According to an IBM analysis, at least 59% of enterprise-scale organizations questioned in India regularly employ AI in their operations.
According to the “IBM Global AI Adoption Index 2023,” early adopters are setting the standard. Among Indian businesses already utilizing AI, 74% have increased their investments in the last 24 months in areas like workforce reskilling and research and development.
The hiring of workers with the necessary skill sets and ethical considerations are two ongoing obstacles that prevent firms from integrating AI technologies into their operations. Thus, the research stated that in 2024, removing these barriers—such as having a strong AI governance framework and equipping people with the necessary skills to work with AI—would be a top priority.
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The fact that Indian businesses are investing more in and embracing AI is a positive sign that they are already reaping the rewards of the technology. Sandip Patel, Managing Director, IBM India and South Asia, stated that despite many firms’ reluctance to go beyond trial and implement AI at scale, there is still a big chance to accelerate.
Currently, 27% of IT experts working for large organizations say they are actively investigating the use of AI, while 59% of them say they have actively implemented the technology. According to the report, around 6 out of 10 IT experts working for large companies say their organization is actively deploying generative AI, while another 34% are just investigating it.
The primary drivers of AI adoption include the development of more accessible AI tools (59%), the desire to automate critical activities and cut costs (48%), and the growing quantity of AI integrated into readily available commercial software (47%).
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According to the report, the top 5 obstacles preventing successful AI adoption at enterprises exploring or deploying AI are insufficient AI skills and expertise (30%), a lack of tools/platforms for developing AI models (28%), complexity or difficulty in integrating and scaling AI projects (27%), ethical concerns (26%), and too much data complexity (25%).
