Artificial Intelligence
MWC 2026: Huawei embeds AI into network control
Huawei has set out a network framework designed to move AI from advisory analytics into the operational core of telecom networks.
The AI conversation at Mobile World Congress in Barcelona this week moved from network optimisation tools to AI systems that take operational decisions inside the network itself.
Huawei introduced its AI-Native Network Framework for Intelligent Operations, describing it as an architectural model that integrates artificial intelligence directly into telecom control systems. The emphasis falls on operations, with AI positioned as part of the network’s execution layer.
Telecom operators already deploy AI across planning and assurance functions. Predictive maintenance models flag equipment likely to fail. Traffic analytics anticipate congestion. Energy optimisation tools reduce power consumption across radio sites. In most deployments, these systems generate recommendations that engineers assess and implement. Huawei’s framework sets out a structure in which AI agents carry out defined actions within established policy boundaries.
The architecture rests on three components.
- Outcome-driven operations.
Network management is tied to measurable service objectives such as latency commitments, throughput thresholds and enterprise service guarantees. Instead of adjusting parameters solely to maintain engineering indicators, the system evaluates whether defined service outcomes are being met and acts accordingly. - A unified digital twin.
Data from radio, transport and core domains is consolidated into a continuous model of the network’s assets, topology and performance state. This digital twin enables cross-domain analysis before operational changes are applied, reducing the risk of unintended consequences across interconnected systems. - Agent-based execution.
Domain-specific software agents operate within defined scopes. They can reconfigure parameters, allocate capacity or initiate remediation processes automatically. Engineers retain governance authority and set policy constraints, while routine adjustments shift into automated workflows.
Huawei presents the framework as part of its broader 5G-Advanced strategy. As operators expand into private networks, industrial connectivity and differentiated service tiers, operational complexity rises. Each additional service category increases the number of variables that must be configured, monitored and maintained across the network.
Enterprise customers purchasing dedicated connectivity require defined performance commitments backed by service-level agreements. Delivering those commitments consistently demands coordination across radio access, transport routing and core processing layers. Huawei argues that AI embedded within the operational architecture can respond faster and at greater scale than manual intervention models.
The competitive landscape reflects similar ambitions. Ericsson continues to advance autonomous network initiatives built around intent-driven automation and cognitive software layers. Nokia promotes its Autonomous Networks Fabric to reduce manual lifecycle management and increase closed-loop automation. Hyperscale cloud providers are also positioning orchestration platforms as part of telecom transformation efforts. Across the sector, vendors are working to elevate AI from analytical support to operational control.
Huawei’s contribution lies in formalising an end-to-end framework that defines how data modelling, decision logic and execution interact. Rather than presenting isolated AI tools, the company is defining a reference architecture intended to guide deployment across domains.
Integration remains a practical consideration. Telecom networks often combine equipment from multiple vendors and incorporate legacy systems that predate current automation platforms. Embedding AI into operational control layers requires alignment with existing management systems, compliance with regulatory oversight and safeguards to ensure accountability in automated decision processes.
Telecom infrastructure forms part of national critical infrastructure in many markets. Automated control mechanisms must demonstrate stability, transparency and resilience. Audit trails and governance structures become as significant as performance improvements.
Huawei indicated that intelligent operations solutions built on the AI-Native Network Framework will be demonstrated during MWC. These solutions target cross-domain coordination, automated fault handling and resource optimisation across evolving 5G-Advanced environments.
The announcement signals a shift in emphasis from incremental optimisation toward architectural integration. As operators look to improve operational efficiency and meet growing enterprise demands, AI is moving closer to the centre of network control. Deployment experience and operational metrics will determine how widely such frameworks are adopted in live production networks.
