Kannan Kothandaraman, CEO & Co-Founder at Selector.gettyAs cloud services went mainstream, the early promise of simplicity was lost in translation with companies using multiple cloud services and combining them with their legacy on-prem services. Complexity now moves across a wider footprint, and visibility depends on how well organizations can understand relationships across that environment. Many leaders now have more telemetry than ever, but less clarity when it matters most.That dynamic defines the AI visibility paradox. Organizations produce more logs, metrics, traces and network signals than ever before, yet critical incidents still have teams guessing what’s happening in the moment, where the issue started and how it is spreading. Cloud intelligence relies on data with context, correlation and operational meaning across the full environment. The central challenge is no longer collecting more signals; it is connecting those signals so teams and AI systems can understand how the environment actually behaves.More Data, More Structure For InsightThat challenge begins with how telemetry is being used. IT operations spent years guided by the assumption that collecting enough telemetry would eventually surface the root cause of any issue. That instinct still has value. Richer datasets can produce stronger insights when they are understood as part of an interconnected system rather than as a sequence of isolated events. The opportunity in modern operations comes from giving data enough structure to reveal meaning across domains.A modern application issue rarely stays within a single layer of infrastructure. A cloud incident might be related to a routing event in the transit network, a dependency on an on-premises database or a configuration drift inside a Kubernetes cluster. Each signal contributes to the full picture, and each signal becomes more useful when it can be interpreted alongside the others. In fragmented environments, those signals land in different tools and reach different teams separately. The network team follows one pattern, the cloud team follows another, and the SRE team assembles a timeline under pressure from multiple dashboards. Fragmentation slows understanding at the moment when precision matters most.The Shift From Collection To ConnectionThat operational pressure points to a larger architectural issue. Traditional observability platforms were built around a vertical model. Logs went to one system, metrics to another and traces to a third. That structure fit an era when infrastructure and incidents often stayed within a single domain. Distributed applications now move across clouds, networks, services, dependencies and physical infrastructure in ways that require a broader operating model.A horizontal intelligence model provides that broader view. In this approach, the relationship between signals carries as much value as the signal type itself. Context emerges from understanding how systems connect: how an application service depends on a cloud instance, how that instance depends on a network path, and how that path reflects conditions beyond the provider’s own abstractions. Once those relationships are visible, AI can follow causality through the environment with greater precision. Telemetry becomes more than a record of events and turns into a map of dependencies, conditions and consequences. That shift changes the operational experience in practical terms. Thousands of signals can be assembled into a coherent narrative that supports prioritization, diagnosis and response. Teams spend less time reconstructing events and more time acting on a shared understanding of the incident. The value of telemetry rises because it arrives with structure, sequence and relevance.AI Operational Value: ContextThis need for connection becomes even more important as AI takes on a larger role in operations. The industry’s rush to embed AI into operational workflows has often emphasized interface design and query speed. Those advances matter, though operational intelligence ultimately depends on the quality and connectedness of the underlying data model. AI systems inherit the blind spots, gaps and fragmentation present in the environments they observe.As operations evolve from traditional AIOps toward agentic systems, AI takes on a broader responsibility. It continuously observes the environment, interprets relationships, identifies emerging risk and supports or initiates remediation within clearly defined guardrails. That progression relies on telemetry enriched with topology, service relationships and operational intent. Data gains operational value when it reflects how the environment behaves across time, infrastructure and dependency chains.Human responsibility becomes more strategic within that model. Operators define intent, establish boundaries and supervise autonomous action across high-stakes systems. Their effectiveness rises with the quality of the context available to the system, because context allows automation to act with greater reliability and discipline. In that setting, visibility supports decision-making, and decision-making supports resilience.The Network, Central To VisibilityThe network is often the missing context in AI visibility strategies. It continues to shape the performance and reliability of modern applications because distributed services rely on paths that cross clouds, regions and on-premises environments. Those paths influence latency, availability and user experience throughout the stack, which means network conditions are part of the operational story from the start.Observability strategies gain depth when they include network telemetry such as cross-region latency, provider-side routing behavior and interdependent traffic paths. These signals form part of the connective tissue of the hybrid cloud. Intelligence layers that explain incidents and guide remediation need network context to deliver a complete operational picture. When network telemetry is integrated into the broader system view, the environment becomes easier to interpret as a single operating reality rather than a collection of disconnected technical domains.More Data, More Operational IntelligenceWith enterprises moving closer into multicloud orchestration and AI-driven services, resilience depends on their ability to transform telemetry into operational intelligence. Competitive advantage grows from turning more data into context, faster decisions and more reliable outcomes across distributed systems.Leaders are shaping systems that rely on unified data, causal reasoning, network-aware visibility and human-centric AI. In the next era of cloud operations, data delivers its greatest value when it arrives with the context required for clarity and action. In AI-driven operations, the winners will not be the teams with the most telemetry; they will be the teams whose AI understands the full operational story.​Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?