Indian firms struggle to turn AI pilots into real ROI gains

Indian organisations are piloting and deploying agentic and generative AI across customer engagement, marketing, operations and IT, with cross-channel customer interactions and marketing content among the most common use cases, according to new research from analyst firm Ecosystm commissioned by Snowflake.

The whitepaper draws on responses from more than 700 business and IT leaders across the Asia Pacific and Japan region. In India, 69% of respondents evaluated AI for customer interactions across channels. Another 65% evaluated AI for generating marketing content, and 58% evaluated AI for improving chatbot responses.

The research also highlights a gap between experimentation and full adoption. Only 23% of Indian companies have fully integrated AI into their business strategy.

Indian organisations now face different pressures as AI programmes move beyond pilots. Seventy-seven percent of surveyed organisations cited demonstrating clear return on investment as the biggest challenge. Another 66% reported concerns about regulatory and compliance processes.

Snowflake noted a shift in executive conversations about AI projects, with short-term efficiency gains remaining a focus while leaders also consider longer-term outcomes from process redesign and scaled deployments.

"Business leaders are shifting towards determining real business value from AI," said Vijayant Rai, Managing Director–India at Snowflake. "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."

"To embed AI deeply within their business strategy, enterprises need structured roadmaps and guidance to translate insights into solutions to derive optimum results," said Rai.

The research identified data readiness as a prominent constraint for Indian organisations, with respondents citing data quality, security and accessibility as recurring roadblocks. In India, 60% cited data quality, 54% cited data security, and 50% cited data accessibility as challenges.

AI adoption can falter when organisations struggle to assemble the right data at the right time. The study also noted issues with accuracy, reliability and data protection as risks increase.

The research flagged unstructured data as another area where technology investment remains limited. Only 38% of organisations across the countries surveyed have invested in technologies that allow analysis of unstructured data.

Fragmented and underprepared data and technology foundations contribute to failed AI adoption. The paper highlights practices for scaled programmes, including centralised metadata catalogues, lineage tracking, and monitoring for model performance, drift, bias and output quality.

Indian organisations are increasingly engaging with technology partners for strategic, technological, and data needs related to AI projects, with 83% either currently engaging or planning to do so. Snowflake links this trend to demand for domain expertise and implementation skills, including advisory firms, system integrators, value-added resellers, and data and application providers.

"As organisations in India are beginning to recognise the strategic value of AI, they are actively turning to the partner ecosystm for domain expertise, platform skills, trusted advice, and proven frameworks to turn their AI ambitions into reality. To leverage AI for business growth, it is essential to have a strong, connected ecosystm including cloud providers, advisory firms, system integrators (SIs), value-added resellers, data and application providers, to drive ROI from their AI investments," said Dhiraj Narang, Director and Head of Partnerships–India at Snowflake.

The whitepaper outlines recommended practices for organisations struggling to demonstrate returns on AI investment. Organisations should measure short-term KPIs alongside broader enablers such as data quality, explainability and workforce adoption. ROI should be assessed across the AI lifecycle, including infrastructure upgrades, model maintenance, governance, compliance and ongoing optimisation.

Fragmented tools across data preparation, model development, deployment and monitoring create blind spots. Organisations should integrate toolchains to connect technical and business metrics and streamline governance. Skills, reliable data and strategic focus are prerequisites for moving beyond pilots.

Some organisations now treat the costs of delaying AI adoption as part of the ROI calculation. Increasingly, AI investment is linked with governance, talent planning and business model changes as organisations move toward scaled deployments.