2 out of 3 organizations in Asia/Pacific are exploring or have invested
According to a recent report by IDC, approximately 32 per cent of organizations in the Asia/Pacific region have expressed their commitment to investing in generative AI technologies. Additionally, 38 per cent of respondents are exploring use cases to implement using generative AI. These organizations, which are primarily digital-first enterprises, see generative AI as a means to enhance enterprise intelligence and drive efficiencies across various functions such as marketing, sales, customer care, research and development, design, manufacturing, supply chain, and finance.
Knowledge management is identified as the primary use case for generative AI in the Asia/Pacific region. It is utilized to facilitate access and search across large repositories of diverse information types, including images, documents, voice, and other formats within an enterprise. The second prominent use case is code generation, where application programmers employ generative AI to create, optimize, complete, test, and debug code, leading to improved productivity and code quality. Other key use cases involve marketing automation and customer-facing roles, enabling marketers to generate highly customized marketing content and search engine-optimized content.
Despite the potential of generative AI to transform organizations, there are complexities and risks associated with its implementation. Privacy, security, accuracy, copyright, bias, and misuse concerns remain significant challenges that need to be carefully assessed. The technology is still in its early stages, and vendors are working to address these concerns.
Various vendors are vying to be at the forefront of the generative AI wave, including hyperscalers, cloud service providers offering Model as a Service (MaaS) offerings, AI engineering companies with point solutions, and specialist storage companies providing infrastructure to host these solutions. Additionally, investment firms are interested in backing this technology to achieve substantial returns. The demand for data to train large language models (LLMs) is driving the emergence of companies offering synthetic training data that can be leveraged for training purposes, addressing issues related to sensitive data and bias.
Practical adoption of generative AI can involve procuring ready-to-use solutions for marketing, customer care, and code generation. Numerous vendors offer GenAI capabilities embedded in their offerings across these areas. Alternatively, LLMs can be adopted, trained, or fine-tuned for specific use cases, which can be resource-intensive due to significant computing and energy costs. Techniques such as prompt engineering and prompt tuning have evolved to simplify model training and reduce compute requirements. Balancing these approaches is crucial. Regardless of the chosen approach, there is an inherent cost associated with the underlying infrastructure, as the models are computationally heavy.
The application of generative AI raises global concerns, as the technology is currently largely ungoverned. Regulatory bodies are under pressure to address issues related to data privacy and security, intellectual property rights, and the potential misuse of AI-generated content. Some governments, such as India, view AI as an enabler of the digital economy and have decided against stringent regulations to avoid stifling innovation and research. Others, like Japan, have established councils to promote AI technology. The Cyberspace Administration of China (CAC) has introduced security assessments for generative AI services before their public launch. Despite concerns, there is a lack of uniformity in AI regulation across the Asia/Pacific region, with countries at various stages of developing their approach to AI regulation.
