Nokia's Mobile Core Dominance Signals AI/ML as the Future of Network Automation

Nokia has once again secured the top position in Omdia's 'Market Landscape: Core Vendors' report, marking its second consecutive year at number one for mobile core portfolio competitiveness. The 2026 report from Omdia placed significant emphasis on the growing role of AI/ML and analytics, recognizing their increasing importance in driving network automation, enhancing efficiency, and accelerating service development. Nokia demonstrated leadership across all seven evaluated categories, including automation, AI/ML, analytics capabilities, and cloud-native maturity. This consistent performance underscores the vendor's strategic alignment with the evolving demands of mobile network operators who are actively modernizing their core infrastructures. This development is critical for network engineers, architects, and DevOps professionals working within the telecommunications space. Nokia's sustained leadership, particularly in areas weighted heavily by AI/ML and automation, indicates a clear industry direction: manual network management is rapidly becoming obsolete. For practitioners, this means that proficiency in AI-driven automation tools and methodologies will be indispensable. The report's findings validate investments in cloud-native platforms and intelligent automation, signaling that these are no longer aspirational technologies but foundational elements for competitive mobile core networks. Those responsible for network strategy and implementation must recognize that the ability to leverage AI for predictive maintenance, anomaly detection, and dynamic resource allocation is now a key performance indicator. This trend fits squarely within the broader movement towards hyper-automation and AI-Ops across cloud and DevOps domains. Just as AI is transforming IT operations, security, and application development, its integration into network automation is a natural progression. The increasing complexity of 5G networks, with their distributed architectures, massive device connectivity, and stringent latency requirements, makes manual configuration and troubleshooting unsustainable. AI/ML provides the intelligence layer needed to manage this complexity, enabling self-healing networks, optimized traffic routing, and automated service provisioning. This aligns with the push for Infrastructure as Code (IaC) and GitOps principles, extending automation from infrastructure deployment to ongoing operational intelligence. The emphasis on cloud-native platforms further integrates networking into the broader cloud ecosystem, where automation and AI are paramount for scalability and resilience. In practice, this means practitioners should actively explore and adopt AI-enabled network automation tools and platforms. This includes familiarizing themselves with how AI/ML models can be trained on network telemetry data to predict outages, identify performance bottlenecks, and automate remediation actions. Organizations should prioritize upskilling their teams in areas like data science for network operations, machine learning engineering, and advanced automation scripting. Furthermore, when evaluating new mobile core solutions or upgrading existing ones, the depth and maturity of AI/ML integration for automation should be a primary consideration. The trade-off between initial investment in AI tooling and the long-term gains in operational efficiency, reduced downtime, and faster service delivery is becoming increasingly favorable, making proactive adoption a strategic imperative for staying competitive in the rapidly evolving 5G landscape.