AI Reality Check: Why IT Teams Are Embracing AI

The conversation around AI in IT operations has changed quickly. Not long ago, organizations were asking whether AI could deliver meaningful value. Today, the question is how quickly they can deploy it.

Executives see the promise of greater efficiency. Vendors are promoting autonomous operations, self-healing networks, and AgenticOps platforms that can resolve incidents with minimal human intervention. Increasingly, AI is viewed as a strategic imperative. But the operational reality is more complicated. For the engineers and operations teams responsible for service availability, outage response, and digital experience, enthusiasm for AI is often tempered by skepticism. The issue is no longer whether AI has potential. It is whether the data feeding those systems is trustworthy enough to deliver reliable outcomes at scale.

Recent findings from NETSCOUT's Cisco Live 2026 survey of nearly 950 IT professionals reveal a striking disconnect between enterprise AI ambition and operational readiness.

AI Adoption Is Accelerating

According to the survey, 82.8 percent of organizations are actively discussing, testing, planning, or running AI and large language model (LLM)-based IT projects, which is clear evidence that AI adoption is no longer theoretical. However, the level of maturity varies widely, with many organizations still evaluating use cases while others move toward production deployments.

Figure 1: AI project adoption stages

These findings suggest that AI has crossed an important threshold, and for most enterprises, it is becoming an operational reality. However, readiness remains uneven, with nearly 1 in 5 organizations saying they are not yet ready to engage in AI initiatives. Others remain in the evaluation phase, discussing possibilities rather than executing deployments. 

This gap suggests that deploying AI is relatively easy compared with operationalizing it successfully. As organizations move from experimentation to production, the focus must shift from AI models themselves to the quality of the operational data, visibility, and governance that support them.

What Are the Roadblocks?

The survey identified today’s biggest barriers to effective AI for IT operations (AIOps) or AgenticOps. The results point to a common theme. The barriers to effective AIOps and AgenticOps are not isolated to technical issues. Rather, visibility gaps, poor data quality, limited trust in AI outputs, and fragmented workflows all received similar levels of concern, suggesting that organizations are facing a broader operational readiness challenge.

The largest single response was for “All of the above,” at 27.2 percent. This result underscores how interconnected these challenges have become.

Figure 2: Biggest challenges preventing effective AIOps or AgenticOps

These challenges all point to the same underlying issue: trust. Poor visibility, fragmented tools, noisy telemetry, and incomplete context make it difficult for IT teams to validate AI recommendations or understand how conclusions are reached. When the evidence behind an AI output is unclear or conflicts with operational reality, confidence quickly erodes.

The findings suggest that successful AIOps isn’t fundamentally an AI problem. Rather, it’s a data and visibility problem.

Visibility as a Priority

The survey also shows that enterprises understand the need for visibility into AI-driven environments. Two thirds (65 percent) of respondents said monitoring AI processes and services is either highly valuable or mission critical. This is an important data point. Organizations are not interested in deploying AI as a black box. They need to observe, validate, and understand how AI-powered systems behave in production environments, particularly as AgenticOps platforms evolve from making recommendations to taking autonomous actions.

Before enterprises allow AI agents to make operational decisions, they need confidence that those decisions are based on accurate, complete information. That confidence starts with visibility.

Figure 3: Importance of AI monitoring

Why Better Data Matters More Than Better AI

According to the survey, only 9.7 percent of respondents identified deep packet inspection (DPI) as their primary source of observability data, yet 64 percent said DPI is important or very important to their observability processes. At first glance, those numbers seem contradictory. In reality, they reveal an important point about modern observability. Most organizations rely heavily on metrics, events, logs, and traces (MELT) to understand system behavior. These data sources are valuable, but they often show symptoms rather than direct evidence of what is occurring across the network. 

Packet-level visibility provides that evidence. It enables teams to observe application and network behavior in real time, validate performance issues, identify anomalies, and accelerate root-cause analysis.

As AI increases participation in identifying issues and recommending actions, this level of telemetry becomes even more important. AI is only as reliable as the data behind it. When that data lacks context, contains gaps, or fails to reflect operational reality, AI outputs become harder to trust. When AI is grounded in trusted telemetry and comprehensive visibility, organizations can act on recommendations with greater confidence and on automated workflows more effectively. In this way, high-fidelity telemetry serves as the ground truth that makes AI trustworthy.

The Next Steps for AI Operations

The survey results highlight an industry at an inflection point. AI adoption is accelerating, and organizations recognize its potential to improve efficiency, reduce operational burden, and enhance service reliability. But AI alone cannot solve longstanding visibility and data challenges.

The future of AIOps and AgenticOps will depend on the quality of the operational foundation beneath them, including trusted telemetry, real-time visibility, cross-domain context, and governance. Enterprises that focus exclusively on AI models without addressing the quality and completeness of their operational data may struggle to achieve the outcomes they expect.

The organizations that succeed will be those that treat observability as the foundation for AI, not an afterthought. The fact is, before AI can become autonomous, it must first become trustworthy.

Learn more from the insights revealed by IT staff attending Cisco Live 2026.