Artificial Intelligence (AI) is driving a paradigm shift in the telecommunications (telecom) industry, ushering in a new era of innovation and efficiency. With its ability to automate processes, personalise customer interactions, and optimise network operations, AI is setting new standards for customer satisfaction and operational efficiency. Telecom companies can now deliver more responsive, intuitive, and customised services that meet the high expectations of today’s consumers.
An NVIDIA report shows that 48 per cent of telecom professionals prioritise enhancing customer experiences as the top AI opportunity for the industry. Yet, despite the excitement around AI’s potential, companies struggle with effective adoption due to a disconnect between AI’s potential and practical adoption.
As AI rapidly becomes essential to delivering seamless, efficient support, what’s holding the telecom industry back from fully integrating it across their operations?
Why are telecoms disconnected from AI?
Many telecom companies struggle with AI adoption due to challenges integrating AI into legacy infrastructures which were never designed to accommodate it. Successful AI integration requires significant investments to modernise outdated systems, but they are often not aligned with top-level management priorities and face limited technical talent and immature technology.
A McKinsey survey found that 85 per cent of executives hesitate to attribute more than 20 per cent of revenue or cost savings to developing AI. While telecoms generate vast amounts of data from customer interactions, network metrics, and IoT devices, much of this data is siloed across different islands of knowledge.
Some promising AI solutions may work in theory but in reality, fail to access relevant information or meet standards for enterprise use. To unlock AI’s full potential, telecoms must rethink their approach to data, allowing AI to provide real-time, actionable insights.
Amplifying AI’s potential in telecom through the context mesh
At its core, AI is driven by connected data. This connected data forms the knowledge foundation for AI systems, and powers use cases such as optimising network operations or enhancing customer interactions. As such, the real-time flow of precisely targeted information across the organisation network is critical. For most telcos, this is where a “context mesh” comes in – providing AI with the real-world context needed to maximise its full potential.
A context mesh operates through an event-driven architecture (EDA) which enables hyper-connected systems to respond instantly to real-time events. With EDA, data flows smoothly across the network, so that events – such as a customer nearing their data usage limit or a network disruption – are immediately communicated and lets telecom companies respond quickly and effectively.
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The context mesh relies on an event mesh, a network of interconnected event brokers designed to seamlessly route event-driven data in real-time across various systems, clouds, or protocols involved. The event mesh captures and routes these signals, and when combined with AI, it evolves into a context mesh, adding the situational context that AI needs to operate more effectively.
For example, when a customer reaches 90 per cent of their data usage, the context mesh allows the system to draw on additional information – like the customer’s data usage trends or location. The telecom provider can then send personalised notifications, such as top-up offers or a custom data package that fits the customer’s needs.
This enables instant responses to shifting conditions, triggering actions to improve customer experience and, by extension, loyalty. By maintaining real-time data flow across all connected systems a context mesh keeps telecoms agile, responsive, and better equipped to meet customer needs.
Speeding up decision-making signals
A context mesh provides real-time context data to both human decision-makers and AI agents, improving decision-making quality and speed. For telecom companies, the flood of information can hinder timely responses, but AI-driven insights help leaders quickly act on critical changes, and minimise risks associated with outdated data. This enables more agile, strategic decisions that swiftly address customer needs, network performance, and market shifts, ultimately improving operational efficiency and enhancing the customer experience.
For instance, when a customer call is unexpectedly dropped, the network monitoring system detects the issue and triggers automated responses. The premium subscriber system initiatives a compensation program, while loyalty and provisioning systems take appropriate actions. This rapid response, facilitated by the context mesh, allows telecom leaders to swiftly resolve customer concerns, enhancing satisfaction and loyalty while minimising the impact of network issues.
Boosting customer experiences
By enabling AI applications to access comprehensive and up-to-date customer data, a context mesh facilitates the delivery of highly personalised telecom subscribers. For example, a customer streaming video on a busy network could receive a bandwidth-optimised experience or even an upgrade offer to a higher-speed plan that matches their needs. By delivering personalised and timely solutions, telecoms can create a smoother, more valuable experience that resonates with customers, fostering loyalty and enhancing brand reputation.
Also Read: Will China lead the Artificial Intelligence game by 2030?
Looking to agentic AI
As telecom companies evolve their AI investments, embracing agentic AI means tapping into greater cognitive intelligence. Capable of being highly adaptable and able to continuously adjust to their surroundings, these AI systems can help telecom providers streamline their operations and enhance customer experiences.
By navigating complex IT landscapes, agentic AI delivers self-serve capabilities that boost operational efficiency across multiple channels. When combined with a context mesh, which acts as the real-time pulse of the organisation, agentic AI can process disparate signals from thousands of interconnected systems, turning them into actionable insights and immediate responses. This is especially critical as traditional data warehouses often fall short due to outdated information and inaccuracies.
Ultimately, agentic AI and a context mesh, when paired together, enable telecom providers to transition from reactive to proactive service models, fostering a more responsive, intuitive approach that boosts both operational performance and customer satisfaction.
Tuning into customer needs
Today’s customers expect fast, efficient, and personalised service from their telecom providers, from first interaction through to post-purchase support. Meeting these expectations is critical to driving business growth and fostering customer loyalty.
With the advent of AI, this vast reservoir of previously untapped data transforms into fertile ground for opportunities to cultivate new services, improve existing ones, and elevate customer experiences while streamlining operations. However, the road to successful adoption comes with its challenges. By strategically implementing a context mesh, telecom companies can deliver real-time data and context to AI agents and models, propelling their organisations forward in the AI era.
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