Three ways AI tackles network complexity
The exciting era of artificial intelligence (AI) has quickly made inroads into the everyday lives of many Indians. AI is expected to contribute an additional 1.2 percent to annual global GDP growth for at least the next decade, according to McKinsey Global Institute.
AI can have a massive impact for communication service providers (CSPs). With the forthcoming evolution to 5G, extreme variances in traffic and dynamic workloads for virtualized network functions is expected to result in increased operational complexity. Indian CSPs, like their global counterparts, are battling with ever-increasing demand for high-definition content anytime, anywhere. For example, Hotstar experienced record viewership for the recent 2019 IPL final game with 18.6 million concurrent viewers. With unprecedented levels of data consumption and forthcoming move to 5G, CSPs are continuously under pressure to maintain robust and scalable networks. As a result, the telecom industry will consider AI models in networks for more efficient operations and to meet service intent for improved customer retention and revenue growth.
Indian CSPs are actively applying the vast potential of AI to optimize churn and develop packages of multiple services to improve customer retention. CSPs like Jio and Airtel have invested in AI platforms for better customer service, for example. CSPs expect that AI can also enable a more efficient network, with intelligence to manage increased operational complexity and challenges, such as fiber cuts, resulting in stronger customer relationships.
Below are three ways service providers can benefit from AI to enable network to be adaptive with predictive analysis of service and network state:
Configure: Service intent to network resource management
AI can help with faster network configuration and management. Management of networks involves a huge volume of repetitive and rules-based processes including day 0 mapping of service intent to network configuration across different technology domains, such as Internet of Things (IoT), cloud, wireless, wireline, and the like. With AI to ensure correct configuration per service intent, CSPs can accelerate service delivery, launch newer services faster, and ultimately improve customer satisfaction.
Predict: Identify and resolve before actual failure
According to Ovum’s report, Artificial Intelligence: Impact and Opportunities by Tom Pringle, published in March 2019, CSPs indicate that their top priority use case for AI is network fault prediction, detection, and correction, with over 40 percent prioritizing the implementation of this use case within the next 18 months. AI enables CSPs to look for patterns within the network state data collected from many sources, which is then used to detect and predict potential network problems before they occur, allowing CSPs to proactively resolve issues before the end consumer is negatively impacted.
Optimize: Improve utilization of available resources
AI is crucial for helping CSPs to build self-optimizing networks (SONs) that automatically self-heal and optimize based on observed network states, such as traffic and user attachment to specific cell sites, for example. With the rise of 5G, IoT, and cloud-based services as well as dynamic workloads for software-based network functions, AI can be used to process a large amount of network and service state to decide suitable optimal configurations in less time. This can help CSPs avoid stranding resources, especially when large variances in traffic is going to be the norm.
Adaptive networking practices harness the power and efficiency of data-driven AI. Coupled with the invaluable domain expertise of engineers, CSPs are able to tackle network complexities and simplify their processes. With AI, CSPs can simplify their processes and lead the industry into India’s next chapter of digital evolution.