Telefónica Deutschland expects artificial intelligence to reshape future network engineering and service delivery.
The operator sees a shift beyond traditional network operations. Until now, many operators used AI mainly to monitor faults, predict issues, and support maintenance. The next stage could place AI much closer to design and deployment work.
Eva Ulicevic, Director of Architecture, Strategy and Technology Enablement at Telefónica Deutschland, outlined the change in a Mobile Europe report. The report, sponsored by Blue Planet, focuses on trusted data and AI modernisation in operations support systems.
She said: “We are going through a fundamental shift from AI-assisted operations to AI-assisted engineering. AI will increasingly translate business requirements into technical service designs, generate network models, validate dependencies, and prepare deployment artefacts.
“This will significantly shorten service introduction cycles while making advanced network engineering accessible to a much broader group of engineers, reducing dependency on highly specialised expertise.”
This could change how operators launch new services. Today, service design often needs deep domain knowledge and manual coordination. Engineers must understand many systems, dependencies, and configuration rules.
With AI-assisted engineering, teams could move faster. They could define business needs, then let automation prepare much of the technical framework. This may help operators reduce delays and respond faster to enterprise demand.
However, this model also raises important questions. AI needs accurate, trusted, and current data. Poor data can create flawed network designs or unreliable service models. Operators must also keep engineers in control of final decisions.
The report also highlights intent-based operations. This approach lets teams describe desired outcomes instead of technical instructions. For example, a team could request secure low-latency connectivity for a site. AI would then map the needed network actions.
Ulicevic added: “Instead of expressing technical configurations, business teams and engineers will specify the outcomes they want to achieve.
“AI and automation will translate those intentions into coordinated actions across increasingly complex multi-domain networks, allowing operators to focus on business outcomes rather than implementation details,” she added.
For telecom teams, this direction looks attractive. Networks now span mobile, fixed, cloud, edge, transport, and partner domains. Managing all layers manually becomes harder with every new service requirement.
At the same time, operators must avoid over-reliance on black-box automation. Engineers need visibility into AI decisions. They also need tools to test, validate, and reverse changes when required.
For OSS vendors, the message is clear. Future platforms must combine automation, data quality, and engineering intelligence. They must also support explainable workflows and strong governance.
Telefónica Deutschland’s view reflects a wider industry trend. Operators want AI to move from support functions into core engineering workflows. If implemented carefully, it could shorten launch cycles and widen engineering participation.
The real challenge will be balance. AI can handle complexity at scale. But trusted data, human oversight, and clear accountability will decide its long-term value.
