PARTNER CONTENT: On the face of it, the rollout of 5G has been rapid and impressive. According to James Joiner of GSMA Intelligence, nearly 350 operators worldwide have now launched 5G networks, present in more than 135 markets. Adoption has accelerated at a blistering pace, with 5G connections expected to account for one-third of the global total by the end of 2025.<\/p>\n\n\n\n
Yet, for all its technical success, certain promises remain unfulfilled. The vision of 5G as an engine for unprecedented financial growth, particularly in the enterprise sector, has largely stalled. Annual mobile revenue growth has seen only modest improvements, and operators have struggled to charge a premium for 5G connectivity. While niche successes like Fixed Wireless Access in North America exist, the massive revenue uplift from enterprise digitalisation, once forecast to be in the trillions, has not materialised.<\/p>\n\n\n\n
So, what happened to the smart cities, the autonomous vehicles, and the immersive experiences? The industry is at a crossroads, and a recent Mobile World Live webinar revealed that the key to unlocking this dormant value lies not in chasing new, speculative services, but in a fundamental transformation of the network itself through open source, AI, and automation.<\/p>\n\n\n\n
The 5G promise vs. the 5G reality<\/strong><\/p>\n\n\n\n At its inception, 5G was positioned as the great driver for enterprise digitalisation. Rimma Iontel, Chief Architect for Telecommunications at open source company and IBM subsidiary Red Hat, recalls the vision: \u201cThe core aspiration was an upgrade from providing consumer voice and data services to high margin enterprise services.\u201d This justified massive upfront investment, with spectrum alone costing operators billions.<\/p>\n\n\n\n However, the reality fell short. The cost of network upgrades exceeded projections, requiring expensive new cell sites and backhaul. Core and RAN applications were slow to adopt a true cloud-native model, and the widespread adoption of the non-standalone (NSA) 5G core, chosen for its faster deployment, became an obstacle, as it lacked the capabilities like network slicing needed for advanced enterprise services.<\/p>\n\n\n\n \u201cBusinesses very pragmatically decided that they didn\u2019t need an upgrade from good enough technologies like Wi-Fi,\u201d Iontel explained. The anticipated revenue to fund further modernisation and new services never materialised, leaving operators with impressive networks but strained finances.<\/p>\n\n\n\n James Joiner highlighted GSMA Intelligence research that reinforces this, noting a \u201cnotable drop off in mobile Capex\u201d as operators rethink investment. The transition to 5G Standalone (SA), seen as critical for unlocking new monetisation, has been slow, with less than a quarter of operators with live 5G networks having deployed it. Concerns about technology maturity, organisational readiness, and a clear return on investment are causing hesitation.<\/p>\n\n\n\n Efficiency as the new revenue<\/strong><\/p>\n\n\n\n Faced with this monetisation challenge, the industry\u2019s focus is shifting. GSMA Intelligence\u2019s survey data reveals that operators\u2019 primary goals for technologies like AI and 5G Advanced are no longer solely revenue generation.<\/p>\n\n\n\n If the promises of 5G haven’t materialised because of complexity and high operational costs, how do AI and automation fix this without just adding more complexity? \u201cBy not tackling the problem wholesale, by breaking it into manageable pieces. Harnessing that flood of information through a unified data framework is the first step for AI ops,\u201d said Rimma Iontel, adding: \u201cYou can bring the power of machine learning to greatly reduce the complexity of uncovering network problems, and then automating the resolution.\u201d<\/p>\n\n\n\n James Joiner added: \u201cBuilding on what Rimma said, I think one of the key areas we see for applying AI is around energy efficiency. We\u2019ve seen operators cite reductions in energy usage somewhere between 10 and 20%, where AI can have an immediate effect and show that measurable return.\u201d<\/p>\n\n\n\n Taking a pragmatic approach to efficiency is echoed in operator priorities. Over 40% cite efficient network operations as the primary goal for AI investment, compared to only 25% who point to revenue generation. The initial use cases are clear, network planning, optimization, and predictive maintenance.<\/p>\n\n\n\n Open source as the foundational enabler<\/strong><\/p>\n\n\n\n This focus on operational efficiency and intelligence is intrinsically linked to open architectures. Open RAN, for instance, is now viewed by over 50% of operators as a top-three technology for driving OPEX savings. The conversation has evolved from just openness to how it enables greater automation and intelligence.<\/p>\n\n\n\n An open source, cloud-native platform provides the essential foundation. It offers the uniform operational experience needed to manage complexity across a hybrid environment, from the customer edge to the public cloud. \u201cBy providing a foundation on which you can build services that suit your goals and the needs of your customers, open source aims at versatility, increased productivity, and efficient operations,\u201d noted Iontel.<\/p>\n\n\n\n This approach prevents vendor lock-in and fosters a collaborative ecosystem. It allows operators to own their data and their AI models, applying them consistently across the network to reduce manual processes, increase reliability, and even optimise capital expenditure through more efficient network planning.<\/p>\n\n\n\n \u201cOpen source gives you that freedom of choice,\u201d added Iontel. \u201cWith an open platform environment, you can deploy your AI apps anywhere from the edge to the core, you own your data, you own your models, you make decisions. You have the control of your network.\u201d<\/p>\n\n\n\n Forging a pragmatic path to 6G<\/strong><\/p>\n\n\n\n The lessons from 5G are invaluable as the industry looks toward 6G. The vision for 6G is a distributed, intelligent fabric that interacts with the physical world. To avoid past pitfalls, the approach must be grounded.<\/p>\n\n\n\n The strategy according to Iontel is clear: \u201cFocus first on building an open, AI-enabled platform. Master AI operations to make the core business profitable. Use those efficiencies and the matured skills of your workforce to then introduce strategic, high-value services for specific enterprise verticals. These successes will fund the broader, longer-term rollout of 6G capabilities,\u201d said Red Hat\u2019s Iontel, adding: \u201cWith 6G what we need to do is to build and bake in open interoperable interfaces into the foundational standard and that in turn might stimulate an overall healthier ecosystem.\u201d<\/p>\n\n\n\n The journey to unlocking the massive financial potential of next-generation networks is not about a single killer application. It is about building an intelligent, automated, and fundamentally open network that operates with unprecedented efficiency today, creating the agile foundation for the profitable services of tomorrow. The vein of value is there; open source, combined with AI and automation, provides the tools to finally mine it.<\/p>\n\n\n\n Watch the full webinar on-demand here: https:\/\/www.mobileworldlive.com\/mwl-unwrapped-webinar-opening-5g-deploy-applications-faster-with-ai-and-automation\/<\/a><\/p>\n\n\n\n <\/p>\n","protected":false},"excerpt":{"rendered":"