AI Roadblocks and Opportunities Before India’s $1T Digital Leap: Exclusive Interview with IBM’s Dr. Amith Singhee

BharatGen and IBM’s collaboration represents a pivotal move toward scaling the impact of AI built for India’s linguistic and cultural diversity. By combining our research and expertise in data preparation, model governance, and scalable solution architectures with BharatGen’s sovereign multimodal AI and large language models, we aim to scale the application of India-centric AI models to serve the needs of native speakers of Indian languages. 

Together, they address two critical gaps: local relevance and trust. The focus is also on expanding the applications of these AI models across various sectors, including education, agriculture, banking, healthcare, and citizen services. This collaboration can redefine India’s digital future by enabling locally grounded, globally scalable, and transparent AI.

When it comes to scaling AI adoption, the top barriers that enterprises face include data and IT complexity, trust in AI systems, and deploying and governing AI use cases. At IBM, we are helping clients transform into AI-first enterprises by leveraging their trusted enterprise data and integrating AI into the fabric of their existing infrastructures, workflows, and processes. 

We offer innovations across the full AI stack - watsonx, our portfolio of AI products that help clients simplify complexities with data management and model deployment, while enabling seamless governance of AI solutions and integration of generative AI in core enterprise workflows; Granite, our family of open-sourced, high-performing and cost-efficient models trained on trusted enterprise data; IBM Consulting, our AI services to redesign workflows; and our hybrid cloud offerings that enable AI-ready infrastructure to scale AI on any cloud or on-premise.

In India, AI must adapt to meet the country's diverse culture, language, geography, and socio-economic strata. For instance, India’s 1.12 billion mobile connections and UPI-led transactions create datasets unlike any other in the world, and with the proper digital infrastructure, AI can unlock significant benefits.  However, the large language models and associated AI tools currently available are insufficient for addressing India’s unique and complex challenges. 

There is scope and a need to train purpose-built models that address specific objectives while also adapting AI tools and datasets to meet India’s needs. IBM is closely engaged with the Indian ecosystem – comprising government, academia, industry, and non-profit organizations – to adapt and apply our models and tools to meet these needs. This is achieved through open collaboration in the AI Alliance, of which IBM is a founding member, as well as deeper partnerships with Indian innovators, such as BharatGen, collaborations with government initiatives, and partnerships with several other ecosystem partners.

Hybrid skills, such as data engineering, AI engineering, ethical reasoning, and domain-specific expertise, will define the future of work. Our flagship platform, IBM SkillsBuild, offers over 1,000 courses for learners across the spectrum, from high school students and university faculty to mid-career professionals. 

Through our academic research collaborations, we provide students with the opportunity to work in an industry setting, applying their knowledge of technologies such as AI, Hybrid Cloud, and Quantum to real-world use cases. We also support academia in designing a course curriculum that is more aligned with the latest technology trends and makes students industry-ready. By emphasizing both technical proficiency, open innovation, and ethical application, we are building talent for a global AI economy that can develop trustworthy AI systems.

Trust remains the biggest hurdle for Indian financial services adopting advanced AI. Concerns range from bias and explainability to regulatory compliance. Many institutions fear opaque models that cannot justify their decisions. IBM’s approach is to embed responsible AI practices, explainability, and governance frameworks into its offerings. 

By ensuring that data residency and regulatory requirements are met, IBM helps financial firms operate securely within Indian boundaries. Its focus on smaller, domain-specific models further ensures higher accuracy and reduced risk compared to generic LLMs, allowing cost-effective and governed deployments. This emphasis on explainable, responsible, and sovereign AI is helping IBM restore confidence, enabling financial services to move beyond experimentation toward scaled adoption.

IBM’s hybrid cloud–AI strategy combines the flexibility of cloud with the reliability of AI built on responsible frameworks. Hybrid cloud allows enterprises to modernize seamlessly without abandoning trusted systems, while AI enables smarter citizen services, agri-solutions, and business innovations. In addition, quantum computing also offers India the opportunity to take a lead in building algorithms that address complex challenges beyond the scope of classical computers. 

India’s digital economy vision relies on scalable, secure, and inclusive technology. By providing enterprises and governments a trusted path to scale, IBM’s hybrid cloud–AI model can catalyze growth across industries with reduced friction of adoption. This makes it a central driver of India’s ambition to reach a $1 trillion digital economy milestone.