As AI projects move beyond experimentation, the imperative has now shifted towards integrated AI adoption that delivers real business value. A new research from analyst firm Ecosystm, “Making AI Work: Strategy, Data, and the Power of Ecosystems,” has found how the APJ region is applying Agentic and Generative AI, the benefits of AI adoption, and crucial roadblocks that are halting organisations from harnessing the benefits of AI.
Commissioned by Snowflake, this Ecosystm whitepaper draws on insights from over 700 business and IT leaders across the APJ region. Ecosystm research finds that, in India, businesses are actively piloting and deploying Agentic and Generative AI across customer experience, marketing & communications, operations, and IT. 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%.
While short-term efficiency wins remain important, leaders are now engaging in more nuanced conversations about what these gains can unlock over the long term. 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.
“Business leaders are shifting towards determining real business value from AI”, said Vijayant Rai, Managing Director- India at Snowflake. “As AI goes mainstream and organizations 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.”
Data Challenges are prominent across India
According to the research, AI adoption often faltered due to the quality of foundational data, accessibility, and security.
India 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.
Investments in technology are required to address the unstructured data challenge
The research emphasizes 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.
“As organizations in India are beginning to recognize the strategic value of AI, they are actively turning to the partner ecosystem 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 ecosystem 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.
However, this is evolving as organisations across the country are shifting towards a more strategic approach to AI. The research found that 83% of Indian organisations are engaging with, or plan to engage with, tech partners to support their strategic, technological, and data needs for AI projects.
Best Practices: Solving for the ROI Challenge
To better demonstrate the ROI of AI projects – and overcome challenges like data accessibility – the research provides five best practices organisations should adopt:
● Immediate Impact, Lasting Value:
AI success hinges on delivering quick wins like faster lead times or better customer satisfaction, while building long-term value through smarter decisions and greater agility. Leading organisations measure short-term KPIs alongside strategic enablers such as data quality, explainability, and workforce adoption to drive immediate impact and resilience for the future.
● Measuring ROI Across the AI Lifecycle:
Pilots prove feasibility but don’t capture full value. True ROI emerges as AI scales and integrates into operations. Organisations must measure across the entire AI lifecycle, from infrastructure upgrades and model maintenance to governance, compliance, and ongoing optimisation, to measure complex costs and sustained benefits, enabling a full picture of AI’s return.
● Integrating Disparate Tools for Clearer Insights:
Fragmented tools across data preparation, model development, deployment, monitoring, and impact tracking create silos and blind spots. Organisations must adopt integrated AI lifecycle platforms that unify technical and business metrics, streamline workflows, and enhance governance frameworks, delivering faster iteration and sharper visibility into AI’s business value.
● Building Strong Foundations:
AI initiatives falter without robust skills, reliable data, and strategic focus. High-performing organisations invest in scalable data infrastructure, build cross-functional teams with technical and domain expertise, and tightly align projects to business goals and KPIs; turning experiments into engines of growth and competitive edge.
● Recognising the Cost of Inaction:
ROI isn’t just about cutting costs; it’s about making smarter decisions, strengthening compliance, empowering teams, and driving continuous innovation. The real risk lies in inaction. Forward-looking organisations are investing in AI today, backed by scalable governance, agile talent, and future-ready business models to stay competitive.