High-quality data access top barrier for CSPs to effectively deploy AI

Accessing high-quality data remains a top challenge for communications service providers (CSPS) to effectively harness the power of artificial intelligence (AI) to enhance network and service operations, according to a new study commissioned by Nokia and conducted by Analysys Mason, a leading research and consulting firm.

The research, based on responses from 84 CSPs surveyed globally, highlights the urgent need for telcos to address these barriers to meet their ambitions for autonomous networks.

The Nokia-commissioned study reveals that a mere six percent of CSPs believe they have achieved the most advanced level of automation, known as zero-touch automation, which leverages AI and machine learning algorithms to manage and enhance network operations.

According to the survey, data access issue is also impacting CSPs' ability to attract and retain AI talent.

Despite a host of challenges, approximately 87 percent of surveyed CSPs have started AI implementation into their network operations, either through proof-of-concept trials or full-scale production integration. Of these, 57 percent report having successfully deployed AI use cases for telecommunications to the point of production.

"AI has a crucial role in driving step changes in network performance, including cutting carbon footprints. 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, including building the right ecosystem of vendor partners with the right skillsets that can better cater to their network needs," Andrew Burrell, Head of Business Applications Marketing, Cloud and Network Services at Nokia.

CSPs are optimistic about the potential of AI to improve network service quality, top-line growth, customer experience, and energy optimisation to meet their sustainability goals.

The research recommends that CSPs should critically evaluate their AI implementation strategies and develop clear roadmaps for AI integration, focusing on overcoming the data challenge and addressing hurdles related to scaling AI use cases.