According to a study by Analysys Mason commissioned by Nokia, telecom operators must overcome several challenges, including access to high-quality data sets in order to effectively deploy artificial intelligence (AI) and realise autonomous operations. The study notes that over 50 per cent of tier-1 telcos viewed data collection as the most difficult stage of the telco AI use case development cycle. Other issues include a lack of technology maturity, the inability to scale AI use case deployments, a lack of budget, and a lack of the required skill sets, among others.
The findings also state that communication service providers (CSPs) are unable to access high-quality data sets since they are using legacy systems with proprietary interfaces and their ability to quickly incorporate AI into their networks will be hampered by this.
At the same time, only 6 per cent of surveyed telcos believe they are at the most advanced level of automation, or zero-touch automation, which depends on AI and machine learning (ML) algorithms to manage and optimise network operations, the study added. The issue of high-quality data is also affecting CSP’s ability to retain AI expertise. Telcos need to examine their AI implementation strategies to address the data quality issue, the study further said.
