AIOps to the rescue: AI-driven automation is more than about faster and cheaper networks

Network transformation enabled by 5G and other new technologies promises higher capacity, lower latency, greater resiliency, improved sustainability, and overall increased efficiency. With these advances, service providers can launch new services and deliver better experiences to users.

But there’s a price to pay for all these gains, and it’s not just about capex. The real cost is an explosion in complexity. For decades, we managed networks with tried-and-true processes, but these methods are now woefully inadequate for the growing complexity and dynamic service requirements of today’s networks.

In this post, I’ll give an overview of how the benefits of AI-driven automation in AIOps can expand over time as the technology evolves and integrates into our networks. In upcoming posts, I’ll address the challenges operators face in adopting AIOps and outline the roadmap toward fully automated operations.

What we need to give up and what we gain

AIOps demands a major shift from our traditional approach to running networks. We need to rely less on individual judgment to manage the network and solve recurrent issues. Instead, human experts should focus on training and supervising AI/ML models, setting targets for them, and ensuring those targets are met.

In return, AIOps lets service providers streamline and automate many processes, boosting network efficiency, performance, and user experience while reducing the need for manual intervention. AI/ML models introduce new hardware and processing demands, but successful automation will lower overall opex by improving network efficiency and trimming staffing costs.

AI and automation need each other

AI and automation together can relieve us of the tedious, repetitive manual tasks involved in network management, and they significantly boost operational efficiency in the process.

In AIOps, AI and automation complement each other in crucial ways. AI models can sift through the massive datasets that service providers collect, but can’t manually analyze them. They have the compute power to extract relevant insights from this data, suggest corrective actions, identify and resolve issues in real time, and even predict and prevent network disruptions before they happen.

Automation makes AI-driven processes scalable and sustainable. It keeps the AI models productive by orchestrating tasks like anomaly detection and root cause analysis (RCA) across the network, and by triggering preventive or corrective actions as needed.

A low-hanging fruit in the short term

AIOps adoption requires an initial investment of time and money to transition to and integrate new processes and practices. However, if the first use cases are wisely chosen, the performance and cost benefits will quickly become evident and measurable in the short term.

Service providers should see improvements in metrics that are easy to quantify, such as power consumption, downtime, mean time to repair, service reliability, maintenance costs, and even truck rolls. Tracking these improvements not only proves the automation is working but also provides feedback to fine-tune the system when needed.

Wait, there is more

All the benefits we’ve discussed so far come from automating tasks based on our human understanding of how networks work. Initially, AIOps essentially mimics and improves what we have been doing manually, doing it faster and more efficiently. That’s a valuable first step. AIOps can help us further reach the ultimate goal of autonomous networks that can run themselves.

As AI technology progresses toward artificial general intelligence (AGI), AIOps models that started out learning from human expertise will become more insightful and creative. They’ll begin to find new solutions that we humans might never have envisioned. Some of these ideas may seem odd or counterintuitive at first, but they could prove to be exactly what the network needs.

Ultimately, networks will operate themselves with minimal human intervention, driven by AI models with a deeper and fundamentally different understanding of network dynamics than we have today. AI has the potential to turn complexity into a gold mine, extracting network efficiencies that we cannot foresee today.