Architecting Autonomous Telco Networks: Insights from the AIOps Market Leadership Survey
A deep dive into how AIOps and agentic AI are reshaping assurance, trust, and operational efficiency as CSPs progress toward truly autonomous networks.
CSPs face rising network complexity and growing expectations for flawless service. Our AIOps Market Leadership Survey reveals how operators are building trust with digital twins, agentic AI, and assurance-led strategies to accelerate the shift toward autonomous, high-performance networks.
Architecting Autonomous Telco Networks: Insights from the AIOps Market Leadership Survey
Communications service providers (CSPs) are managing networks that are exponentially more complex than they were five years ago. But when a problem occurs, customers now expect flawless service and instant resolution. Manual operations simply can’t scale to meet these demands, leading the industry to invest heavily in AIOps and autonomous network operations. Yet most operators aren't ready to deploy at scale, and the roadblock isn’t technology – it’s trust.
To understand how operators are navigating this challenge, Amdocs conducted the AIOps Market Leadership Program survey with Heavy Reading. The findings reveal how CSPs are approaching autonomous operations, which technologies they’re prioritizing to build trust, and where they see the biggest deployment challenges. They also portray an industry that’s progressing deliberately, not recklessly, toward autonomy.
Building trust for autonomous operations
The pace of deployment varies significantly, reflecting operators' cautious approach to building trust. While 47% already have autonomous operations running in specific domains, 64% don’t plan to deploy closed-loop automation for another 2-3 years. And while some operators are targeting highly autonomous networks in the short term, most are taking a more gradual approach.
One main technology being employed to establish trust is digital twins, which enable operators to simulate AI-triggered changes and validate outcomes before rolling them out to production networks. The survey reveals that 58% of operators are already using the technology for monitoring with human oversight, while 55% use it to simulate and validate AI-driven changes (Figure 1). This continued human oversight points to another trust requirement: explainability. As AI systems grow more complex, operators need decisions they can audit and understand. Frameworks that make AI explainable – alongside digital twins for validation – give operators the confidence to move faster toward autonomous operations.
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Source: Heavy Reading (now part of Omdia), June 2025
Figure 1: What methods are your organizations using to establish trust for AI decisions and to advance its autonomous networks? (Select all that apply)
How CSPs are deploying agentic AI
Agentic AI is changing telco automation through intelligent agents that can reason, plan, and execute tasks with minimal human oversight. Here, the survey found operators are exploring different deployment strategies. 38% opted for in-house development using hyperscaler platforms (Figure 2), while the rest are split between network equipment providers, multivendor solutions, and independent software vendors.
Yet regardless of which path they choose, CSPs need partners who understand telco operations, particularly for legacy system integration. Indeed, 55% cite legacy OSS/BSS as their biggest challenge in adopting AIOps. Specialized automation and AI vendors fill this gap by providing standardized APIs, phased transformation strategies, and data curation capabilities that connect autonomous AI systems to decades-old infrastructure, together with pre-trained models for telecom use cases and real-time analytics built for network data.
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Source: Heavy Reading (now part of Omdia), June 2025
Figure 2: What is your organization’s strategy for deploying Agentic AI for network automation
Deploying AI agents
The survey reflects real urgency for deploying AI agents across NetOps processes, with over 70% planning to do so over the next two years (Figure 3). The first wave focuses heavily on assurance, including reactive troubleshooting and root cause analysis, proactive anomaly detection and degradation forecasting, and closed-loop remediation recommendations. This prioritization reflects the focus on improving network performance, reliability, and customer experience.
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Source: Heavy Reading (now part of Omdia), June 2025
Figure 3: When is your organization planning to deploy the following AI agents?
Prioritizing assurance
CSPs have clear priorities for backing up deployment plans, with 39% identifying automatic detection of network anomalies as their top AI use case over the next 12 months (Figure 4). Strategically, this makes sense, as integrating AI into assurance workflows delivers measurable improvements in fault detection and performance optimization – and ultimately, better customer experience.
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Source: Heavy Reading (now part of Omdia), June 2025
Figure 4: Which use case will your organization prioritize for AI transformation within the next 12 months?
The business case for AI-driven assurance
CSPs have clear expectations about where AI will deliver the most value, with 53% identifying fault and performance management as the highest-value area for AI integration, with customer care and service management systems close behind (Figure 5). The payoff comes from faster fault resolution, reduced downtime, and the ability to detect and fix issues before customers notice them.
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Source: Heavy Reading (now part of Omdia), June 2025
Figure 5: What value does your organization expect to gain from integrating AI-driven assurance with other network systems?
Next steps
Digital twins, explainable AI frameworks, and specialized vendor partnerships are addressing the trust challenge, and assurance is where operators are putting them to work first. This explains why 70% of CSPs plan AI agent rollouts within two years with a clear focus on assurance. It's where they can demonstrate AI's measurable value through faster fault resolution, reduced downtime, and better customer experience, while building the confidence they'll need for broader autonomous operations.
Amdocs Intelligent Networking Suite provides the unified data foundation, legacy system integration, and validation capabilities that autonomous networks require. Learn more here.
For operators focused specifically on assurance, Amdocs Service Assurance Suite delivers the AI-driven fault detection, performance optimization, and predictive capabilities the survey respondents identified as the highest priority. Explore the solution here.
The full survey findings and analysis are available in our white paper here.
