The Asia/Pacific region (including China & Japan) is experiencing a remarkable acceleration in adopting AI and Generative AI technologies, including software, services, and hardware designed for AI-driven systems. According to I nternational D ata C orporation 's latest Worldwide AI and Generative AI Spending Guide, AI and Generative AI (GenAI) investments in the region are projected to reach $110 billion by 2028, growing at a compound annual growth rate (CAGR) of 24.0% from 2023 to 2028. This surge highlights the region’s critical influence in shaping the future of AI innovation and driving technological progress on a global scale.
“As organizations transition from experimenting with generative AI to more large-scale enterprise implementations, there is an increased emphasis on comprehensive AI governance. This approach not only considers technological frameworks like AI platforms and models but also addresses regulatory, organizational, and cultural dimensions to protect internal and external stakeholders' interests. AI governance is therefore much larger than data governance and must be an integral part of an enterprise’s AI strategy.” Deepika Giri, Head of Research, Big Data & AI, IDC APJ.
With a market share of 23.8 percent in 2024, the software and information services industry is one of the leading industries in AI adoption. A significant portion of AI investment in professional services is allocated to AI infrastructure provisioning, where service providers manage the IT infrastructure for AI systems, and end users access the necessary resources for computing and storage for AI system development or provision of AI services.
The banking sector, as an early adopter of AI, continues to accelerate its adoption, with investments directed towards boosting operational efficiency, enhancing customer experience, and strengthening security. Prominent use cases include AI-driven fraud detection, personalized recommendations, and automated customer service. Banks are utilizing AI to gain deeper customer insights, enhance propensity modeling for growth, and make more data-driven credit decisions, all contributing to cost and time savings. Meanwhile, telecom companies are also rapidly embracing AI and GenAI, employing it to optimize network operations, improve customer service through virtual assistants, and enhance predictive maintenance. AI plays a key role in managing vast amounts of user-generated data, resulting in improved service quality and the creation of new revenue opportunities.
"The adoption of AI and Generative AI in the APJ region is driving a notable shift in business strategies and value creation. Companies are leveraging AI to boost efficiency and enrich customer experiences, opening doors to fresh growth opportunities," says Vinayaka Venkatesh, Senior Market Analyst, Data & Analytics, IDC Asia/Pacific. "This strategic focus on AI is leading the region into a new era of digital success, where AI-powered insights and solutions are transforming the future of business," Venkatesh ends.
AI adoption is rapidly transforming industries across Asia/Pacific, with top use cases demonstrating the technology's wide-reaching impact. Key areas of fast-growing AI implementation, aside from AI infrastructure provisioning, include customer service. This is emerging as a crucial business function where organizations harness AI technologies to improve customer engagement, streamline service processes, and deliver personalized experiences at scale. The adoption of AI and GenAI in customer service is transforming how businesses engage with customers, enabling more human-like and empathetic interactions.
Augmented fraud analysis and investigation are pivotal areas in which AI is making a substantial impact. By leveraging machine learning algorithms, financial institutions can analyze vast amounts of transaction data in real time to detect patterns indicative of fraudulent activity. This proactive approach not only enables immediate fraud detection but also helps prevent future incidents by continuously adapting to new fraudulent techniques.
In government operations, AI has the potential to revolutionize various sectors by enhancing efficiency and decision-making. For example, AI can improve threat intelligence and prevention through real-time analysis and predictive insights, thereby reducing risks. In defense, AI supports surveillance and strategic operations, while in public safety, it optimizes emergency response times and resource allocation. Additionally, AI-driven investigation and intelligence systems bolster counter-terrorism efforts, fostering a proactive stance on national security. Overall, AI adoption in these areas streamlines processes and enhances accuracy and responsiveness in critical government functions.
Taxonomy Note: The AI-enabled applications market includes process and industry applications that automatically learn, discover, and make recommendations or predictions. These applications use natural language processing (NLP), search, and machine learning (ML) to provide expert assistance in a wide range of areas. To be considered an AI-enabled application, the AI must meet the following conditions: the AI technology must be central and critical to the function of the application; the AI technology must include some sort of machine learning, and some sort of user/data interaction or knowledge representation capability; and the AI application may sometimes only be bought in conjunction with another business application (i.e., ERP, CRM, SCM, and HCM). Generative AI is a subsegment of AI that involves unsupervised and semi-supervised algorithms that enable computers to create new content using previously created content, such as text, audio, video, images, and code in response to short prompts.
The IDC Worldwide AI and Generative AI Spending Guide (V2 2024) examines the artificial intelligence (AI) and generative AI (GenAI) systems opportunity from the use case, technology, industry, and geography perspectives. The Spending Guide quantifies the AI opportunity by providing data for 42 use cases across 27 industries in nine regions and 32 countries. Data is available for two AI types (GenAI and rest of AI), three technology groups with nine technology categories comprising 17 technologies, and two deployment types (public cloud services and on-premises/other).
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