What exactly defines an AI-native Telco? It’s no longer sufficient to simply be “AI-ready.” The landscape has shifted even in the last few years. Today, telcos need a deeper integration of artificial intelligence not just a tool but as a fundamental capability woven into the very fabric of their organization. According to McKinsey, an "AI-native" telco is one where "AI is viewed as a core competency that powers decision making across all departments and organization layers." I’d go further than this and argue that an AI-native Telco is one where AI is not just a way to drive efficiency and productivity from existing processes, but where it is the catalyst for growth in new markets with new offers that attract new customers.
This transformation is intrinsically linked to current imperatives: bolstering competitiveness at both corporate and national level; the relentless pursuit of productivity; and establishing clear leadership in the global digital economy. Telcos have both the opportunity, and in reality the necessity, to position themselves as customer champions in the ongoing wave of technological transformation. Mastering AI, both internally, and on behalf of customers is essential for telcos.
AI to overcome internal challenges
Telecom operators face constant pressure to reduce costs and increase returns in a market where revenue growth has been largely flat. They must constantly square the circle to make billion-dollar investments in infrastructure to keep up with demand whilst facing increasing headwinds to making short term returns on those investments. AI offers a potent pathway towards achieving this balance. By optimizing network operations, predicting failures, automating routine tasks, and enabling smarter capital expenditure planning, AI helps operators significantly reduce both capital and operational expenses. It can help maximise efficient use of bandwidth, capacity and resources.
Creating differentiation and delivering enhanced customer experience with AI
In today's hyper-connected world, customer expectations are constantly escalating. AI empowers telcos to meet and even exceed these expectations through personalized offers, proactive issue resolution, AI-powered virtual assistants, self-service flexibility and by predicting and preventing customer issues before they even arise.
Perhaps the most important defining characteristic of an AI-native telco, is its use of AI as the engine for creating new revenue streams and solidifying the telco’s role in the broader digital ecosystem. AI-native operators focus on emerging AI use cases, including call center optimization, enhanced network services like network slicing, connectivity as a service (CaaS) and especially pertinently, sovereign infrastructure offerings. The opportunities to use AI to not only offer high-value services but to act as a trusted partner for enterprises to consume and offer their own AI-enabled applications, have the potential to radically transform the role and reputation of telcos. A proactive approach, using AI to drive not just productivity and efficiency but innovation, is vital for telcos to remain relevant and competitive in the face of rapidly evolving technological landscapes.
Any journey starts with the first step
Telcos have made significant progress in adopting AI in the past years but there is uneven deployment of AI across different areas of telco operations. It’s important to remember that there is more to artificial intelligence than the generative AI (GenAI) and large language models (LLMs) that have recently caught the limelight. Telcos have been using discriminatory AI and machine learning for years in areas as diverse as fraud detection and predictive maintenance. Indeed, non-generative AI continues to be the bedrock of many critical network and operational improvements and currently accounts for almost 70% of operators' AI activities.
However, there are approaches to accelerate AI deployment at scale. It goes without saying that data is the linchpin of any successful AI strategy. Addressing challenges related to data availability, accessibility, quality, management, and, crucially, trust in data usage is essential. Siloed and fragmented data architectures need to evolve to provide a unified foundation for AI model training and inference. In hybrid, multi-cloud environments it makes sense to bring AI to the data. Teradata’s in-database processing capabilities can deliver powerful AI models that run where data already resides.
Demonstrate business value
It is also paramount to demonstrate clear business value from AI development. Operators need to identify and prioritize AI use cases that directly align with their key business objectives and provide a demonstrable return on investment in production. Finally, overcoming stovepipe development, where different teams within a telco develop AI solutions in isolation without sharing knowledge or best practices, is critical.
We see the industry still largely at the stage of proof-of-concepts (POCs) and experiments rather than widespread, scaled deployments, as evidenced by surveys from TM Forum and Analysys Mason. In the coming weeks we’ll explore how to scale and deploy at speed to bring the AI-native telco to life. We will look specifically at AI’s role in driving enhanced customer experience (CX), improving and innovating network operations, and creating new revenue streams from enterprise customers.
The journey towards digital competitiveness for telcos is inextricably linked to the imperative of becoming AI-native. To do so requires a strategic focus on building robust and accessible data platforms specifically designed for AI. By addressing the challenges of demonstrating business value and managing data effectively, telcos can move beyond experimentation to fully embrace the transformative power of AI, solidifying their crucial role in the evolving digital landscape. Stay tuned to find out how.