The pressure is real, and it's compounding
Telecom networks are no longer static infrastructure that gets configured once and left alone. Traffic patterns shift constantly, services are increasingly personalized, and the number of configuration options grows with every new technology layer added to the stack. That growing complexity isn't just an inconvenience – it's a risk multiplier. Manual configuration has always been prone to error, but when the number of variables explodes, so does the probability that something goes wrong.
At the same time, the pace of business has changed. Service delivery timelines that used to be measured in months are now expected in minutes, often across an entire ecosystem of partners. Add to this the steady erosion of "tribal knowledge" – the deep operational expertise that keeps multi-technology networks running – and you have a perfect storm: rising complexity, shrinking expertise, and shrinking patience for delay.
Then there's resilience. As networks become more distributed, more software-driven, and increasingly built on 5G and cloud-native architectures, the old fault management playbooks simply can't keep up. Manual escalation processes are too slow and too inconsistent for the volume and speed of incidents that modern networks generate.
Operational efficiency, cost pressure, and competitive dynamics are all pulling in the same direction. Networks must evolve from human-driven to machine-assisted, and ultimately to machine-led.
Why "just add AI" isn't a strategy
Faced with this pressure, it's tempting to treat AI as a plug-in upgrade – take a powerful model and let it take over. But AI-controlled autonomy brings its own serious challenges that can't be waved away.
A single, centralized AI making every decision creates what's often called the "God Object" problem – a single point of architectural complexity and failure that becomes nearly impossible to reason about, debug, or trust.
On top of that, AI models are fundamentally statistical. Errors and hallucinations aren't edge cases – they're inevitable, especially in the "unknown unknown" scenarios that real networks throw up constantly. Keeping models accurate requires continuous, costly retraining pipelines, and the energy consumption and data privacy implications raise real ethical and sustainability questions.
And underneath all of it sits an unavoidable truth: no matter how autonomous a network becomes, human beings remain accountable for every decision it makes. That accountability gap is exactly why "let the AI run the network" can't be the starting point.
What actually comes first?
Autonomy isn't something you bolt on – it's something you build toward. Before AI can be trusted with meaningful decisions, a Telco needs a solid foundation:
- Standardized service definitions
- Robust network ontology
- Precise network insight capabilities
This foundation is what allows AI to be introduced gradually, in carefully bounded ways, rather than as an all-or-nothing leap of faith.
This is the essence of the "divide and conquer" approach – breaking decision-making down into smaller, stable control loops, each with clear boundaries and clear accountability. AI then gets applied where it's genuinely strong (like correlating massive volumes of data to find root causes), while the riskier, more consequential actions remain governed by deterministic, explainable logic and human-defined intent.
Where you are supported
Tieto Tech Consulting operates in this space every day. With deep expertise in network automation, multivendor systems integration, and custom OSS/BSS development for mobile operators and communications service providers, our team – 1,400+ experts in AI, Data, Cloud, and Enterprise Applications – help Telcos move past the hype and build automation that actually works in production.
One of the technologies we bring to this challenge is SBA (System Behavior Analysis) – a novel approach that combines AI-based analysis of network metrics with a white-box model of the network itself. The result is dramatically more accurate anomaly detection and root cause analysis than traditional threshold-based monitoring, giving operations teams the precise insight needed to power a real control loop – not just another dashboard full of alerts.
Don't navigate this alone
The pressure on Telcos won’t dissolve, and the temptation to chase AI as a quick fix isn't going away either. Operators who get this right will be the ones who build the right foundation first – and bring in partners who understand both the technology and the operational realities of telecom networks.
We've teamed up with FNT Software and Inmanta to publish a strategic brief that lays out exactly how to approach this: "Are Autonomous Networks Real? A Telco's Guide on How to Move Forward." It dives into closed-loop architecture, the role of intent-driven orchestration, and how AI should – and shouldn't – be deployed across your network.
Download the full brief to get the complete picture, including a real-world autonomous network scenario walkthrough.
