According to research company International Data Corporation, the artificial intelligence (AI) industry in India is anticipated to expand at a CAGR of 20.2 percent over the next five years, reaching US$7.8 billion in total revenues by 2025. (IDC). For the next five years, Indian enterprises will accelerate the use of both AI-centric and AI-non-centric applications, according to IDC.
By the end of 2025, the AI software sector will have dominated the market, growing at a CAGR of 18.1 percent from US$2.8 billion in 2020. Applications accounted for the biggest part of revenue in the software (AI) sector, with a 52% increase in revenue by 2020.
The survey discovered that businesses were using a variety of AI tools, including CRM, ERM, and others, to manage operations, grow supply chains in response to real-time or projected demand, and enhance ROI and cost savings.
According to IDC India Associate Research Director (Cloud and AI) Rishu Sharma, “Indian organisations plan to invest in AI to address current business scenarios across functions, such as customer service, human resources (HR), IT automation, security, recommendations, and many more.”
“Increasing business resilience and enhancing customer retention are among the top business objectives for using AI by Indian enterprises,” he adds.
Artificial Intelligence has already made its mark as a transformational technology in the digital age. This is unsurprising, considering that AI has the potential to bring about radical—and maybe unprecedented—changes in people’s lives and work. Although the AI revolution is far from over, the majority of its economic impact has yet to be realized.
The India AI market report gives an overview of the various data types that businesses are processing for AI-ML solutions. The report also shows the current state of AI projects in organizations and addresses the main concerns about AI-ML deployment methods. Indian organizations cited the cloud to be their preferred deployment location for their AI/ML solutions.
The study also discovered that disruptions in current business processes and a lack of follow-up from business units were two of the most common causes for AI initiatives failing.
Swapnil Shende, an AI Senior Market Analyst, stated With data being one of the most crucial components in an AI/ML project, businesses use a variety of databases to handle large data volumes for making real-time business decisions. Swapnil emphasized that organizations must concentrate on obtaining high-quality training data for AI and machine learning models.