The importance of data and digital transformation has accelerated manifolds in the last few months. Covid-19 has brought to the fore the importance of digital technologies including data and artificial intelligence in addressing the healthcare crisis, restarting supply chains, enabling online education and almost every aspect of the economy.
Role of Data and AI in India’s GDP
Digital India has enabled the country to become a rapid consumer of data and digital adoption in India is accelerating. However, India also needs to build on this and become a data innovator. This could play a key role in driving recovery, economic stimulation and socially relevant reforms. The NASSCOM report Ùnlock Value from Data and AI: the India Opportunity’ showcases that an integrated data utilization strategy can add $450- $500 Bn to India’s GDP by 2025. In the immediate term, data and AI can play a strong enabling role across sectors.
Healthcare is an obvious example, but workforce planning and protection, doubling farmer income, water management, financial health and support for MSMEs – all these and many more can be supported with an effective data utilization strategy. At the same time, we need to incentivize R&D and innovation to solve for India, accelerate India’s journey as a global hub for data analytics and AI and catalyse innovative startups. Learning from best practices of countries, a coordinated program is needed across 5 building blocks –
Strategy (Develop a data and AI vision for the country)
Data (Identify data sets required to unlock priority use cases)
Technology Stack (Create platform(s) to securely host data, AI services, models, open-source libraries, applications and testbeds)
Talent (Define AI roles, such as data scientists, data engineers, and translators and establish standards for their training and certification)
Execution (Design a national program for AI and an empowered central apex body to spearhead it)
to capture this opportunity.
- AI in Agriculture:
Precision farming for improved yields
Improved production planning
Crop failure prediction
- AI in Energy:
Energy demand prediction and management
Rationalization of asset utilization
- AI for MSMEs:
Improved cost competitiveness through yield, energy and throughput optimization
Real time marketplaces for input materials to optimize input costs
- AI in Healthcare:
Timely epidemic outbreak prediction
Remote diagnostics and treatment
Optimized health resource allocation
- AI in Financial Services:
Financial risk modelling
AI based credit underwriting
1-2-1 differentiated pricing
- AI in Logistics and Mobility:
Road network optimization using geo-spatial data
Traffic pattern analysis
