amplitude, polarization, and phase, AI can help networks detect accidental or malicious physical intrusion threats in time to prevent or mitigate their negative impacts, and alert performance issues earlier, and more accurately, than deterministic algorithms, improving infrastructure awareness, health, and resilience.

Predictive maintenance is another use case with considerable potential. AI models trained with historical and real-time data can forecast equipment and fiber failures before they occur, enabling operators to schedule interventions proactively and avoid costly downtime.

These AI-powered use-cases represent the next phase of automation, a phase where automation not only helps react to events but also anticipates and adapts to them. As these capabilities evolve, they will further empower network operators to deliver faster, more adaptable, and reliable services, leading to a better quality of experience.

Building future-ready networks

The adoption of network automation is a strategic journey, not a one-time task.

A practical way to begin this journey is by launching targeted pilot projects in collaboration with automation solution vendors. Operators can choose to automate specific functions, like capacity fulfillment, and expand over time, integrating new capabilities as their needs and the network evolve. This incremental approach minimizes disruption, allowing teams to test automation in controlled environments, building confidence, and demonstrating value. Starting small also makes it easier to iterate and improve before scaling up. Digital twin and emulation environments are also great means of testing automation before productizing it in a network. Using modular, standards-based approaches ensures that automation efforts are flexible, extensible, and future-proof.

Automation also gives operators an opportunity to rethink how teams work, collaborate, and contribute. One pressing challenge in telecoms today, especially for smaller operators, is the shortage of skilled professionals. As demand for bandwidth increases, many operators struggle to capture enough talent and develop deep domain expertise. Automation helps bridge this gap by streamlining mundane, repetitive tasks and accelerating diagnostics. This enables experienced professionals to focus on higher-value activities, bringing insight into the design, validation, and governance of the automation frameworks, and ensuring the systems are robust, reliable, and fit the desired modes of operation. Automation can also ease the integration of new professionals. With intuitive tools, automated workflows, and conversational AI, they can take on network operation tasks more quickly and confidently, adding value to the team sooner.

By embracing automation as a journey and allowing trust in the systems to grow, operators can create what will eventually become more agile and resilient organizations, ready to meet the demands of tomorrow’s digital infrastructure.

Now is the right time to automate!

AI-era networks are reshaping the telecom landscape, increasing network scale and complexity, and imposing more demanding service requirements. At the same time, operators face growing pressure to improve return on investment and unlock new revenue opportunities. Many are turning to automation as a strategic approach to enable a more agile and efficient network operation.

The good news is that the network automation tools and the enabling standards and frameworks needed to address these challenges are now mature and available to all. Network automation is no longer a future aspiration; it is delivering results today.

By starting incrementally and using standards-based solutions, operators can scale automation over time without disrupting existing operations. This journey enables them to work more efficiently, to respond to customer needs more effectively, and to embark on new growth opportunities more rapidly - all of which strengthen their competitive advantage.