The Indian economy is going through fast-paced reforms and growth in digital infrastructure. The transformative power of Artificial Intelligence and Generative Artificial Intelligence (GenAI) is emerging as a game changer in the country's tech-driven economy. Organizations are increasingly embracing AI technologies to improve efficiency and decision-making and drive digital transformation. According to an EY Report, GenAI has the potential to add a cumulative $1.2 -1.5 trillion to India’s GDP over the next seven years.

This rapid pace of AI adoption across sectors reflects India's commitment to harnessing innovative new technologies that address complex challenges and unlock possibilities. However, amidst the enthusiasm surrounding AI adoption, it is crucial to recognize and address the difficulties that organizations encounter on this transformative path.

Challenges of Modern AI in India 

Data Quality and Accessibility:

Many organizations face issues with the quality and accessibility of data. These issues may complicate AI adoption by hindering AI model training and accuracy due to a lack of standardization and inherent inconsistencies . 

Vector databases can boost accuracy by providing contextualization, which in turn helps enterprises improve decision making and enhance user interactions. Accretion, or building up a set of vectors, is crucial for developing a comprehensive context encompassing various types of interactions, customers, or situations. This assembled context enhances the AI's predictive and responsive capabilities.

Data Cost:

Adopting AI means expanding IT infrastructure and data architecture, resulting in increased budgets.

About one-third of cloud budgets are misspent, often due to overprovisioning and lack of proper cost management. Despite the potential value of cloud adoption, companies often overspend, with up to a 20% increase in cloud budgets annually, as cited in an EY Report. In addition, it states that 80% of organizations only save over 10% of costs through hybrid approaches, and 85% use automatic scaling to control costs. 

Implementation of real-time data analytics can help organizations more effectively manage budget allocation and costs. By saving database operations and developer time, efficiently using compute infrastructure, and optimizing database management, real-time databases can help companies meet their larger goals while also reducing their cloud costs.

Infrastructure and Scalability:

Data comes in many forms from different sources and various volumes. Building scalable infrastructure to handle large volumes of data is essential for AI applications. Organizations constantly struggle to handle and process the vast influx of data from a diverse range of sources, which is crucial for powering AI models and generating timely, accurate outputs. Companies need solutions that can serve up data quickly at scale. Without scalability, AI systems can become bottlenecks rather than enablers. 

Investing in technologies like graph and vector databases support reliability, performance, and scalability in AI systems. These databases are particularly useful in handling complex relationships and large-scale data, enhancing AI capabilities in sectors like finance and logistics.

Skilled Workforce and Talent Shortage:

Another challenge to AI adoption is the shortage of skilled professionals in data science and AI. According to a report by NASSCOM, India faces a demand-supply gap of 140,000 in AI and Big Data analytics roles.

Investing in talent development and fostering partnerships with academia can address the talent gap and build a skilled workforce. For example, initiatives like the collaboration between IITs and industry leaders aim to upskill professionals in AI and related fields. Building strong collaborations with academic institutions can facilitate research and development, providing a steady pipeline of skilled professionals.

Conclusion 

Overcoming obstacles to AI adoption is crucial to unleashing the transformative potential of AI in India. Collaborative efforts between government, industry, academia, and technology providers are necessary to address these challenges and drive AI innovation across sectors.

As India continues to integrate AI into its digital infrastructure, addressing these challenges will ensure that AI not only enhances business efficiency and decision-making but also contributes to sustainable economic growth and societal benefits.

EY and Nasscom report