Indian retailers doubled down on their AI investments between 2020 and 2024, making 2026 a tipping point for e-commerce companies. When it comes to customer expectations, 94% of Indians expect the hyper-personalised retail experiences provided by AI to be the bare minimum. This means that AI is now a linchpin anticipating demand – auditing inventory in real-time and averaging a variety of different metrics as a reference point to craft new discounts and create personalised offers.
In an ideal scenario, this data-driven approach should mean that traffic hits all-time highs, while customers experience a smooth purchasing journey. But for many Indian organisations, AI adoption is accelerating, as are deployments of newer advancements such as agentic AI. However, the challenges around poor judgement calls have become a pain point that threatens to derail progress.
The need for thoughtful integration
When deploying AI, adopting a broad strokes approach is a high-stakes gamble during peak sales cycles, with the resulting brand erosion being potentially catastrophic. For example, if AI reads data incorrectly and suggests false discounts during a sale, this can create a risk of financial and reputational damage at the worst possible time. Additionally, irrelevant product recommendations can cause customers to be driven away, which can result in brand loyalty plummeting. With quick commerce booming in India, the impact of AI hallucinations can be particularly devastating to these companies. Imagine AI hallucinating a non-existent or restricted route to meet a 10-minute delivery SLA. This would impact delivery timelines and lead to customer unhappiness.
These scenarios aren’t rare. When AI is deployed across retail functions, the margin for error becomes almost zero. The best antidote here is to have the right safeguards and visibility, with a structured approach ensuring that AI systems operate as intended. This is critical when customer experience and brand credibility are on the line.
Observability becomes the best defence
Retailers today are caught in a high-stakes race where AI is the primary engine of growth. To prevent this engine from failing during critical cycles, observability has become the essential safeguard. With 55% of the industry already embedding active monitoring into their workflows, the message is clear: the path to digital maturity requires granular visibility
AI-strengthened observability offers consolidated visibility into application performance, data quality, and system behaviour. This allows teams to understand not just what AI is doing, but why. By continuously monitoring the full lifecycle of AI requests and responses, businesses can detect anomalies early, validate assumptions, and take timely corrective action before issues surface during order fulfilment.
Observability also plays a critical role in identifying and mitigating risks such as bias, hallucinations, and performance bottlenecks within AI systems. Continuous monitoring allows teams to detect when models begin relying on incomplete, outdated, or skewed data, which can quietly degrade performance long before customers are made aware of problems.
In the context of a peak-season sale, this level of visibility changes outcomes. Instead of AI confidently surfacing products that are no longer in stock, observability can highlight inconsistencies between real-time stock signals and inferred availability. Similarly, when pricing or personalisation logic begins to drift from actual customer behaviour, anomaly detection mechanisms can flag these deviations early.
Observability further supports this by enabling teams to pre-validate AI applications in staging environments and reduce the risk of disruptions once systems move into production. Automated rollouts, combined with real-time monitoring, allow updates to be introduced gradually and safely, even during high-traffic periods such as festive sales.
If unexpected behaviour is detected, teams can pause or roll back changes quickly, limiting both downtime and customer impact. This is especially important since the cost of high-business-impact outages in retail can be as high as $1 million per hour.
Moving ahead with clarity
AI may be the engine of growth, but observability is the strategic guardrail that ensures the car stays on the road. Those who have already invested in these safeguards are reaping the rewards, with performance improvements creating up to 2x ROI. In an experience-driven market, the winners will be those who pair aggressive innovation with the granular oversight required to ensure every touchpoint is flawless. Research reflects this shift, revealing that 50% of retailers have already started integrating granular browser and mobile monitoring to bridge visibility gaps. However, to safely scale these technologies, retailers must also correlate front-end user behaviour directly with AI-based back-end performance. By adopting comprehensive AI observability solutions, such as New Relic, engineering teams can monitor model response times, track prompt quality, and manage operational costs without losing sight of the mobile or browser experience that ultimately drives revenue.
–Authored by Ganesh Narasimhadevara, Director of Solutions Consulting, India