IDC breaks down generative AI adoption and application in the Asia Pacific

  • By 2023, 70% of organizations had started exploring or investing in generative AI.
  • Regulators grapple with issues of data privacy, security, intellectual property, and potential AI content misuse.

Recently, generative AI technologies like ChatGPT have stirred up significant interest, with organizations across Asia Pacific (APAC) keen on exploring their application and potential for driving business growth. Despite initial concerns on the technology, businesses are eager to implement it, with the hopes that generative AI can improve productivity and enhance the workforce.

How APAC reacts toward generative AI adoption and application

During a recent virtual media briefing, Deepika Giri, the Associate Vice President for Big Data and AI Research at IDC Asia/Pacific Japan, disclosed that approximately 70% of organizations were exploring potential use cases or had begun investing in generative AI technologies as of 2023.

Giri outlined the top ten emerging analytics and AI trends in Asia Pacific that IDC predicts:

  • GenAI’s rapid growth: In 2023, about two-thirds of organizations in Asia/Pacific are either investigating potential applications or investing in generative AI technologies.
  • Multimodal AI: By 2026, 30% of AI models are expected to integrate multiple data modalities, enhancing learning efficacy.
  • Rise of LLM: By 2026, enormous (>1 trillion parameter) foundation models (for Natural Language Processing [NLP], AI-generated images, etc.) will be offered as standard utilities by only the largest vendors.
  • AI and NLP-powered BI: By 2025, widespread use of AI-infused analytics will lead 33% of A2000 to unite data intelligence, decision ops, and data literacy initiatives.
  • Augmented analytics: By 2025, 50% of A2000 (up from 33% in 2022) will incorporate analytics into enterprise or productivity apps to encourage data-driven decision-making.
  • Use of spatio-temporal data: By 2028, the most data-savvy 20% of G2000 will employ spatio-temporal data processing, spurring demand for telematic analytics expertise.
  • Migration of AI workloads to cloud: 56% of AI workloads were on the cloud in 2022, a number predicted to rise above 73% by the end of 2026.
  • BI/AI skills gap: 66% of AP enterprises identify a data science skills gap as a major hindrance to becoming data-driven.
  • BI/AI value maximization: 62% of AP enterprises cite the cost of solutions and implementation as a significant challenge to becoming data-driven.
  • Strong data governance and security: The need to build robust data governance, security, and compliance capabilities for BI.

In light of these trends, Giri identified product design and software development as the two areas likely to see the most significant impact from generative AI in the next 18 months. Moreover, the three most promising use cases for organizations in Asia Pacific are knowledge management, code generation, and marketing applications.

Despite the promising prospects of generative AI, Giri stressed that this technology brings challenges. “Generative AI utilizes machine learning to infer information, which inevitably presents potential accuracy issues,” she explained. “Furthermore, pre-trained large language model applications, such as ChatGPT, are static. For instance, ChatGPT can only pull data from before 2021, as its training concluded that year.”

Additionally, Giri expressed concern about deep fakes, which can be used to generate synthetic media like images, videos, and audio. AI-generated content can be difficult or even impossible to differentiate from authentic media, raising significant ethical questions. An ethical concern of generative AI is the uncertainty over the authorship and copyright of AI-generated content.

Data security and privacy are equally important when dealing with generative AI. “The uploading of personal or proprietary information for training these models can inadvertently expose sensitive details,” Giri cautioned.

The players in the generative AI space

The generative AI landscape is vast, encompassing various stakeholders, each contributing in distinct ways. Among them, AI engineering companies are particularly noticeable for their role in developing and refining models for specific use cases. In parallel, processor and coprocessor manufacturers are engaged in creating host CPUs capable of handling generative AI models.

“Cloud service providers, such as Amazon and Google, facilitate the training, fine-tuning, and customization of these models by offering their infrastructure on rent. This enables the models to transition into the production phase,” explained Giri. Simultaneously, vendors specializing in servers, storage, and networking supply the necessary infrastructure for model training.

IDC: A deep dive into generative AI adoption and application in the Asia Pacific

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A substantial segment of this ecosystem comprises AI application development companies that harness APIs to craft applications based on these models. Large software vendors have also started integrating these models into their products. A noticeable trend is the increasing number of CRM and ERP vendors incorporating generative AI into their offerings.

Venture capital firms also play a key role in this landscape. They provide funding for this groundbreaking technology, expecting substantial investment returns.

APAC point of view – Generative AI regulations

Around the globe, there are escalating concerns about the largely unregulated application of generative AI. Regulatory bodies face the challenge of addressing data privacy, security, intellectual property rights, and the potential misuse of AI-generated content.

At best, Giri mentioned that governments are attempting to comply with existing policies in these areas while formulating new ones. For example,the Indian government has decided against regulating AI in the digital economy, arguing that stringent laws would suppress innovation and research. Meanwhile, the Cyberspace Administration of China has introduced security assessments to evaluate the impact of generative AI services before they are launched in the public domain.

“In spite of the concerns mentioned earlier, we have yet to see any legislation specifically addressing generative AI in the APAC region. Such regulations are often seen as barriers to the spirit of innovation that drives a digital economy,” Giri concluded.