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By 2026, artificial intelligence has moved decisively from experimentation to operational dependency within telecommunications networks. AI now optimises traffic, predicts failures, automates configuration, secures infrastructure, and increasingly influences strategic decision-making.
For telecom operators, AI is no longer just a tool for efficiency. It is becoming a new control layer – one that concentrates power in the hands of those who own the models, the data pipelines, and the compute infrastructure on which modern networks depend.
This article argues that the central question for European telcos is no longer whether to adopt AI, but who controls it, under what legal authority, and with what long-term consequences for sovereignty, security, and competitive position.
1. AI Has Become Infrastructure
AI is now embedded across the telecom value chain:
- Predictive maintenance and fault detection
- Radio access network (RAN) optimisation
- Energy management and sustainability optimisation
- Cybersecurity monitoring and anomaly detection
- Customer experience and churn management
In many cases, AI systems are no longer advisory. They act autonomously, adjusting network behaviour in real time. As a result, AI is becoming inseparable from the functioning of the network itself.
AI is becoming inseparable from the functioning of the network itself.
2. From Automation to Dependency
Early AI deployments focused on efficiency gains – reducing operational expenditure, automating manual tasks, and improving quality of service.
By 2026, a second-order effect is emerging: structural dependency.
Operators increasingly rely on:
- Vendor-trained models they cannot fully inspect
- Proprietary data formats and telemetry pipelines
- Closed optimisation loops embedded in network equipment
As AI systems grow more complex, replacing them becomes harder, not easier. The result is a new form of lock-in – not at the level of hardware or software, but at the level of intelligence.
3. Power Concentration in the AI Stack
AI-driven networks concentrate power across three layers:
- Compute – access to large-scale, high-performance AI infrastructure
- Models – ownership of foundation and domain-specific models
- Data – control over training, fine-tuning, and inference data
Today, these layers are dominated by a small number of global technology firms. For telecom operators, this raises uncomfortable questions about long-term autonomy and negotiating power.
AI risks becoming the mechanism through which value and control migrate away from operators toward platform providers.
4. AI, Security, and New Failure Modes
AI enhances security capabilities, but it also introduces new vulnerabilities:
- Model poisoning and data manipulation
- Adversarial attacks on inference systems
- Opaque decision-making during incidents
- Cascading failures triggered by automated actions
In highly automated environments, errors propagate faster and at greater scale. When AI systems fail, they often do so in ways that are difficult to predict or explain.
Security teams must therefore secure not only infrastructure, but decision logic itself.
5. Sovereignty and Jurisdiction in AI Operations
AI systems do not respect borders by default. Training, inference, monitoring, and optimisation frequently occur across jurisdictions.
For European operators, this creates sovereignty challenges:
- Where are models trained and updated?
- Which legal regimes govern access to telemetry and logs?
- Who can compel disclosure of model behaviour or data?
Without clear answers, operators risk losing effective control over critical network functions.
Regulation alone cannot resolve strategic dependency. Compliance does not guarantee control, resilience, or competitive strength.
6. Regulation: Guardrails, Not Strategy
The EU AI Act provides an important regulatory foundation, particularly around transparency, risk classification, and accountability.
However, regulation alone cannot resolve strategic dependency. Compliance does not guarantee control, resilience, or competitive strength.
AI strategy must therefore extend beyond legal conformity to encompass architecture, sourcing, and long-term capability building.
7. Strategic Choices Facing Telco Leaders
By 2026, telecom executives face a set of unavoidable strategic decisions:
- Do we treat AI as a commodity service or a core capability?
- Which AI functions must remain under direct operational control?
- How do we avoid intelligence-level lock-in while still moving fast?
- What role should European AI providers play in our ecosystem?
These are board-level questions with multi-decade implications.
8. Toward a Telco-Centric AI Model
A resilient AI strategy for telecom operators is likely to include:
- Modular AI architectures with clear substitution paths
- Strong data governance and model auditability
- Hybrid deployment models balancing scale and control
- Collaboration with European AI and cloud providers
The objective is not technological isolation, but strategic optionality.
Conclusion: AI as the Next Sovereignty Frontier
AI is rapidly becoming the intelligence layer that shapes how networks behave, adapt, and defend themselves. Control over this layer will define who holds power in the telecom ecosystem.
For European operators, the challenge is to harness AI’s transformative potential without surrendering autonomy, resilience, or strategic leverage.
These questions – at the intersection of sovereignty, security, and artificial intelligence – will be central to discussions at Netaxis Inspiration Day 2026, where industry leaders will confront not just what AI can do, but who it ultimately serves.
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