Authored by Ravi Iyer, Senior Director, Software Engineering, New Relic
Companies are competing to swiftly adopt AI, and keep pace with the rapidly evolving capabilities it offers. India is among the fastest-growing AI economies globally, and its domestic market is projected to more than triple by 2027. As the current star of the tech world, AI is pushing businesses to jump on the bandwagon. But to avoid falling into the trap of AI FOMO, it’s important to dive deeper into the reasons for adoption. The rush to integrate AI at breakneck speed has often led to soaring costs and minimal returns.
Organisations need to take a step back and think about their AI investments holistically. The best way forward isn’t to succumb to the pressure of AI FOMO, but to chart a well-thought-out strategy that enables them to maximise their AI investments — sustainably, intelligently, and with purpose.
FOMO blinds the practicalities
Companies that adopt AI without a clear strategy can fall into the trap of strategic overreach. Overly ambitious rollouts of all-encompassing solutions can miss the real pain points that need solving. These unrealistic pursuits strain budgets, stretch resources, and often lead to costly financial and operational setbacks.
Hasty decisions can also cause organisations to overlook the nuanced needs of different employee functions. As a result, employees eager to boost their productivity may turn to alternate tools they find most useful for their roles. This shadow AI usage introduces significant security risks, as it can expose sensitive or proprietary data to public AI platforms, jeopardising both information security and brand reputation.
Another common oversight among companies rushing into AI adoption is data quality. Many organisations still struggle with unstructured, fragmented, and siloed data environments. Since AI output is only as good as the data that feeds it, messy, incomplete, or biased data inevitably produces flawed and unreliable results. Unrefined and ungoverned data can also create compliance and data protection risks.
Most importantly, AI systems need constant monitoring. As AI models are dynamic and data-driven, performance can fluctuate due to changes in data quality, prompts, or infrastructure. Continuous monitoring through AI observability ensures that metrics like response time, token usage, accuracy, latency, and APM signals are tracked in real time. This helps teams detect drifts in model behaviour, measure efficiency, and identify issues before they affect end users.
Taking a phased approach with baby steps
When it comes to AI adoption, the approach must be strategic and phased. Businesses should start by identifying clear use cases and pinpointing the problems that need solving first. This problem-centric approach drives faster value creation and helps stakeholders see the rationale behind adoption and the resulting operational shifts.
And most importantly, before diving into large-scale adoption, businesses must start small and monitor the performance with an intelligent observability platform. When intelligent observability anchors AI adoption, the end result is powerful. Organisations can analyse how prompts and responses perform in terms of speed, accuracy, and cost impact.
Realising the value of this level of oversight, India leads globally in observability spending, with New Relic research finding that 79% of organisations invest at least $1 million annually. Establishing comprehensive AI monitoring will also help manage risks and ensure stable and sustainable systems. In India, companies that prioritised intelligent observability have garnered tangible benefits: 42% of respondents reported a 2–3x ROI, and half (51%) saw a 2–5x ROI, reiterating the importance of a well-structured AI adoption strategy.
Choosing the best path forward
AI is already transforming how businesses operate, compete, and grow. But adopting it out of FOMO is like setting sail without a compass. It can be exciting at first, but directionless in the long run. The real win comes from knowing where AI fits, where it doesn’t, and how it can make people better at what they already do. The companies that treat AI as a continuous game, not a short-term fix, will be the ones still standing when the hype fades.
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