Nisum Report: Data Readiness Key for AI in E-commerce to Succeed

A Nisum report warns e-commerce firms that AI investments will fail without clean, structured, and centralised data. Data readiness is the biggest factor determining if AI projects deliver real business outcomes beyond just experimentation.

E-commerce companies investing in artificial intelligence (AI) could struggle to get the desired results if they do not first build clean, structured and centralised data systems, according to a report by global tech firm Nisum.

The report said data readiness is one of the biggest factors determining whether AI projects move beyond experimentation and deliver measurable business outcomes.

It noted that AI systems depend heavily on the quality of the information they receive, making data infrastructure a critical foundation for businesses looking to scale AI.

The Dangers of Poor Data

"AI is only as powerful as the data feeding it, and the data problem is almost always bigger than the client thinks," the report said.

The report said companies often discover problems with their data only after they have already started an AI project. Product information, inventory records and customer data can be spread across different systems, making it difficult for AI tools to work with a complete picture of the business.

The report also warned that poor data can do more than limit the performance of AI. If inventory counts are inaccurate or customer records are duplicated across multiple databases, AI systems can reproduce and amplify those problems at a much larger scale.

Impact on E-commerce Operations

"The single most common failure point is data infrastructure," the report said.

For e-commerce businesses, the problem becomes particularly important when AI is used for personalisation, pricing, forecasting or inventory management. A marketing system, for example, may have advanced AI capabilities, but if it is not connected to the inventory system, a business could end up promoting products that are unavailable.

The report said this is one reason why companies need an integrated architecture in which generative AI, predictive analytics and automation systems can work together rather than operating as separate tools.

The Path to Data Readiness

The report also pointed to the importance of centralising data from different parts of a business. It said retailers often have fragmented information across stores, e-commerce platforms and apps, which can prevent them from moving beyond broad customer segmentation towards more personalised experiences.

At the same time, the report said businesses should not treat data preparation as a one-time exercise. AI models need accurate, fresh and consistent information, while changes in products, suppliers and customer demand can affect the quality of predictions over time.

Key Recommendations for Businesses

The report recommended that companies first identify and unify scattered data sources, establish clear ownership of data quality and introduce governance processes before scaling AI initiatives.

The AI Commerce Opportunity and Risks

"Only 5.5 per cent of organisations using AI see real financial returns from their investments. That number should stop every commerce leader in their tracks," said Anurag Chauhan, who has been appointed Chief Executive Officer of Nisum, a global technology consulting and digital engineering company.

"The AI commerce opportunity is real. But the gap between the average result and the top of the market is now enormous, and it is widening."

The report said businesses that address data readiness can move more quickly from AI pilots to production, while those that overlook the foundation risk ending up with disconnected insights, unreliable forecasts and AI projects that never reach full-scale deployment. (ANI)

(Except for the headline, this story has not been edited by Asianet Newsable English staff and is published from a syndicated feed.)