Ecommerce companies in India investing in AI risk failure if they do not prioritize clean, structured, and centralized data systems, according to a report by global tech consultancy Nisum. The quality and organisation of data are pivotal for AI projects to move beyond pilots and deliver real business value.
- Data readiness is crucial for successful AI implementation in Indian ecommerce.
- Fragmented data can cause AI failures and amplify errors in forecasting and personalisation.
- Integrated architectures and ongoing data governance are needed for scalable AI.
What happened
A report by Nisum, a global technology consulting and digital engineering company, found that many Indian ecommerce businesses struggle to harness the full potential of AI due to lack of clean, centralised data infrastructure. AI projects often stall or fail because they are built on fragmented and inaccurate data scattered across multiple systems.
The report highlights that key data components such as product information, inventory counts, and customer details are often siloed, preventing AI systems from generating reliable and actionable insights. Companies frequently only identify these data issues after initiating AI pilots, leading to suboptimal results and stalled progress.
Why it matters
AI’s effectiveness is directly tied to the quality of the data it consumes. As ecommerce applications increasingly rely on AI for personalisation, dynamic pricing, demand forecasting, and inventory management, poor data quality can lead to wrong decisions, such as promoting unavailable products or misjudging customer needs.
The report warns that without a centralised and structured data approach, these AI shortcomings not only limit business impact but also risk amplifying errors across the enterprise. This threatens to widen the gap between AI adopters who realise tangible returns and those stuck in costly pilots with ineffective outcomes.
What to watch next
Ecommerce companies in India should focus on unifying their data sources, establishing clear roles for data ownership, and embedding governance processes to maintain data accuracy and consistency over time. Continuous data management efforts are critical as business conditions evolve.
Stakeholders should also prioritize creating integrated AI architectures where generative AI, predictive analytics, and automation tools operate cohesively rather than in isolation. This approach can accelerate the transition from experimentation to scalable AI-driven commerce, unlocking stronger financial returns.