The Role of Observability in AI Adoption in India

Businesses in India are adopting AI rapidly. A vast majority (87%) of them have an AI strategy but according to NASSCOM, budgeting and scaling is where challenges arise. Progress also tends to slow down when AI outcomes aren’t aligned with business goals, and quality data isn’t being fed into the systems; without which, AI’s impact on the business can’t be determined.

For example, customer data residing in silos across tools makes it more difficult to automate internal processes, impeding businesses from unlocking new business opportunities and efficiencies.

It’s perhaps why only 27% of businesses say their telemetry data includes context to adequately quantify the business impact of events. The lack of visibility makes it challenging to attribute telemetry data like CPU utilization, concurrent traffic, and incident data for example to exact business outcomes. That’s because every business is different and needs to deploy AI that drives the most value. With the right strategies and tools, businesses can reap rewards.

Business observability, which is the ability to actively align key business initiatives to the performance of systems, applications, and processes, helps to drive better business outcomes. In essence, business observability captures, measures, and manages the live interaction between technology and capital, eliminating silos by aligning monetary costs and revenue impacts to the performance of systems, applications, and processes in real time–including those that utilize AI.

Observability for better decision-making

Observability metrics aren’t just technical indicators, they are proxies for business health. However, businesses need to have clear visibility across their technical estate in order to understand how these metrics can translate into actionable business outcomes. Most organizations generally report achieving only 25–35% coverage of telemetry data. Furthermore, only 24% of businesses capture their telemetry across the full tech stack.

Aiming for 70% coverage of a businesses’ telemetry data or more via the right observability platform  offers a complete picture to make data-driven decisions. By broadening the scope of data collection and offering a comprehensive understanding of the entire business ecosystem, businesses can make informed, data-driven decisions that have real business impact.

AI-driven intelligent observability platforms can pinpoint bottlenecks and inefficiencies that may be impacting revenue generation, customer satisfaction, or operational costs. They can also help businesses prioritize inefficiencies so corrective action can be made fast.

For example, intelligent observability solutions help to identify latency in digital assets, the effects of which can lead to poor user experience, potentially affecting customer satisfaction and retention. By monitoring site traffic patterns, these tools help businesses scale operations effectively, and provide real-time actionable data to prevent outages from occurring.

They also identify error rates and the frequency of them, along with pinpointing service saturation which is vital to knowing how much of the system's capacity is being used to avoid service interruptions.

Proactive monitoring reduces risk

With intelligent observability, businesses can enable proactive, real-time risk assessment and management that detects anomalies and potential issues in real time. Companies are then positioned to proactively mitigate risks before they spiral into bigger problems, preventing costly outages, service disruptions, and customer dissatisfaction.

For example, observability tools can map business-critical processes and quantify the total financial impact of any issue or outage. This helps quantify costs by attributing a financial value for the activity and availability.

As these solutions continuously monitor the entire tech stack of a business, they are well placed to identify opportunities for innovation, allowing businesses to swiftly act on them, which can lead to developing new products, services, or business models that drive growth and differentiation in the market.

Observability is essential for driving business success. By effectively monitoring, analyzing, and acting on observability metrics, companies can enhance their operations, improve customer experience, and achieve their business objectives. With many businesses adding more AI tools into their stack, they need intelligent observability to proactively monitor the complexities of modern software environments effectively. By closely aligning observability metrics with business objectives, software engineers and business leaders can work together to drive success.

By Rohit Ramanand, GVP of Engineering India, New Relic