How AI And Digital Agents Revolutionize Network Operations
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Artificial Intelligence (AI) has the potential to revolutionize network operations. Companies that use AI to deploy digital agents can realize substantial gains by automating tasks, workflows, and using generative AI to aid in decision-making, among other things. This new paradigm has introduced a variety of opportunities across the network lifecycle, aiming to not only optimize operational costs but also enhance customer experiences, network quality, and return on investment.

Key areas impacted by digital agents include network planning, back-office operations, and customer experience management. But it is important to note that AI itself is not the answer. Any deployment of an emerging technology must be part of broader strategic and operational goals. Leaders need to be crystal clear on the problems they are willing to solve first.

The disruptive impact of AI and digital agents on networks

Imagine a future where your network operations are not just efficient but transformative, boosting efficiency and improving customer experience. By implementing AI in workflows and operations — from network design to maintenance — telecom operators can create a seamless ecosystem of digital agents that work together to drive meaningful business outcomes. This orchestration layer is the backbone of innovation, enabling these agents to collaborate — including with human agents — and adapt in real time, ensuring that your network is not only responsive but also proactive in addressing challenges.

The transformation is expected to result in a reduction of capital expenditures (CapEx) and operational expenditures (OpEx) by 20-40%, while simultaneously increasing return on investment (ROI) by 10% to 15% through automated and streamlined processes. Critically, AI-driven network automation is predicted to become standard practice in a near future, radically changing the level of operations performance. Companies seeking to maintain their competitive edge must be proactive in their strategies.

Exhibit 1: AI and digital agents’ potential on network spend
Digital Agents on Network

Transforming network operations with AI and digital agents

Imagine a future where your network operations are seamlessly interconnected across all stages of the value chain — strategy and planning, engineering and build, and operations and maintenance. This future is empowered by an ecosystem of digital agents working in harmony, transformings your approach to network management. This vision, allows you to respond to challenges and opportunities in real time.

When it comes to strategy and planning, AI can be used to determine demand and traffic patterns, ensuring that networks are always aligned with market needs. More informed decisions based on real-time data can significantly improve network architecture and dynamic experience optimization. Backhaul planning and site acquisition become streamlined as well, maximizing ROI and enabling companies to develop robust build plans that set a clear roadmap for success. The entire construction process in network engineering is better and faster, lowering CapEx by 5% to 10% and builds time by 40% to 60%.

From an engineering and building perspective, AI can speed site designs and construction models through the use of data-driven insights. Radio frequency (RF) planning and tower design are optimized for efficiency, while engineering plans and bill of materials (BOM) generation are created with minimal manual intervention. AI can also be used to improve vendor negotiations and contract management by analyzing real-time data and combing through reams of documents to find areas of improvement. Permitting and approvals are expedited through automated workflows enabled by digital agents. Project management could also be transformed with progress tracking and inventory management that keeps companies ahead of schedule and under budget. This approach transitions to managing project ‘by exception,’ focusing on the most critical items only, with the rest being managed by digital agents.

Exhibit 2: Examples of high-impact AI use cases in network

Boosting operational efficiency with AI in network management

In operations and maintenance, your centralized network operations center utilizes AI to monitor network performance and traffic, providing immediate insights into outage resolution and break-fix scenarios. Tower and RF operations could become optimized with predictive analytics, while field operations benefit from automated dispatch and scheduling, ensuring that maintenance is timely and effective. Based on client experiences, we predict that the use of AI in network monitoring can decrease incident resolution times by 40%. Energy management becomes smarter, and financial operations are optimized through software-defined networking (SDN), enhancing your overall operational efficiency.

Impact of AI on NOC engineer day-to-day operations

Netwok Monitoring

Today: John continuously monitors network health and performance utilizing multiple applications to identify issues.

Tomorrow: AI continuously monitors network health, rapidly identifying issues and predicting problems before they occur to reduce system downtime.

Incident Response

Today: John identifies, looks up, categorizes, and resolves network issues as they occur to minimize impact.

Tomorrow: AI alerts, triages, and runs root cause analysis, resolving incidents without human intervention and probes network for further issues. In many cases resolutions are proactive before the issue hase even happened.

Troubleshooting

Today: John troubleshoots network problems, ranging from connectivity issues to network outages using predetermined sequence. 

Tomorrow: AI analyzes system logs and error codes in real-time to automatically resolve issues, including dispatching field workers.

Maintenance

Today: John performs routine maintenance and manually updates network software based on predetermined schedules.

Tomorrow: AI predicts maintenance needs, prioritizing activities based on real-time data, including both software and in-field solutions.

Communication/ Coordination

Today: John compiles data from different sources and communicates network status, planned maintenance, and incident resolution to stakeholders.

Tomorrow: AI sends real-time updates and generates dashboards on network status, maintenance, and incident resolution, communicating to the critical stakeholders.

Documentation

Today: John keeps detailed manual records of network configurations, incidents, and maintenance activities.

Tomorrow: AI creates centralized, categorized and up-to-date documentation in a central repository for convenient access.

Exhibit 3: AI transformation framework

Navigating the AI transformation for network leaders

Network leaders are building specialized agents collaborating as one to deliver focused business outcomes. Building these agentic models has been found to be 30 % more efficient compared to more advanced off-the-shelf models. In addition to developing high impact use cases, network leaders need to build the orchestration layer enabling an ecosystem of digital agents to work seamlessly to really deliver this future state vision.

Beyond that, succeeding with AI, including generative AI and digital agents, requires building the right foundations to run models, scale use cases, ensure adoption, and optimize costs. These foundations go beyond the technical to encompass operating models, systems and data, talent and culture, and change management. Especially when it comes to preparing for the advent of next-generation operations, such as digital agent systems, telcos will need to answer several critical questions, as shown highlighted above.

Answering these questions won’t be enough to ensure successful scaling. Accelerating an AI-driven transformation requires additional key practices, such as clearly defining the business problem from the outset and regularly reviewing the sequencing of use cases. Other essential strategies will include embracing an iterative process that prioritizes progress over perfection, allowing for continuous improvement, and focusing on seamless integration rather than optimizing each individual component.

AI boosts network efficiency and maintenance

Unlocking the true value of AI is fundamentally about enhancing business outcomes. For network leaders, this means reimagining business processes and operating models from end-to-end. This involves designing streamlined processes, simplifying business rules, redefining network maintenance procedures, and automating activities wherever possible. By leveraging AI and digital agents, network organizations can accelerate this transformation journey. However, the primary challenge lies not in the technology itself, but in establishing sustainable practices, addressing misconceptions, and mitigating associated risks. Effectively managing change at scale will be crucial to overcoming this challenge and truly position network as an asset.