Indian enterprises leveraging AI for growth and innovation: study

Indian enterprises are actively piloting and deploying agentic and generative AI across customer experience, marketing and communications, operations, and IT, according to a study.

A research commissioned by Snowflake and done by Ecosystm titled - Making AI Work: Strategy, Data, and the Power of Ecosystems, noted that with customers as the primary focus, the most evaluated use cases are interacting with customers across channels, reported by 69% of organisations, improving chatbot responses at 58%, and generating marketing content at 65%.

This whitepaper drew on insights from over 700 business and IT leaders across the APJ region.

As Indian businesses transition from isolated pilots to scaled developments and large enterprises re-engineer processes to be AI-native, 77% of the surveyed organisations mentioned that demonstrating clear ROI remains the biggest challenge. Additionally, 66% of organisations are concerned about regulatory and compliance processes.

According to the research, AI adoption often faltered due to the quality of foundational data, accessibility, and security.

Indian respondents cited data quality (60%), data security (54%), and data accessibility (50%) as their roadblocks. Organisations struggle to bring together the right data at the right time, ensure it’s accurate and reliable, and protect it against growing risks.

These challenges highlight the on-the-ground reality, with the research finding that only 23% of Indian companies have fully integrated AI into their business strategy.

“As AI goes mainstream and organisations move from isolated applications to AI-driven co-innovation, it becomes more important than ever to build a trusted, scalable, and reliable data foundation before AI can succeed,” said Vijayant Rai, Managing Director- India at Snowflake.

The research emphasises that fragmented and underprepared data and technology foundations lead to failed AI adoption. For AI adoption to succeed, it requires a flexible, high-performance data backbone; seamless access to data through centralised metadata catalogues and lineage tracking; and continuous, real-time monitoring of model performance, data drift, bias, and output quality.

According to the research, only 38% of organisations among all nations surveyed have invested in technologies that enable them to analyse unstructured data.