AI-Driven NetOps: How Enterprises are Embracing Intelligent Network Management Solutions

Q: What are the primary business benefits of AI-driven network management?

According to the EMA study, the top four benefits are: Faster resolution of network problems (54.1%), improved network performance and user experience (51.3%), reduced security risk (48.7%), and cost optimization (47.8%).

Q: How do IT professionals prefer to interact with AI tools?

The most popular model is an AI-enabled collaborative workspace (33.6%), followed by AI insights embedded directly into existing workflows and dashboards (29.5%).

Q: Is the industry ready for closed-loop (autonomous) operations?

While 98% of IT pros desire AI-automated actions, the approach is cautious: 50% want to apply it selectively to low-risk operations, and only 31% completely trust current AI-driven insights.

EMA research reveals that while 93% of organizations have allocated budgets for AI-driven NetOps, only 35% report complete success due to significant barriers like security risks and data quality issues . Enterprises are primarily leveraging vendor-provided AI features to achieve faster problem resolution and improved performance, yet 47% of users frequently encounter false or mistaken insights that necessitate human oversight. To reach operational maturity, IT leaders must invest in high-quality data lakes and rigorous evaluation techniques that utilize continuous accuracy monitoring and production feedback loops.

As enterprise environments grow in complexity, IT organizations are shifting from traditional ML-driven AIOps toward agentic operations to optimize and automate their network infrastructure. This new research from EMA, based on a survey of 458 IT professionals across North America and Europe, reveals that while engagement with AI is high, "complete success" remains elusive for the majority.

Currently, only 35% of enterprises report being completely successful in applying AI to network management, primarily due to persistent concerns regarding data quality, security risks, and technical complexity. This study provides a comprehensive look at how modern teams are balancing the promise of faster task execution and error reduction against the reality of frequent AI-generated mistakes.

The EMA research makes it clear that AI is not a "magic" solution that works out of the box; it requires a foundation of high-quality data and a culture of continuous improvement. To bridge the gap between partial and complete success, IT leaders must prioritize data quality, as only 44% of professionals currently feel very confident that their network data can support AI-driven initiatives.

Success correlates directly with the implementation of data lakes and the ability to effectively evaluate AI solutions through continuous accuracy monitoring and feedback loops in production. Ultimately, building trust through transparency and clear data source attribution will be the deciding factor for organizations aiming to move toward extensive closed-loop network operations.

Frequently Asked Questions

What are the primary business benefits of AI-driven network management?

According to the EMA study, the top four benefits realized by IT organizations are:

Faster resolution of network problems (54.1%)
Improved network performance and user experience (51.3%)
Reduced security risk (48.7%)
Cost optimization (47.8%)

Why is network data quality considered the "foundation" of AI value?

Data efficacy determines the accuracy of AI insights. Only 44% of organizations are very confident in their data quality. Key issues undermining AI success include collection errors (such as packet drops), storage corruption, and inconsistent data formatting across different vendors.

How do IT professionals prefer to interact with AI tools?

The most popular interaction model is an AI-enabled collaborative workspace (33.6%), followed by AI insights embedded directly into existing workflows and dashboards (29.5%) . Chatbots and virtual assistants are popular but ranked lower than integrated, team-based environments.

What are the biggest barriers to achieving value with AI in NetOps?

The report identifies security and compliance risk as the leading business challenge (46.1%), while data issues (31.7%) and network complexity (29.5%) stand as the primary technical hurdles. Budget limitations also play a significant role in preventing organizations from reaching their AI goals.

Is the industry ready for closed-loop (autonomous) operations?

Interest is nearly universal, with 98% of IT professionals expressing a desire for AI to take automated actions. However, the approach is cautious: 50% want to apply it selectively to low-risk operations only, while only 47.6% are ready to implement it extensively. Trust remains a major factor, as only 31% of respondents completely trust current AI-driven insights.