Vivek Mohindra, Senior Vice President, Corporate Strategy, Dell Technologies
Artificial Intelligence (AI) is no longer a futuristic concept—it’s here now. AI is opening new avenues for productivity, efficiency, creativity, and innovation that seemed impossible, and the pace of change is only accelerating.
For India, AI presents an extraordinary opportunity to reshape its economy, society, and public services. By 2038, AI could add US $675 billion to India’s economy, with the biggest productivity boosts in IT, healthcare, and manufacturing. Already, India is a global leader in AI adoption, with more than 90% of knowledge workers using GenAI at work.
However, while India’s AI rocket ship is primed for launch, realizing its full potential will require clear strategies, strong collaboration, and deliberate effort.
The Challenge of Actualizing AI’s Benefits
Despite rapid advancements, implementing AI at scale remains a considerable challenge for organizations. Gartner estimates that 30% of generative AI (GenAI) projects will be abandoned after the proof-of-concept (POC) phase due to poor data quality, lack of risk controls, and escalating costs. Additionally, many companies struggle to implement POCs in ways that assess their real business value, resulting in limited tangible outcomes.
Building India’s AI Ecosystem
India’s public-private partnership was integral for accelerating digital transformation and will be key to facilitating long-term AI innovation. This is currently being demonstrated through the great progress of the IndiaAI Mission and focus on building a scalable ecosystem, creating indigenous LLMs, and expanding AI education. Continued success will hinge on several critical factors that demand collective effort, investment, and policy frameworks. These include the following components:
1. Localized AI Development
AI must be inclusive, reflecting India’s rich diversity in language, culture, and priorities. Only 0.1% of LLM training data is currently in Hindi, despite it being India’s most widely spoken language. Taking a sovereign AI approach—where models are trained on local infrastructure and data—can create more relevant and impactful solutions.
2. Robust Infrastructure Investment
Access to secure, scalable, and AI-optimized infrastructure across a multi-cloud environment is essential. Developing local infrastructure can address security, privacy, and sustainability concerns while fostering the growth of a stronger AI ecosystem.
3. Nurturing the Open-Source Community
India ranks second globally in open-source contributions and is already driving AI innovations in healthcare and agriculture. Expanding grants and incentives for startups, researchers, and developers can strengthen India’s vibrant open-source community and stimulate growth that benefits both local and global initiatives.
4. Bridging the Skills Gap
Addressing the AI skills gap is imperative. Practical, hands-on training in AI tools and techniques can help build a skilled workforce while high-quality education programs can prepare the next generation of AI practitioners.
5. Ethical and Thoughtful Regulation
Proactive regulations are essential to ensure AI technologies are used ethically and responsibly. Transparent governance frameworks, data privacy laws, and AI accountability standards can foster trust among businesses and consumers alike.
Core Strategies for Success
To accelerate India’s AI adoption and achieve meaningful outcomes, organizations must focus on building a solid strategy tailored to their unique needs. Here are five core beliefs to guide them:
1. Data is the Differentiator
The quality of AI-driven insights depends on the quality of data. With global data expected to reach 394 zettabytes by 2028, businesses must effectively manage, store, and process vast volumes of unstructured data. Invest in tools that clean, categorize, tag, and secure data to unlock actionable insights and stay competitive.
2. Bring AI to the Data
Over half of all data today is generated at the edge, and 83% resides outside public cloud environments. To maximize efficiency, AI models should be placed where the data resides, minimizing environmental impact, enhancing security, and delivering faster insights at the point of need.
3. There is no One-Size-Fits-All Approach
Not every AI project needs massive investment or a custom-built model. Scalable, purpose-built models can run efficiently on edge devices or AI PCs. Test and learn what works best for specific workloads, taking a measured approach to AI deployment that aligns with business objectives.
4. Flexibility Through Open, Modular Architecture
AI success depends on a seamless integration of data, computing power, networks, and software. Adopting an open and modular IT architecture allows for better adaptation, ensuring agility and resilience within an evolving AI landscape.
5. A Broad, Open AI Ecosystem will Drive Innovation and Growth
Open ecosystems give businesses access to a diverse range of tools and applications while avoiding vendor lock-in. Many of today’s GenAI advancements stem from open-source technologies, and their role in sustainable AI development will only continue to grow.
The Path Ahead
India’s AI ambitions are significant, but they are achievable with clear strategies, collaborative partnerships, and a commitment to inclusivity and innovation. By working together, private and public institutions can turn the AI rocket ship into a tool for unprecedented growth and progress – and transform sci-fi fantasies into global realities.
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Disclaimer: Content Produced by ET Edge
