India stands at the cusp of a significant AI breakthrough. Yet experts across the industry believe many companies struggle to translate AI ambition into lasting business value. Globally, nearly 95% of AI and generative AI projects fail to scale beyond pilots, never achieving their intended operational impact.
This trend serves as a wake-up call for Indian enterprises eager to harness AI’s transformative power.
Why do so many AI projects fail?
The staggering failure rate of AI projects—estimated at 95% of generative AI pilots by MIT in 2025—stems from multiple challenges. Chief among them are misaligned expectations, weak data infrastructure, and an underestimation of the resources needed for scale.
Most AI efforts falter because businesses chase flashy applications without addressing foundational needs such as clean, labeled datasets, robust data governance, deployment readiness, and regulatory compliance, according to a recent Constellation Research survey.
Costs also escalate due to ongoing investment in cloud infrastructure, model retraining, and ensuring AI fairness and privacy. A 2025 S&P Global survey found that 42% of companies abandoned most of their AI projects, up sharply from just 17% the previous year. Moreover, 88% of AI pilots never progress to production, underscoring the gap between experimentation and impact.
The in-house dilemma for Indian businesses
India’s vast IT talent pool provides a strong foundation for AI development, but executing enterprise AI solutions at scale exposes significant skill and capability gaps.
Expertise in enterprise AI development, MLOps, and compliance remains limited across many organisations. Building reliable AI also demands substantial investment in data quality and infrastructure—risks that can overwhelm internal teams without external expertise.
Founded in 2016, DataCouch, an Indian AI and data specialist, has emerged as a key enabler helping businesses transition from AI pilots to full-scale deployment.
Partnered with global technology leaders such as Snowflake, Confluent, Alibaba Cloud, and Databricks, DataCouch claims to focus on delivering value-driven, scalable AI solutions that accelerate enterprise transformation.
The company claims to have served over 100 Fortune 500 enterprises, empowering professionals worldwide in cutting-edge Web 3.0 technologies, including AI adoption and upskilling. One notable success story involves a global enterprise where DataCouch implemented an AI chatbot powered by Agentic Retrieval-Augmented Generation (RAG). The solution improved customer engagement, reduced inquiry resolution time by 40%, and lowered staffing needs—demonstrating how DataCouch bridges the gap between prototypes and operational impact.
The four pillars of AI success
AI partners must deliver reliable, repeatable results. DataCouch’s CEO, Bhavuk Chawla, outlines four essential pillars for AI success:
• Scalability: Seamless transition from pilot to enterprise-wide deployment without performance loss.
• Speed-to-Value: Achieving measurable ROI rapidly rather than after prolonged experimentation.
• Vendor Leverage: Access to robust global technology ecosystems ensuring solution stability.
• Capability Transfer: Training internal teams to build self-sustaining AI expertise for continuous improvement.
“In the AI era, leadership depends on trusting partners who don’t just envision possibilities but execute with discipline,” Bhavuk Chawla, CEO, DataCouch, said.
