AI in telecom is no longer a single project or a lab experiment. In 2026 it is a portfolio of deployments across radio networks, core networks, operations, customer workflows, and security, mainly because telecom estates generate vast telemetry and because operators are optimizing reliability, energy, and operating cost under regulatory and market pressure. The opportunity is real, but so are the failure modes, because telecom is a high consequence environment where automation mistakes can cause outages, degrade security controls, or introduce persistent misconfigurations that are hard to detect.
A neutral way to think about AI in telecom is to separate decision support from decision execution. Decision support compresses complexity into predictions and recommendations that humans can validate. Decision execution closes loops and changes network state, which raises the bar for safety constraints, rollback, auditability, and continuous validation. 3GPP explicitly frames AI and ML usage across multiple 5G system domains, including management and orchestration, the 5G core such as NWDAF, and NG RAN intelligence, which is a strong signal that AI is becoming embedded into telecom architecture rather than bolted on later.
AI in the RAN
In the RAN, AI is commonly used for traffic prediction, energy optimization, and anomaly detection on performance counters because the data is rich and the objectives can be quantified. The most consistent value appears when the model output is constrained, such as recommending parameter adjustments inside hard safety envelopes, or ranking cells and sectors that require human investigation. The risk is that the RAN is a tightly coupled control system, so an AI driven optimization can create oscillations and subtle quality regressions if the deployment lacks conservative guardrails and staged rollout practices.
A practical Europe reality is that RAN AI must coexist with multi vendor environments and strict operational change governance. That pushes teams toward measurable objectives, conservative automation, and a preference for explainable recommendations over opaque end to end control. 3GPP’s description of AI and ML functional arrangements across scenarios such as RAN management systems and gNB hosted inference is useful here, because it forces engineers to specify where training happens, where inference runs, and what data crosses trust boundaries.
AI in the 5G core and analytics functions
In the core network, AI is used for forecasting, anomaly detection, and decision support around congestion, policy behavior, and network exposure patterns, because core telemetry is often structured and repeatable. 3GPP’s framing of AI and ML in the core, including NWDAF, signals that analytics driven optimization is part of the 5G system evolution and not an optional add on.
The neutral risk statement is that core models can fail during topology changes, upgrades, and vendor swaps, and they can learn the wrong baseline if the training data already contained undetected compromise or chronic misconfiguration. This is why many operators treat AI in the core as a detection and diagnosis layer that proposes hypotheses with confidence scores, rather than an authority that overrides security controls or pushes live changes without an independent validation step.
GenAI copilots for telecom engineering
GenAI copilots are widely adopted in 2026 because they compress time spent searching documentation, summarizing incident history, drafting runbooks, and preparing change plans. The value increases sharply when outputs are grounded in internal sources such as runbooks, ticket history, and approved configuration standards, and when the copilot is integrated into workflows that force verification rather than encourage copy and paste changes.
The telecom specific limitation is that generic models often misunderstand telecom terminology, standards context, and operational constraints, which is why telecom specific evaluation matters. GSMA’s Open Telco LLM Benchmarks positions itself as a telecom oriented effort to develop and evaluate LLM performance using specialized benchmarks, datasets, and models, and GSMA has also published updates describing how the community evaluates frontier models and where gaps remain for network automation readiness.
Agentic AI and network operations
Agentic AI is where GenAI shifts from producing text to coordinating workflows and potentially executing actions. In telecom operations, the near term value is strongest when agents coordinate evidence collection, correlate alarms, propose hypotheses, and run safe checks that do not change network state. The risk grows rapidly when agents are given broad tool permissions, when execution is not bounded by policy constraints, or when rollback and audit are not first class features, because the blast radius of a wrong action in telecom is large and often cross domain.
A balanced position is that agentic AI is viable in narrow automation lanes with stable feedback loops and strict constraints, and it is risky as a general purpose operator for complex multi vendor estates. 3GPP work describing network automation enablers and the role of analytics functions such as NWDAF provides a useful architectural anchor for how automation interacts with exposure, analytics, and orchestration in the 5G system.
AI for telecom security and the security of AI
AI is both a tool and a target in telecom security. On the tool side, AI can help correlate weak signals across domains, prioritize alerts, and accelerate investigation by adding protocol context and anomaly summaries that would otherwise require specialist time. On the target side, AI systems can be attacked through poisoning, adversarial inputs, and prompt injection when copilots are connected to internal tools and sensitive data, and the operational risk includes accidental leakage of internal knowledge or unsafe automation triggered by malicious inputs.
A neutral operational stance is to threat model AI systems like any other critical service, apply least privilege to tool access, monitor model behavior for drift and abuse, and enforce evidence based outputs so that actions require verifiable sources. ETSI’s EN 304 223 is directly relevant because it defines baseline security requirements for AI models and systems, including generative AI in scope, and it is positioned as lifecycle based security requirements rather than a one time checklist.
Europe, governance, and standards alignment
In Europe, AI in telecom sits inside a governance triangle of technical standardization, assurance expectations, and regulatory pressure. A non biased view is that GSMA, ETSI, and 3GPP play different roles that often complement each other. GSMA convenes industry stakeholders and creates practical initiatives and benchmarks that help the ecosystem evaluate readiness and reduce fragmentation. ETSI produces European standards and guidance that influence procurement language and security baselines, including EN 304 223 for securing AI systems. 3GPP specifies the architecture and protocols that become the reality of what vendors implement and operators run, including how AI and ML capabilities are placed and used across 5G system domains.
Assurance and certification discussions also matter. ENISA describes work on a candidate EU 5G cybersecurity certification scheme through ad hoc working group structures and maintains a cybersecurity certification framework that includes schemes under development for 5G. This affects how operators and vendors in Europe think about auditability and evidence, which tends to push AI deployments toward stronger logging, traceability, and lifecycle governance.
Build versus buy in telecom AI
Most telecom teams in 2026 land on a hybrid approach. Vendor delivered AI features can be faster to deploy and more tightly integrated into specific network stacks, but they can be harder to validate and may be opaque in how they use data and make decisions. Internally built models can align better with local data governance and sovereignty requirements, but they require sustained MLOps discipline, data engineering maturity, and security hardening that telecom organizations sometimes underestimate.
A neutral evaluation framework is based on data sensitivity, required explainability, action authority, and cost of failure. Use cases that change network state or influence customer outcomes tend to demand higher assurance and tighter governance, while recommendation and triage use cases can deliver value earlier if they are instrumented and continuously validated.
Conclusion
AI in telecom in 2026 is best understood as a layered capability that turns telemetry into decisions, with value concentrated in well scoped domains and with risk concentrated where AI can trigger actions without strong guardrails. A balanced, Europe relevant approach treats standards and industry alignment as engineering inputs, uses telecom specific evaluation such as GSMA’s benchmark efforts to measure model readiness, anchors automation and analytics to 3GPP’s evolving system architecture, and applies lifecycle security baselines such as ETSI EN 304 223 to reduce AI specific cyber risk.
.jpg)
