Leverage intelligent network automation to amplify human expertise
The artificial intelligence (AI) revolution is in full swing, and AI is portrayed as the panacea to, well, nearly everything. Understandably, interest in AI technology also continues to grow among network operators. That is because the ability to harness AI network apps empowers intelligent automation for improved overall performance and faster service delivery.
As network operators face rising costs, shrinking revenue and increasing network complexity, the power of intelligent automation becomes ever more enticing. Yet, AI is not a magic bullet and it cannot replace human ingenuity. This technology is a tool to be used judiciously and with precision.
With that said, there are many ways that AI network apps can make a substantial difference in the near term, enabling network optimization and reducing total cost of ownership (TCO). Let’s explore the most important AI use cases to give you a head start.
Why do you need intelligent network automation?
As the network market continues to transform, network operators are continually squeezed by competitive pressures and shrinking profits. Compounding those challenges, the blending of next-generation and legacy network architectures is driving increasing complexity, particularly as new end-to-end service touchpoints are added to today’s networks.
Keeping pace with this evolving landscape requires greater flexibility, efficiency, agility and cost-effectiveness. Toward that end, savvy network operators are adopting intelligent network automation to enable significant performance improvements, speed troubleshooting and improve their overall business case for enhanced profitability.
With the rapid evolution of AI, intelligent network automation is continually offering innovative new capabilities. For example, automated network traffic steering helps reduce congestion and improve latency, responding to network conditions in real time to optimize performance. Likewise, automated lifecycle service orchestration, control and management functions contribute to a more harmonized architecture, instilling agility for enhanced network reliability and efficiency.
How to leverage AI network apps
Implementing AI for networks is paramount for operations teams to cope with increasingly complex architectures. Infusing AI intelligence into the network empowers the flexibility needed to manage diverse traffic content and fluctuating demand, allowing real-time network configuration with minimal manual intervention. In fact, AI network apps are now being used to enable a wide range of intelligent capabilities and business improvements.
- Planning and decision making: With AI for networks, operators can create a dynamic, virtual copy of their physical network using digital twins. With the help of sophisticated digital twin models, operations teams can model “what if” scenarios and predict future behavior, allowing them to anticipate service disruptions and plan upgrade projects for more informed decision-making.
- Network optimization: The power of AI-enabled network optimization contributes to significant time savings for operations teams. One such use case is when network issues arise, thousands of alarms can be generated in rapid succession, creating an overwhelming alarm storm that can take many hours to resolve. With AI and machine learning (ML) tools such as Virtuora AX Accelerated Root Cause Analysis (RCA), engineers can significantly reduce issue resolution time and predict new alarm data patterns, enhancing network analytics insights. This not only saves manual work, but importantly, it maintains improved overall quality of service (QoS).
- Sustainability and energy savings: For many network operators, energy spending now significantly outpaces business growth by as much as 50 percent or more. Therefore, energy consumption management is becoming a critical use case for AI network apps. AI-powered predictive behavior modeling can be used to switch capacity on or off as needed, helping to reduce environmental impact and support sustainability goals while maintaining service continuity and performance. Moreover, this enables network operators to fully leverage resources with less risk.
- Data analytics and intelligence: Increasing digitization of the network produces exponentially more data than ever before. Effective use and management of this data is key to successful operations and increased profitability. With AI intelligence and large language models (LLMs), AI-generated data sets can be used to mine specialized network knowledge, supporting evidence-based analysis and improved business forecasts. By distilling valuable insights, AI enables operations teams to model the relationship between user experience and QoS, capturing such metrics as throughput, delay and loss.
Meet your next ops team assistant
The growing complexity of performance management and service delivery across converged networks has made intelligent network automation a strategic imperative for all manner of mobile and optical networking. In this way, AI can be tailored to specific networking needs, allowing operators to enable seamless service, enhance customer experience, improve operational efficiency and meet service level agreements (SLAs).
Based on our vast telecoms experience, 1Finity network intelligence and analytics tools leverage proprietary AI models, vendor-neutral data management, analytical insights, visualization and AI/ML to help you speed and simplify this transformation. These AI network apps can be trained on diverse datasets to deliver actionable insights, enabling a wide range of planning, optimization, energy savings and intelligence capabilities. Use cases include anomaly detection and fault predictions, traffic prediction, network snapshots, alarm storm detection, root cause analysis, predictive network coaching and agentic AI.
A more intelligent future
The future network will be an intelligent system that autonomously learns, predicts and adapts to network conditions in real time. With the ability to coordinate resources across the entire infrastructure, this intelligent network will focus on high-level goals rather than just device management. As a result, network operations teams will benefit from such capabilities as:
- Autonomous learning and adaptation
- Seamless coordination of resources
- Protection against emerging threats
- Reliability and optimal quality
- Intelligent operator insights and control.
Fast-forwarding to 2033, imagine that the network engineer’s day might begin by reviewing a dashboard powered by multiple AI agents — each monitoring alarms, key performance indicators (KPIs) and SLAs. These autonomous agents would provide readouts, recommend actions, and even interact with business and customer personas directly. Example scenarios include:
- Autonomous vehicles and drones: Agents coordinate connectivity and maintenance.
- Business-driven network engagement: Enterprises query the network for service needs, and AI agents evaluate, recommend, and execute solutions.
- Event optimization: Preparing for large-scale events like the World Cup or Super Bowl with predictive, proactive network adjustments.
As we look further into the future, imagine advanced concepts like integrated sensing and communication (ISAC) for 6G, where radio signals serve both data transmission and environmental sensing. In this way, the network becomes a distributed radar and perception system, enabling smart cities, real-time monitoring and ultra-low latency applications.
Moreover, this intelligence helps advance development of digital twins, which play a crucial role by simulating changes before deployment, thereby ensuring safety and enabling closed-loop automation. This autonomous system will be able to simulate outcomes and orchestrate actions across domains, reporting risks and remediation options for optimized performance and customer experience.
Transforming tomorrow’s business model
With superior complex problem resolution powered by AI, the evolution to AIOps enables much more than just advanced technology. Network operators can leverage optimal workload placement and automated optimization of network parameters to increase performance and utilization.
With smaller, more specialized operations teams focused on policy engineering and automation supervision, energy/resource optimization will be streamlined with self-service portals. Likewise, automation will be managed by revenue-aware AI agents, and policy-driven orchestration will enable vast improvements in efficiency. Plus, with cost analysis integrated into service recommendations, operators can reduce TCO while maintaining quality of experience (QoE).
As AI evolves on the path to light-touch operations, we are paving the way to ever more autonomous networks that will continually troubleshoot and self-optimize, freeing up network teams for more valuable business operations. Ultimately, the advanced capabilities of AI network apps empower network operators to deliver differentiated service offerings based on more realistic QoS and QoE predictions. This enduringly transforms the competitive landscape to enable greater profitability and return on investment.
