Bangalore December 24 2017: Indian organizations are displaying an increasing appetite towards the adoption of Artificial Intelligence (“AI”), which in turn is expected to spike organization spends on this technology, over the next 18 months, finds an IDC study commissioned by Intel. Nearly 70% organizations in India are expected to deploy AI solutions before 2020, says the study.
Nearly 75% of the firms surveyed, anticipate benefits in business process efficiency and employee productivity with the use of AI, and 64% of the respondents believe that this technology can empower them in revenue augmentation through better targeting of offers and improved sales processes. While these present some great incentives for AI in India, the other side of the coin shows that 76% of the companies are or believe that they will face a shortage of skilled personnel to harness the power of AI.
Indian enterprises have been quick to adopt AI in the recent past, with nearly 1 in 5 organizations (22.2%) across the four verticals surveyed implementing the technology in some way. This number is anticipated to soar considerably by mid-2019 with nearly 7 in 10 firms (68.6%) anticipated to deploy AI, indicating that the technology will reach mainstream adoption. The Intel India commissioned report highlights the cross industry functions that will benefit from AI over the next 18 months:
Prakash Mallya, Managing Director, Sales & Marketing Group, Intel India said, “We all talk about the opportunity that India presents for AI, but often the types of industries that will embrace AI, the challenges that AI can address, and the roadblocks in implementation, are vague. This research is a small step towards comprehending this knowledge, and enabling companies such as ours, shape strategy and move ahead in the right direction.”
However, concerns around adoption of AI continue with high cost of solutions, acute shortage of skilled professionals, unclear return on investment, and cybersecurity emerging as the key hindrances. Regulatory compliance and lack of quality data are seen as other important challenges.