At lease 59 per cent of enterprise-scale organisations surveyed in India actively use AI in their businesses, according to a report released by IBM.

The ‘IBM Global AI Adoption Index 2023’ has found that early adopters are leading the way, with 74 per cent of Indian enterprises that are already working with AI having accelerated their investments in the past 24 months, in areas like R&D and workforce reskilling.

Ongoing challenges for AI adoption remain, including hiring employees with the right skillsets and ethical concerns, inhibiting businesses from adopting AI technologies into their operations. Therefore, in 2024 addressing these inhibitors would be a priority, such as providing people with the relevant skills to work with AI and having a robust AI governance framework, the report noted.

“The increase in AI adoption and investments by Indian enterprises is a good indicator that they are already experiencing the benefits from AI. However, there is still a significant opportunity to accelerate as many businesses are hesitant to move beyond experimentation and deploy AI at scale,” said Sandip Patel, Managing Director, IBM India and South Asia.

Today, 59 per cent of IT professionals at large organisations report that they have actively deployed AI while an additional 27 per cent are actively exploring using the technology. Similarly, around 6 in 10 of IT professionals at enterprises report that their company is actively implementing generative AI and another 34 per cent are exploring it, said the report.

Advances in AI tools that make them more accessible (59 per cent), the need to reduce costs and automate key processes (48 per cent), and the increasing amount of AI embedded into standard off the shelf business applications (47 per cent) are the top factors driving AI adoption.

The top 5 barriers hindering successful AI adoption at enterprises both exploring or deploying AI are limited AI skills and expertise (30 per cent), lack of tools/platforms for developing AI models (28 per cent), AI projects are too complex or difficult to integrate and scale (27 per cent), ethical concerns (26 per cent) and too much data complexity (25 per cent), the report noted.