Product Case Study

Intelligent Observability at Scale

Real-time, scalable observability

Short Description

A global managed service provider (MSP) is using ScoutITAi A&I Solutions’ generative AI platform to sharpen internal IT operations and deliver smarter service to a large client base. By layering ScoutITAi across Broadcom NetOps and AppNeta, the company gains rich, contextual insights at scale. The payoff: more intelligent monitoring, smoother operations, and a solid foundation to bring AI-powered observability to every customer.

Problem Statement

As a Broadcom NetOps customer supporting over 100 clients, the company needed to go beyond basic fault detection and achieve true situational awareness across its entire environment. NetOps offered deep visibility into device-level events, but it didn’t always connect the dots. It lacked the broader context to surface patterns, recommend improvements, or flag risks early. The company sought a smarter layer one that could synthesize vast volumes of telemetry across monitoring domains and inform decisions with insight, not just information.

Proposed Solution and Architecture

The company partnered with A&I Solutions to embed ScoutITAi as an intelligent AI layer across its Broadcom observability stack. With a five-year commitment to the platform, the company is using ScoutITAi in two key ways:

  • Internally: to improve visibility, speed up decisions, and boost operational efficiency across the service infrastructure.
  • Externally: with plans to offer ScoutITAi as a value-added service, extending AI observability to hundreds of managed environments.

ScoutITAi integrates seamlessly with the company’s Broadcom tools. Key components include:

  • DX NetOps delivers deep fault and performance visibility across client networks.
  • AppNeta: expands monitoring into hybrid WAN environments.
  • ScoutITAi unifies and interprets telemetry through generative AI, providing natural-language insights, surfacing patterns, and enabling contextual decision-making across diverse client infrastructures.

Outcomes and Success Highlights

Although adoption is still early, the company has already seen meaningful wins with ScoutITAi’s AI-native model.

  • The CTO tested ScoutITAi’s Agentic architecture by asking real operational questions and was impressed by the quality and depth of the insights.
  • Those results accelerated the rollout of ScoutITAi and AppNeta to more sites and led to additional monitoring points.
  • Cross-domain analysis and clear context are setting the stage for smarter service delivery and proactive issue resolution at scale.

As the company continues to deploy ScoutITAi, it’s building an AI-driven MSP operating model, helping teams think and act faster with unified insight across customers, tools, and technologies.

TCO Considerations

A formal TCO analysis is forthcoming, but the company already expects significant operational value from:

  • Consolidated analysis across all monitored domains, reducing tool fragmentation and manual correlation.
  • Enhanced decision support that speeds incident detection and enables proactive management.
  • Scalable insight delivery that strengthens both internal operations and client-facing services.

Lessons Learned

  • Early experimentation with ScoutITAi’s Agentic model delivered clear value and helped fast-track the rollout.
  • Global context and AI-driven analysis are key differentiators compared to traditional monitoring tools.
  • The company is well-positioned to extend ScoutITAi to its customers, creating a new value stream that blends service delivery with intelligent observability.

With ScoutITAi, the company isn’t just monitoring networks; it’s reimagining how AI can elevate MSP performance, insight, and scale.


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