A new study, The IDC Data and AI Pulse: Asia Pacific 2024, commissioned by SAS, reveals that although Indian organisations are rapidly adopting AI, 15% still in the evaluation and planning stages, and 44% have a short-term or functional focus in their AI deployment. Hence it is not surprising that only 18% of Indian businesses are identified as AI Leaders, highlighting a significant gap between those driving long-term transformational change and the majority that are experimenting without a defined AI strategy.
Of those surveyed, AI Leaders in India indicated their top business outcomes from AI initiatives are focused on expanding market share, improving employee productivity, and saving costs.
Despite the enthusiasm around AI’s potential for driving business growth, Indian companies continue to face implementation challenges – 37% of executives cited the costs associated with AI development and deployment as a hurdle while 27% cited the lack of compelling business cases or difficulty in realising ROI. Additionally, 32% of executives reported struggling with data governance processes, further complicating AI deployment.
Addressing these challenges, the study highlights the importance of boosting infrastructure investments and leveraging data platforms to enhance AI performance. Computational power, continuous data monitoring, and data privacy/security remain essential for ensuring robust and trustworthy AI solutions.
Indian enterprises are also working to implement responsible AI platforms by focusing on explainability, human oversight, and bias mitigation. About 35% of executives indicated that their AI platforms are designed with explainable AI (XAI) techniques, ensuring transparency and accountability. The study also indicates that leveraging data platforms and advanced model management techniques like ModelOps will help Indian enterprises enhance operational efficiency, enabling real-time insights and streamlined decision-making processes.
Commenting on the survey, Noshin Kagalwalla, Vice President & Managing Director, SAS India, said: “Indian companies are undoubtedly making progress in AI adoption, but significant work remains. The challenge lies not only in deploying AI but also in a way that it is trustworthy, scalable, and aligned with long-term business objectives. Strategic investments in data governance and AI infrastructure will be crucial to driving sustainable AI performance across industries in India.”
“The disparity in target outcomes between AI Leaders and AI Followers demonstrates a lack of clear strategy and roadmap. Where AI Followers are focused on short-term, productivity-based results, AI Leaders have moved beyond these to more complex functional and industry use cases,” said Shukri Dabaghi, Senior Vice President, Asia Pacific and EMEA Emerging at SAS.
“As businesses look to capitalise on the transformative potential of AI, it’s important for business leaders to learn from the differences between an AI Leader and an AI Follower. Avoiding a ‘gold rush’ way of thinking ensures long-term transformation is built on trustworthy AI and capabilities in data, processes and skills,” said Dabaghi.
“The IDC Data and AI Pulse: Asia Pacific 2024 study is an important snapshot of how hundreds of large APAC organisations are approaching adoption and implementation of AI, highlighting the leaders and followers across industries,” said Chris Marshall, Vice President, Data, Analytics, AI, Sustainability, and Industry Research at IDC Asia/Pacific. “These insights give us the opportunity to unpack the barriers to successful AI implementation, allowing businesses to make wiser investments into these new and emerging technologies, without being caught-up in the gold rush”.
GenAI is only one part of the AI journey
While a great deal of AI hype has focused on generative AI, the study reveals that Asia Pacific organisations have also been investing in predictive and interpretive AI. In 2023, generative AI accounted for just 19% of AI investment in Asia Pacific, but by 2024 it is expected to increase to 34% reflecting a more balanced spending distribution across these three AI categories.
IDC’s latest spending guide suggests AI spending in Asia Pacific will reach US$45 billion in 2024, rising to US$110 billion by 2028 at 24% CAGR (2023-2028).
The research reveals that organisations are reallocating budgets for the 2024 increase in generative AI investment, with a third saying it will come from redistributing funds away from infrastructure modernisation and 37 percent from application modernisation.
Expectations are high when it comes to ROI
The study reveals this prospective gold rush fuelled by inflated expectations of AI’s potential return on investment. The research found that 40% of Asia Pacific organisations surveyed expect at least a three-fold return on investment, with the “fear of missing out” continuing to spur AI spending. As a result, the research shows AI has at times been adopted without a clear alignment between investments and their outcomes and business value.
With 43% of Asia Pacific organisations planning to increase their AI investment by 20% or more in the next 12 months, organisations risk being disillusioned with AI because of these tactical investments’ likely returns. Instead, business leaders should realise that building an AI capability takes time and requires solid AI foundations to ensure long-term value add.
“While consumer access to generative AI tools made AI feel magical, integrating it into an enterprise environment takes a lot of work, the right infrastructure, and often the high expectations placed on these tools are unrealistic,” said Dabaghi. “Understanding these pitfalls provides us the opportunity to learn how we tackle these issues, enabling a higher success rate, and meeting business objectives when it comes to adopting and successfully implementing AI.”
Pulse of AI across industries
The study provides a detailed analysis of how AI is impacting different industry sectors in the APAC region, with key focus areas including banking, insurance, healthcare, and government sectors.
The skills-gap remains a consistent challenge across industries when it comes to successful AI adoption and implementation. This skill-gap is felt the most within the healthcare industry (41%), followed by the government sector (38%), insurance industry (32%), and less so in banking (29%). Despite this challenge, these industries continue to invest in improving their data and AI capabilities to deliver more streamlined decision-making, greater automation, faster time to market for new products and services, cost savings, and a host of other benefits.
Nonetheless, some use cases are being consistently and successfully deployed – in banking for instance, with its top three use cases: liquidity risk management, asset and liability management and financial crime analytics. In insurance, the research suggests we are seeing AI use cases for insurance claims fraud, omni-channel delivery of products and intelligent pricing. In health care, notable use cases include health care fraud and cost containment, while in government, the popular AI use cases relate to ensuring social benefits programme integrity, supporting emergency response, and tax and revenue compliance.
AI adoption trends vary across countries
The AI landscape in APAC varies by country, with each market showing unique adoption trends. China is leading in AI investments, showing a large increase in AI projects over the next 12 months (59 per cent), with India and Japan following suit (51 per cent; 46 per cent respectively). Furthermore, China and South Korea are advancing more rapidly in AI adoption and integration than the others. This disparity is driven by factors such as investment levels, regulatory frameworks, and the availability of AI talent and infrastructure. The lack of skilled personnel is a national as well as an industry concern in Japan, Australia and South Korea and many parts of Southeast Asia.
The research highlights the opportunities, and the challenges associated with increasing
AI investments across APAC in the coming years. It suggests that to unlock AI’s full potential, companies must develop in-house skills, build a strong portfolio of strategic use cases and plan for AI costs and risks from the start. By doing so, they can achieve some of the promised higher returns and foster greater trust in future AI investments.