Why Indian Enterprises Are Struggling To Prove AI’s Business Value

As artificial intelligence moves from boardroom ambition to business deployment, Indian enterprises are discovering that adoption is no longer the hard part. Making AI deliver measurable business value is.

New research from analyst firm Ecosystm, commissioned by Snowflake, offers a closer look at how Indian organisations are applying agentic and generative AI and why many still struggle to move from experimentation to enterprise-wide impact.

The study, titled Making AI Work: Strategy, Data, and the Power of Ecosystems, draws insights from more than 700 business and IT leaders across the Asia-Pacific and Japan (APJ) region. Its findings suggest that Indian enterprises are actively deploying AI across customer experience, marketing, operations, and IT, but structural challenges continue to slow returns.

Customer Experience Is Where AI Is Landing First

In India, AI adoption is most visible at the customer interface. According to the research, 69% of organisations are evaluating AI use cases focused on interacting with customers across channels. Marketing content generation follows closely at 65%, while 58% are working to improve chatbot responses.

These early deployments reflect a pragmatic approach: customer-facing AI offers faster feedback loops and visible efficiency gains. Enterprises are using AI to reduce response times, personalise communication, and manage rising service volumes without proportionate cost increases.

Yet these wins, while tangible, only scratch the surface of AI’s potential.

ROI Has Become The Central Question

As AI projects mature, leadership conversations are shifting. Short-term productivity gains are no longer enough. According to the research, 77% of Indian organisations say demonstrating clear ROI is their biggest AI challenge.

This marks a turning point. Enterprises are now questioning how isolated pilots translate into long-term outcomes—better decisions, sustained growth, and competitive advantage rather than incremental efficiency.

Regulatory and compliance concerns add another layer of complexity. About 66% of organisations flagged governance and compliance as a key concern, underscoring the pressure to balance speed with responsibility as AI systems influence core operations.

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

Data Readiness Remains A Structural Weakness

The research makes one reality clear: AI ambition often collides with data limitations.

Among Indian respondents, 60% cited data quality as a major barrier, followed by data security at 54% and data accessibility at 50%. These issues are not abstract. Enterprises struggle to unify data across systems, ensure accuracy, and protect sensitive information as AI models consume larger volumes of business data.

As a result, only 23% of Indian organisations surveyed have fully integrated AI into their overall business strategy. For the rest, AI remains fragmented—deployed in pockets, disconnected from broader transformation goals.

Unstructured Data Is The Next Bottleneck

The challenge becomes sharper when unstructured data enters the picture. Documents, images, emails, chat logs, and audio represent a growing share of enterprise data, yet most organisations remain ill-equipped to analyse them.

The research found that only 38% of organisations across surveyed markets have invested in technologies that enable analysis of unstructured data. Without these capabilities, AI systems lack context, limiting accuracy and relevance.

Ecosystem’s findings point to the need for stronger data backbones, centralised metadata management, lineage tracking, and real-time monitoring of model performance, bias, and drift—capabilities that many enterprises are still building.

Why Ecosystems Are Becoming Central To AI Strategy

To bridge these gaps, Indian enterprises are increasingly leaning on partners.

According to the study, 83% of Indian organisations are already engaging or plan to engage technology partners to support AI strategy, data modernisation, and execution. This includes cloud providers, advisory firms, system integrators, and data platform vendors.

“As organisations in India recognise the strategic value of AI, they are actively turning to the partner ecosystem,” said Dhiraj Narang, Director and Head of Partnerships–India, Snowflake. “To leverage AI for business growth, it is essential to have a strong, connected ecosystem to drive ROI from AI investments.”

The shift reflects a broader realisation: AI transformation is less about tools and more about coordination across data, skills, governance, and execution.

Five Practices Emerging Among High-Intent Enterprises

The research outlines five practices that organisations are adopting to address the ROI challenge:

First, leading enterprises focus on quick wins with long-term intent, measuring immediate KPIs alongside enablers like data quality and workforce adoption.

Second, they measure ROI across the full AI lifecycle, not just pilot outcomes, accounting for infrastructure, governance, and ongoing optimisation.

Third, they integrate fragmented tools across the AI lifecycle to reduce silos and improve visibility into business impact.

Fourth, they build strong foundations, investing in scalable data infrastructure and cross-functional teams aligned to business goals.

Finally, they recognise the cost of inaction, viewing AI not just as a cost-saving lever but as a driver of better decisions, compliance, and innovation.

From Experimentation To Enterprise Muscle

The research signals a shift in how Indian enterprises think about AI. The conversation is moving away from curiosity-driven pilots toward discipline, structure, and accountability.

AI adoption in India is no longer constrained by interest or intent. The real test now lies in data readiness, governance, and the ability to connect technology investments to sustained business outcomes.

For enterprises that get this right, AI could move from an experimental capability to a core business muscle. For those that don’t, the risk is not just low ROI but falling behind in an increasingly AI-native market.