Artificial intelligence is moving beyond the role of an assistant in enterprise IT. Instead of simply identifying problems or recommending solutions, newer systems are increasingly capable of analyzing operational conditions, making decisions and taking action.
Cisco and Omdia research published on September 23, 2026, highlights how quickly this shift is happening across network operations. The study found that 95 percent of surveyed enterprises believe their existing non agentic AIOps tools fall short in at least one significant area. At the same time, more than half are already running agentic AI in production.
This development is creating growing interest in AgenticOps, an approach in which AI systems can sense, reason and act across IT environments while people establish direction and guardrails.
Why Traditional AIOps Is Under Pressure
Modern enterprise infrastructure has become increasingly complicated. Cloud services, distributed applications, connected devices and AI workloads generate large volumes of operational information that IT teams must monitor continuously.
According to the Cisco and Omdia research, organizations surveyed generate an average of about 4,100 monitoring alerts and events each day, with more than half related to networks. The research estimates that manually clearing the daily network alert backlog would require roughly 100 IT specialists.
Moreover, 92 percent of respondents said performance issues commonly cross multiple domains and require correlation across ten or more tools. Consequently, IT teams can spend significant time moving between dashboards and investigating information that may ultimately turn out to be a false positive.
From Advisory AI to Agentic Operations
Traditional AIOps generally helps teams identify anomalies, analyze events and receive recommendations. The emerging model goes further by allowing AI systems to perform operational tasks.
The Cisco research found that 75 percent of respondents had already deployed AI for network operations, while 51 percent said they were running agentic AI that acts in production. Additionally, 82 percent were comfortable allowing AI to make at least some production network changes without prior human approval.
For example, an AI system could potentially reroute traffic, adjust wireless parameters, isolate a suspicious endpoint or help resolve an incident. Therefore, the role of the IT professional can shift from manually handling every operational task toward setting objectives, reviewing outcomes and managing exceptions.
AI Is Creating New Network Demands
The rise of AI is also changing the infrastructure that IT teams need to manage. AI applications can generate substantial volumes of data exchange, while autonomous systems may communicate and perform tasks at machine speed.
Cisco’s research states that AI traffic is on a trajectory to double every six months based on its analysis of aggregated direct to AI network telemetry. Cisco testing also found that tasks performed by agentic AI can generate up to 450 percent more total network traffic when agent generated traffic is included.
As a result, IT monitoring cannot remain focused only on conventional infrastructure metrics. Organizations increasingly need visibility across applications, networks, security systems and AI workloads.
Trust Becomes Central to IT Automation
Greater autonomy brings a corresponding need for transparency. An AI system that can change production infrastructure needs to provide enough information for IT teams to understand what happened and why.
Cisco and Omdia found that 69 percent of respondents require detailed explainability for agent driven actions. Furthermore, 36 percent said full observability that includes detailed tracing, summarized reasoning and post action audits represents the minimum acceptable standard.
Therefore, successful automation depends on more than technical capability. Organizations also need governance, visibility and clearly defined controls.
The Move Toward Integrated IT Management
Enterprise IT environments often rely on numerous specialized monitoring and management tools. However, fragmented information can make it harder to understand problems that cross several systems.
The Cisco research found that 86 percent of respondents viewed a single integrated platform as the most effective path forward. In addition, 84 percent expected to reach an AI led operating model within twelve months.
This points toward a broader change in the IT ecosystem. Instead of adding another isolated monitoring tool, organizations are increasingly looking for systems that can connect operational data and coordinate actions across different technology domains.
Impact on IT Professionals and Skills
The evolution of intelligent IT operations will also influence the skills organizations need. IT professionals may spend less time manually investigating individual alerts and more time designing workflows, validating AI decisions and establishing operational policies.
Meanwhile, understanding data, cybersecurity, cloud infrastructure and AI governance is becoming increasingly valuable. These changes connect with broader HR trends and insights, particularly around workforce development and technical reskilling.
Similarly, professionals following Technology insights and IT industry news are increasingly seeing AI, observability and automation discussed as connected parts of enterprise transformation.
Implications for Business Operations
Changes in IT infrastructure can affect departments beyond technology. More reliable systems can support customer facing applications, digital sales channels and internal business platforms.
Consequently, developments in intelligent IT operations can influence Sales strategies and research as businesses seek more dependable digital experiences. Marketing teams can also benefit from resilient customer platforms, while finance teams may examine the operational costs and potential efficiencies associated with automation.
These connections make Finance industry updates and Marketing trends analysis increasingly relevant to technology leaders because infrastructure decisions can influence broader business performance.
AgenticOps represents a shift in how organizations think about IT management. The objective is not simply to automate more individual tasks. Instead, the focus is increasingly on creating systems that can understand operational context and coordinate actions across complex environments.
However, autonomy should not remove accountability. Organizations still need clear policies around permissions, monitoring, human intervention and recovery when automated decisions produce unexpected results.
Omdia’s research also emphasizes the importance of human involvement and governance as enterprises move toward increasingly autonomous AI systems.
Valuable Insights for the Future
The Cisco and Omdia findings suggest that enterprise IT is moving toward a model where AI can participate directly in operational decision making. The speed of this transition makes visibility and governance particularly important.
For businesses, a practical approach is to begin with clearly defined use cases where outcomes can be measured. Organizations can then establish appropriate permissions, monitor performance and gradually expand autonomy as confidence grows.
The larger opportunity lies in combining intelligent automation with human expertise. As AI systems become capable of handling more operational work, IT professionals can increasingly focus on architecture, strategy, security and the complex decisions that require broader business context.
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Source : cisco.com
