New research commissioned by Nokia and conducted by Analysys Mason finds that 87 percent of communications service providers (CSPs) have started to implement AI into their network operations, either as proof of concepts or into production; with 57 percent saying they have deployed telco AI use cases to the point of production.
While CSP respondents said they believe AI will help improve network service quality, top-line growth, customer experience, and energy optimisation to meet their sustainability goals, it is also evident that major obstacles remain.
“CSPs are unable to access high-quality data sets (which will enable them to make more accurate decisions) because they are using legacy systems with proprietary interfaces. This will restrict how quickly they can integrate AI into their networks,” according to the research, which is based on responses from 84 CSPs surveyed globally. Almost 50 percent of Tier-1 CSPs ranked data collection as the most challenging stage of the telco AI use case development cycle.
Further, the research found that only six percent of CSPs surveyed believe they are at the most-advanced level of automation, or zero-touch automation, which relies on AI and machine learning (ML) algorithms to manage and improve network operations. The high-quality data issue is also impacting CSPs’ ability to retain AI talent.
The research said CSPs should evaluate their telco AI implementation strategies and develop a clear roadmap for AI implementation to overcome their data challenge and other impediments, such as an inability to scale AI use case deployments. The report can be found here.
Adaora Okeleke, Principal Analyst, at Analysys Mason said: “CSPs must transition to more-autonomous operations if they are to manage networks more efficiently and deliver on their main business priorities. But as this research demonstrates, accessing high-quality data remains a critical obstacle to deploying telco AI within their networks. They need to really examine their AI implementation strategies to work around this data quality issue.”
