Organizations across India’s various industry sectors, including BFSI, manufacturing, telecom, and critical infrastructure, are accelerating their digital transformation efforts to enhance innovation and differentiation. However, this change has contributed to the rise in complexity of the computing environment, where observability can help ensure that digital systems remain secure, resilient, and perform as expected.
“Not all observability solutions are the same, even though several vendors are positioning AI-driven observability as the magical solution to complex problems.”
For several years, organizations have been investing in observability solutions to improve network and application performance, availability, business continuity, and enterprise resilience. However, not all observability solutions are the same, even though several vendors are positioning AI-driven observability as the magical solution to complex problems. Vendors can go to the extent of promising autonomous operations, rapid detection, and remediation with near-zero downtime. But in reality, AI can amplify observability, but cannot improve data quality.
AIOps: Separating Myth from Reality
AIOps solutions support IT operations to enhance key tasks and processes. They are not intended to replace IT professionals, but rather to enhance their working capacity. AI needs a strong observability foundation, which includes clean telemetry and metrics to reduce mean time to repair (MTTR). AIOps systems can perform various tasks such as correlate huge volumes of data, deduplicate logs and alerts by leveraging machine learning, and execute automated responses. However, this does not mean they can be left unattended, as they still require IT teams to train the systems and validate the outputs. To benefit fully from AI-driven observability, Indian enterprises must focus on strengthening and establishing sound operational systems rather than pursuing a smarter dashboard.
Actionable Insights or Just Correlated Alerts
Many vendors claim their platform delivers actionable insights, whereas in reality, they are just aggregated findings or correlated alerts and not real recommendations for accurate decision-making. Observability platforms must go beyond generating alerts, detecting anomalies, and forecasting. Real actionable insights are evidence-based observations that are a result of data analysis and offer specific recommendations to make informed decisions while enhancing overall performance. The recommendations have to be timely, clear, relevant, and feasible. The insights should radically reduce decision latency for IT professionals to a few minutes rather than hours.
Measuring AI-driven Observability requires a combination of metrics for Indian enterprises, including:
MTTR and MTTK
Organizations’ complex, multivendor environments, including remote sites, translate to increased time taken for incident resolution. Here, it is important to understand how MTTR is calculated across its lifecycle of identification, knowledge, fix, and verify. Reducing mean time to identify (MTTI) the problem can be done by proactive synthetic testing to evaluate user experience from the remote sites. In doing so, any disruption can be identified when there is a deviation, and the notification is sent to the IT team. Yet this is often the easiest step. To actually accelerate problem resolution, it is necessary to discover the root cause of why or where a disruption or outage has occurred. Understanding why faster reduces the mean time to knowledge (MTTK).
User Experience (UX)
AIOps’ system performance must meet user requirements to operate efficiently and deliver a seamless user experience. This provides an understanding of how the systems are managing resource demands and processing speeds, while ensuring scalability aspects are met. Latency is measured by the time taken for the system to process the request and generate a response, and high latency negatively impacts user experience. Tracking memory, CPU, and other resources helps measure their utilization. User retention rates, active usage, and time saved reveal much about user behavior and experience.
Customer Experience (CX)
A shift from just focusing on technical metrics to measuring business impact visibility helps calculate customer experience in AI-driven observability. Indian enterprises need to correlate performance data with transaction success rates and digital experience indicators. The speed at which the pages are getting loaded for end-users, any failure in transactions, customer churn signals, or the occurrence of disruptions across channels should be monitored.
A Real-World Adoption Roadmap for Indian Enterprises
Indian enterprises can extract real value from AI-driven observability by adopting unified platforms to eliminate tool sprawl, using AIOps for automated root cause analysis, and transitioning from reactive monitoring to proactive, predictive IT operations.
- Implementation of ‘Smart Data’ – An AI model should receive clear and relevant data for effective performance. Providing smart data gives AI agents the accurate information essential for accurate root cause analysis. It also enhances customer experience and reduces churn.
- Enable Closed-Loop AI Automation – Smart data provides the intelligence to facilitate closed-loop automation. This process leverages smart data as a feedback signal to detect anomalies, trigger remediation workflows, validate outcomes, and continuously optimize network behavior.
- Eliminate Data Silos – Data silos drain businesses financially by preventing collaboration, weakening AI efforts, and delaying decision-making. These silos exist due to legacy systems, and the adoption of several new tools rapidly without proper integration. Organizations should unify their data systems and confirm that all teams across business units and processes are aligned with the organization’s vision.
- Ensure AI is leveraged for correlation – AI should not be used to replace the critical judgment of humans, but for correlation across variables and to reduce noise. Automation, along with governance, should be implemented to reduce new risks.
- Proactive Security and DDoS Mitigation – It is crucial to integrate proactive security and DDoS mitigation into AIOps implementation. This guarantees predictive defense rather than reactive troubleshooting.
AI-driven observability in India will succeed not through hype, but through strong data, mature operations, and intelligent human expertise. As digital ecosystems continue to grow more complex, enterprises must move beyond siloed tools to unified, intelligence-led operations. By prioritizing smart data, aligning teams, and using AI strategically to augment, but not replace decision-making, organizations can move observability from reactive to strategic, boosting resilience, performance, and delivering the seamless digital experiences our fast-evolving economy demands.
Guest author Gaurav Mohan, VP Sales, SAARC & Middle East, NETSCOUT, a technology company specializing in network and cybersecurity solutions, including observability, threat protection, and performance management for complex networks. Any opinions expressed in this article are strictly those of the author.
