When even seconds of downtime can cost companies millions, swiftly identifying IT issues is no longer a luxury; it is a necessity. Businesses across sectors are moving beyond traditional monitoring to embrace observability—a smarter, faster way to manage complex digital systems. It's not just about spotting problems but understanding why they happen, often before they impact the user.
From reducing the noise of constant alerts to empowering teams to focus on strategy instead of firefighting, observability quietly transforms how IT teams work. And as India’s enterprises increasingly adopt hybrid and multi-cloud environments, the role of observability in maintaining operational continuity and team efficiency has never been more crucial.
In an exclusive conversation with All Things Talent, Abhijit Banerjee, Managing Director – India & SAARC at SolarWinds, breaks down why observability matters, how AI is making a difference, and what businesses must keep in mind when making the shift.
Observability vs. Traditional Monitoring: What’s the Big Shift?
“Observability is an evolution of monitoring and the next logical step,” Banerjee explains. “While observability isn’t the same for every vendor, a common thread is its ability to make sense of collected data, instead of waiting for a human to dig through data pools.”
Whether through simple rule-based approaches or advanced dynamics involving multiple variables, observability is designed to ease the burden on IT professionals operating in increasingly complex environments. “Using AI is another step up,” he adds.
Why AI-Driven Observability Is a Game Changer for IT Teams
Banerjee notes that AI, particularly AIOps, helps improve the IT team's efficiency.
“A two-pronged approach would be to use AI to lower the amount of data that requires processing, for example, by removing alert noise, and then to use a different AI model to prepare the data so humans can make an educated decision faster, removing the guesswork.”
Why Critical Industries Can’t Afford to Ignore Observability
Observability is essential for each industry, but it's crucial anywhere there is a high level of automation. “In sectors with high levels of automation, any failure can stall business operations,” Banerjee points out. Observability is crucial here because it doesn’t just monitor machines or IT components — it links those systems to business KPIs, helping leaders understand the impact of a failure in real time. “It's not only about detecting issues but ensuring business continuity,” he says.
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How AI Observability Sharpens Real-Time Decisions
Banerjee emphasises that AI isn’t just about automation — it’s about clarity. “AI, particularly AIOps, helps improve the IT team's efficiency. A two-pronged approach would be to use AI to lower the amount of data that requires processing, for example, by removing alert noise, and then to use a different AI model to prepare the data so humans can make an educated decision faster, removing the guesswork,” he explains.
Hybrid Cloud Shift: Impact on Productivity and Collaboration
Any change, including digital transformation, comes with challenges. Migrating workloads to a hyperscaler can be complicated and might require expertise that isn't available. “This means employees should be given a chance to skill up, or new talent needs to be acquired, which isn't always easy. Such a step is not just about technology; it will also impact the IT department from a human point of view,” says Banerjee.
He suggests that enterprises going hybrid in India can learn from the mistakes made by other regions, considering that many workloads return to on-prem because of increasing cloud costs.
Faster Issue Detection and Smarter Workforce Allocation
“Observability, unfortunately, does not work with magic, only with data,” says Banerjee. One typical mistake is to pay too much attention to detailed metrics instead of seeing the big picture.
He illustrates: “For example, nowadays, most IT functions are distributed and run on many moving parts. It's almost natural that something isn't 100% or behaves unexpectedly. Instead of going into crisis mode, observability helps differently: Measuring the user experience. If the average time between two mouse clicks in a business-critical application is still the same, that application, distributed or not, is still acceptable. That's hugely beneficial as it allows the team to focus on more pressing matters.”
Key Challenges in Implementing Observability—and How to Tackle Them
Banerjee is clear: “Unfortunately, Observability shares a destiny with security solutions in many organisations: Budget is available only after something happens. In a perfect world, an Observability solution should be part of a project charter when a new system or change is coming. Any cost in the form of money or time should be factored in right from the beginning.”
Another challenge is the lack of broader buy-in. “Frequently, just one team drives the sourcing and deployment of such a solution, and that's a recipe for disaster. Other teams do have different needs, and a siloed approach should be avoided. It's easy to bypass: Meet all IT teams and discuss available options together.”
Mamta Sharma is a freelance journalist committed to sharing stories on talent management, DEIB, workplace culture, alongside narratives on leadership, entrepreneurship, tech innovation and employee wellbeing.
