# AI Adoption in NetOps: Trust and Governance Shape Future Operations > As businesses face increasing complexities in network management, the integration of AI and agentic systems is revolutionizing NetOps. Leaders must now focus on establishing trust and safety in expanding these autonomous capabilities. **Source**: blogs.cisco.com | **Published**: 2026-09-23 | **Type**: research ## Key Facts - 75% of organizations use AI in NetOps, indicating rapid market shift towards automation. - Average of 4,100 alerts daily vs. 21 cleared by practitioners highlights operational overload. - 80% of firms trust AI for autonomy, but demand governance and explainability for risk management. - AgenticOps enhances decision-making by providing a unified view across multiple operational domains. - Transition to AI-led operations signals a strategic shift in NetOps, emphasizing trusted autonomy. ## Summary The network operations (NetOps) landscape is undergoing a significant transformation as organizations increasingly adopt AI-driven solutions. A recent study by Cisco and Omdia reveals that 75% of IT and network operations leaders have already implemented AI for network management, with over half utilizing agentic AI systems that autonomously manage operational tasks. This shift is crucial as businesses face mounting performance expectations amid growing complexity from applications, devices, and security protocols. The rapid deployment of AI in NetOps signals a critical evolution in how organizations will manage their networks, emphasizing the need for a robust framework that ensures safety and efficiency. The current operational model for NetOps is reaching its limits. Network teams are inundated with data, generating approximately 4,100 alerts and events daily, with half related to network performance. Despite this overwhelming volume of information, practitioners can only resolve about 21 alerts per day, necessitating a workforce of around 100 specialists to manage the backlog effectively. This reality forces teams to prioritize urgent issues while neglecting others, leading to reactive maintenance rather than proactive management. The introduction of agentic AI is seen as a solution to alleviate this burden, enabling systems to autonomously correlate data, identify issues, and take action, thereby enhancing operational efficiency. The complexity of modern network environments means that many issues span multiple domains, complicating troubleshooting efforts. For instance, a slow application may have its roots in cloud connectivity, security policies, or even endpoint configurations. Traditional agents, which operate within a single domain, struggle to provide comprehensive insights. In contrast, Cisco's AgenticOps offers a unified view across various domains, allowing AI agents to make informed decisions based on a holistic understanding of the network. This capability is critical as it enhances the quality of decision-making and enables faster, more effective resolutions. Despite the growing comfort with AI in operational roles—80% of organizations express readiness to grant AI significant autonomy—there are essential conditions for its deployment. Leaders emphasize the importance of explainability, policy-based controls, and audit trails for AI-driven actions. Organizations are increasingly seeking a governed model where AI can operate autonomously but within defined boundaries. This approach fosters trust, as successful interactions between AI systems and human operators build confidence in the technology's capabilities. As AI transitions from simple prompt-and-response tools to more complex, goal-oriented agents, the role of the network is evolving. It is no longer just a conduit for data but becomes an active participant in the operational process. AgenticOps represents a paradigm shift toward human-led, agent-powered operations, where AI takes on a greater share of the workload. This evolution highlights the necessity for enterprises to establish a solid foundation for trusted autonomy, characterized by shared context, governed actions, and demonstrable improvements in user experience. The implications of this shift are profound. As organizations continue to integrate agentic AI into their NetOps, the focus will increasingly be on building the necessary infrastructure to support these systems. This includes ensuring reliable telemetry, maintaining shared context across domains, and establishing clear policies for AI behavior. The path forward will require leaders to prioritize these foundational elements, as they will determine the effectiveness and reliability of AI in critical network operations. The future of NetOps will hinge on how well enterprises can cultivate this environment of trust and autonomy, ultimately redefining operational excellence in the digital age. ## Entities - **Companies**: Cisco, Omdia - **Products**: AgenticOps - **Technologies**: AI, autonomous agents ## Key Concepts agentic AI, network operations, autonomy, operational complexity, trust in AI, shared context, governed action, performance expectations ## Definitions - **AgenticOps**: A framework for network operations that utilizes agentic AI to provide a unified view across multiple domains, enabling better decision-making. - **autonomous agents**: AI systems that can perform tasks and make decisions independently within defined parameters. - **operational automation**: The use of technology to perform tasks with minimal human intervention, particularly in network operations. - **trust in AI**: The confidence that users have in AI systems to make decisions and take actions based on reliable data and clear reasoning. - **governed action**: Actions taken by AI systems that adhere to established policies and approval processes to ensure safety and compliance. ## Use Cases - AI-led networks - automating network incident response - enhancing operational efficiency - improving decision-making in complex environments - reducing manual workload in network operations - enabling real-time monitoring and action ## Frequently Asked Questions **What is the role of agentic AI in network operations?** Agentic AI plays a crucial role in network operations by automating tasks and providing insights that help teams manage complexity and improve performance. It allows for faster decision-making and reduces the manual workload on IT specialists. **How do organizations ensure trust in AI systems?** Organizations ensure trust in AI systems by implementing explainability, approval gates for high-risk actions, and maintaining audit trails. This governance helps build confidence in the AI's decision-making processes. **What challenges do NetOps teams face today?** NetOps teams face challenges such as overwhelming amounts of data, alert fatigue, and the need for quick decision-making in complex environments. These challenges necessitate the adoption of AI to streamline operations. **What is the significance of shared context in AgenticOps?** Shared context is significant in AgenticOps as it allows AI agents to understand interactions across different domains, leading to more accurate diagnoses and effective actions. It enhances the quality of decisions made by the AI. **How is the future of NetOps expected to evolve?** The future of NetOps is expected to evolve towards greater autonomy and the use of AI systems that can operate with minimal human intervention. This shift will focus on building a foundation of trust and governance to manage AI's role in critical infrastructure. ## Links - [Read on Welcome.AI](https://welcome.ai/content/ai-adoption-in-netops-trust-and-governance-shape-future-operations) - [Original source](https://blogs.cisco.com/news/netops-is-already-deploying-autonomy-trust-will-decide-how-far-it-goes) - [Cisco](https://welcome.ai/company/cisco): Featured company --- Source: Welcome.AI | https://welcome.ai/content/ai-adoption-in-netops-trust-and-governance-shape-future-operations