TL;DR:
- AI is at the forefront of tech innovation, driving productivity gains through cloud computing, language models, and natural language processing.
- Telecommunications operators are embracing AI for internal optimization and new revenue streams.
- “AI-native” telecoms require a foundation in cloud-native architecture.
- Modernizing network tech, transitioning to 5G Standalone core, and OSS/BSS upgrades are essential steps.
- AI’s role extends beyond marketing, focusing on enhancing the product and customer experience.
- Overcoming data fragmentation is a key challenge for AI adoption.
- Digital twins and Gen AI offer solutions to bridge data gaps.
Main AI News:
In the fast-paced world of technology, artificial intelligence (AI) stands at the forefront of innovation, fueling dreams of enhanced productivity through cloud computing, expansive language models, and natural language processing capabilities. Telecommunications operators, recognizing the winds of change, are pivoting towards AI, aiming to optimize their internal operations and explore customer-facing applications that promise newfound revenue streams. Amidst the buzz, even though 6G networks remain a distant horizon, there’s growing chatter about crafting the next-gen cellular systems with an “AI-native” essence. But what exactly does this entail?
The Prerequisite: Cloud-Native Meets AI-Native
McKinsey and Company’s recent report, titled “The AI-native telco: Radical transformation to thrive in turbulent times,” sheds light on the path ahead. To delve deeper into the vision and practicalities of achieving this transformation, RCR Wireless News engaged in a conversation with McKinsey and Company Senior Partner, Tomás Lajous, a co-author of the report. Lajous emphasized a crucial point: being cloud-native serves as the bedrock for an AI-native approach. He noted that, at present, many operators are yet to fully embrace cloud-native architecture.
“The concept of a cloud-native telco,” Lajous explained, “is inseparable from that of an AI-native telco. If we were to establish a telecom company from scratch today, the optimal approach would undoubtedly involve placing AI at its core. This entails integrating AI into every decision and operational facet, fostering a culture that champions AI adoption, from marketing and call centers to network management.”
To realize this vision, Lajous asserted, demands a robust technical infrastructure, which is best achieved by embracing cloud technology. Hence, cloud infrastructure is an imperative component of the AI journey.
Not a Fantasy, But a Forward-Thinking Concept
While starting a telecom company from scratch is an elusive luxury, becoming AI-native is more of a forward-thinking concept than a predefined destination. Lajous underlined several critical elements that operators must incorporate into their strategies: modernizing network technologies across the board, transitioning to 5G Standalone core, and upgrading Operational Support Systems and Business Support Systems (OSS/BSS). The latter pertains to the provisioning, charging, and consumption of services within a cloud-native network. Lajous also acknowledged that regulatory constraints, user consent, and other factors have restricted operators from fully capitalizing on the highly personalized and contextualized data at their disposal.
AI for Enhanced Product Enhancement
Lajous stressed that the role of AI extends beyond traditional profiling and marketing. Fundamentally, it revolves around improving the product itself. Telecom has grappled with assessing customer experiences within mobile networks. AI steps in by providing the means to comprehensively monitor network performance and evaluate it against individual needs.
With this invaluable data, operators can refine their offerings, enhance customer experiences, and, subsequently, create a distinctive edge. One significant challenge, however, is the fragmented nature of data within operator organizations. To be an asset to AI tools, data must be unified, organized, and accessible, a hurdle that many enterprises, including operators, confront.
“In the telecom space,” Lajous explained, “we’ve been trapped in a vicious cycle where bad data leads to inadequate AI, resulting in reduced emphasis on data generation, which, in turn, leads to even poorer data quality. But we are breaking free from this cycle.” He outlined two key elements in this transformation: the adoption of digital twins as the foundation for AI strategies, rather than relying on the use case-specific datasets, and the role of Generation AI (Gen AI) in addressing these challenges. Lajous exemplified how Gen AI can streamline the handling of voluminous circuit inventories and documentation, enabling the creation of digital twins that can undergo AI modeling— a prime example of how digital twins and Gen AI can bridge the data gap.
Conclusion:
The telecom industry’s shift towards becoming AI-native signifies a strategic move to stay competitive and customer-focused. Embracing cloud-native architecture, modernization, and AI-driven product enhancement will reshape the market, offering operators new avenues for growth and innovation. This transformation is not just a trend but a pivotal step in securing a prosperous future in the telecommunications sector.
