implementation.
The consequence of this restriction is a bottleneck in the integration of AI technologies into the networks of CSPs. The inability to access high-quality data not only undermines the accuracy of AI-driven decisions but also impedes the potential benefits that AI could bring, such as automation of network management, predictive maintenance, and optimized resource allocation. Another consequence is the potential of falling behind competitors.
To successfully jump that hurdle, CSPs must embrace more open, modular and interoperable architectures that facilitate data sharing and integration. By doing so, CSPs can unlock the potential of AI, enable data-driven decision-making and expedite the evolution towards more autonomous and efficient networks.
“CSPs must transition to more-autonomous operations if they are to manage networks more efficiently and deliver on their main business priorities,” said Adaora Okeleke, principal analyst at Analysys (News - Alert) Mason. “They need to really examine their AI implementation strategies to work around this data quality issue.”
And 87% of CSPs have started to implement AI into their network operations, either as proof of concepts or into production; with 57% saying they have deployed telco AI use cases to the point of production, according to the report.
CSPs believe AI will help improve network service quality, top-line growth, customer experience and energy optimization to meet their sustainability goals. They just need to overcome the hurdle that comes with legacy, outdated systems.
“AI has a crucial role in driving step changes in network performance, including cutting carbon footprints,” said Andrew Burrell, Head of Business Applications Marketing, Cloud and Network Services at Nokia (News - Alert). “CSPs are aware of the challenges of more deeply embedding AI into their operations and, as this research points out, the steps they can take to positively alter that situation.”
Edited by
Alex Passett
