As explained by Counterpoint Research, AI application in mobile networks circles around three applications – Self Optimizing networks (SONs), Software defined networks (SDN) & Network Function Virtualization (NFV) and enablement of neural networks. Among these, we may see SONs at the earliest. SONs enable operators automatically to optimize the network quality based on traffic information by region and time zone based on various machine learning algorithms.
IDC on the other hand has predicted that 31.5% of the telecommunication organizations are primarily focusing to leverage existing investments/infrastructure and rest 63.5% are making new technology investments for AI systems. While these continue to be global trends, India should equally see an increase around AI; primarily driven by enterprise needs to drive viable efficiencies and consumer demand for contextualization.
On the subscriber side, AI and Machine Learning will help telecom operators in subscriber profiling and analyzing offer conversion rates, content usage trends and network activity. This will help them push offers that are tailored as per subscriber needs at the right time, believe analysts from Counterpoint.
Using AI and data analytics, operators will be able to identify and push various services to the customers at the right time, for e.g. – in case of post-paid customers, operators must encourage high speed data services and offer tailored data packs when subscriber is running low on data. The timing of offering tailored packages based subscriber intelligence is very important.
Recently, Airtel partnered with Korea’s SK Telecom to enable AI-assisted network. SK Telecom has deployed an AI-assisted network (known as TANGO) with big data analytics and machine learning capabilities to enhance customer experience through automated detection, troubleshooting and optimization of mobile networks.
Why we need AI for telecom networks?
“As more reliable and affordable bandwidth is enabled, it unleashes a plethora of opportunities that can traverse over telecom networks. So, a convergence at network level becomes possible. This is then value enhanced by adding dynamism and intelligence in to the systems through AI which makes the solution intuitive, proactive as well as reactive to the situations,” said Faisal Kawoosa, Lead Analyst, CyberMedia Research. He added that telecom becomes the default highway for anything that is to do with digital and adds a lot of opportunities in the telecom domain. One may not see the telecom the way we look at it presently, meaning a different set of revenue streams as well.
“AI is expected to have an impact in a multitude of areas – the most important being traffic classification, anomaly detection and prediction, resource utilization and network optimization, along with network orchestration. Further, it will also assist the mobile devices with virtual assistants and bots,” said Arjun Vishwanathan, Associate Director, Emerging Technologies, IDC India.
According to Pankaj Lamba, Customer Business Executive, (India) Amdocs, Artificial intelligence will solve most of the issues related to customer care, network coverage, billing, service/product offering and many more. Personalization of service and care would witness a new benchmark.
AI will help telcos in creating alerts and advice subscribers to the best plan. It will be essential for creating personalized and adaptive customer journeys.
AI’s role in automating networks
Emerging technologies such as IoT and cloud are pushing the networks to handle higher volumes of data, therefore; making automation an imperative for better network planning and connectivity.
“Typically, networks through nodes observe something and then the controller, generally a human being, takes a desired action. With AI, the network can decide on its own and also take the next course of action through various hardware / software solutions, essentially IoT solutions,” explained Kawoosa. Added to it through Machine Learning, the network will keep on adding intelligence, so it will grow in capabilities like humans as they acquire more skills and knowledge.
“AI-based intelligent network applications such as precision algorithms can provide intelligent network optimization/operation solution, and intelligent network operation and maintenance. Further, AI technology will also lead to the evolution of automatic, self-optimizing and self-healing networks, complemented with high performance computing power and data analytics capability,” said Vishwanathan.
As explained by Lamba, existing business processes such as network operations (both planning and optimization) have been performed manually resulting in delays and errors, which negatively impact on customers’ experience. To resolve these challenges, CSP business processes can be automated using AI capabilities such as machine learning, deep learning, and natural language processing. The need for AI to drive automated CSP operations will continue to grow as the CSP network moves from being physical to being virtual. Software defined networking (SDN) and network functions virtualization (NFV) will be dependent on automated processes to deliver service agility and cost efficiency. Capabilities such as self-diagnostics and self-optimization can only be achieved using intelligent insights obtained from the analysis of quality data sets.
“AI-enabled networks can think beyond their correlative programming and suggest outcome-based scenarios (‘what would you like to happen’). In the future, AI will be able to differentiate between correlative and causal, and proactively pursue their own choice of outcomes beyond the scope of human programming, and before any problems are noticed by subscribers (‘I can take care of myself’),” added Lamba.
How AI will integrate technologies
According to Kawoosa, there will be requirement of all software/hardware tools to add intelligence. These will help in building sensory system to the networks and in a decentralised architecture which is important for such a solution. Also, through SDx (Software Defined Anything), the networks will have the agility to respond to the situations without requiring phenomenal changes in the system components.
“SDN/NFV in combination with AI is becoming a powerful tool for evaluating and securing networks effectively. It can help telcos in addressing their concerns over analyzing massive volumes of information to detect consumer patterns, anomalies and potential security concerns,” said Vishwanathan.” He further said that at a granular level, this could further help them in optimizing the profit margin arising from enhanced network operations and reconfiguring the network to restore or mitigate services in the event of any cyber security attack.
AI and new revenue streams
As stated by Lamba, AI algorithms can combine historic patterns and behavior (plus “look alike” patterns) with ongoing real-time engagement to provide the right next best action to the customer at the right time and in the right context of their journey. The outcome for the consumer will be recommendations and offers that are personalized, well targeted, and relevant. The result for the CSP will be an increase in revenues and ARPU.
“The key area where telcos can deploy AI to generate new revenue source would be ‘Subscriber Intelligence’. From contextual and personalized upselling to innovative credit models, telcos can customize their offerings “real time” to improve the conversion rate of offers, thus enabling incremental wallet shares from their customers,” said Vishwanathan.
“As offers start becoming smarter with improved machine learning, customers buyer behavior will also improve. This will result in higher conversion rates of newly launched business models; that are technologically powered by AI,” added Vishwanathan.
What does the future hold?
IDC’s research has stated that the primary goal of telecom/media companies to leverage AI technologies is largely driven by improving efficiency, reducing staff related costs and increasing revenue. Other key drivers include improving customer support, marketing & engagement, operational insight, regulatory compliance, fraud detection, along with supporting business innovation.
“Most telco players today continue to experiment with AI, especially in the domain of generating actionable intelligence from structured and unstructured data. In this they are partnered by emerging vendors/startups. Over the next 12-18 months, we should see a variety of offerings that are AI powered in the background,” said Vishwanathan.
At present, AI is at pre-mature stage in India. We may see higher adoption of AI among telecoms with introduction of 5G in India, concluded Kawoosa.