TL;DR:
- 70% of organizations in APAC are exploring or investing in generative AI technologies.
- The top ten emerging analytics and AI trends in APAC include GenAI’s rapid growth, multimodal AI, and the rise of large language models (LLM).
- Product design and software development are expected to be heavily impacted by generative AI in the next 18 months.
- Knowledge management, code generation, and marketing applications are the most promising use cases in APAC.
- Challenges include accuracy issues, the static nature of pre-trained models, deep fakes, and concerns about data security and privacy.
- The generative AI landscape involves various stakeholders, including AI engineering companies, cloud service providers, and AI application development firms.
- Governments in APAC are grappling with the need for regulations balancing innovation and responsible use of generative AI.
Main AI News:
Generative AI technologies, exemplified by ChatGPT, have ignited a wave of interest in Asia Pacific (APAC) as organizations strive to tap into their transformative power and propel business growth. Initial apprehensions about the technology have given way to a strong desire to implement it, driven by the belief that generative AI can revolutionize productivity and empower the workforce.
In a recent virtual media briefing, Deepika Giri, the Associate Vice President for Big Data and AI Research at IDC Asia/Pacific Japan, divulged that as of 2023, approximately 70% of organizations in APAC were either actively exploring potential use cases or investing in generative AI technologies. This remarkable surge in interest signifies a shift in mindset, as businesses across the region recognize the value and competitive advantage that generative AI can deliver.
According to Giri, IDC predicts ten emerging analytics and AI trends that will shape the APAC region:
- GenAI’s Exponential Growth: In 2023, around two-thirds of organizations in Asia/Pacific will be engaged in investigating potential applications or investing in generative AI technologies.
- Multimodal AI: By 2026, 30% of AI models are expected to integrate multiple data modalities, thereby enhancing learning efficacy.
- The emergence of Large Language Models (LLM): By 2026, vendors will offer enormous (>1 trillion parameters) foundation models for Natural Language Processing (NLP), AI-generated images, and more, becoming standard utilities.
- AI and NLP-powered Business Intelligence (BI): By 2025, the widespread adoption of AI-infused analytics will prompt 33% of A2000 enterprises to unify data intelligence, decision ops, and data literacy initiatives.
- Augmented Analytics: By 2025, half of A2000 enterprises (up from 33% in 2022) will seamlessly integrate analytics into enterprise or productivity applications, fostering data-driven decision-making.
- Harnessing Spatio-Temporal Data: By 2028, the most data-savvy 20% of G2000 companies will leverage spatio-temporal data processing, leading to increased demand for telematic analytics expertise.
- Migration of AI Workloads to the Cloud: In 2022, 56% of AI workloads resided in the cloud, a number projected to exceed 73% by the end of 2026.
- Bridging the BI/AI Skills Gap: A significant hurdle for 66% of AP enterprises is the scarcity of data science skills, hindering their journey toward becoming data-driven organizations.
- Maximizing the Value of BI/AI: For 62% of AP enterprises, the cost of solutions and implementation poses a considerable challenge on their path to becoming data-driven.
- Strengthening Data Governance and Security: Building robust data governance, security, and compliance capabilities becomes imperative for BI initiatives.
Highlighting these trends, Giri pinpointed product design and software development as the two areas poised to experience the most profound impact from generative AI in the next 18 months. Additionally, knowledge management, code generation, and marketing applications emerged as the three most promising use cases for organizations in the APAC region.
However, Giri acknowledged that along with its promising prospects, generative AI presents challenges. She emphasized that “Generative AI relies on machine learning to infer information, introducing potential accuracy concerns.” Furthermore, she noted that pre-trained large language models like ChatGPT are static, meaning they can only draw from data available up until 2021, when their training concluded.
Another significant concern raised by Giri is the rise of deep fakes, which leverage generative AI to create synthetic media such as images, videos, and audio. The authenticity of AI-generated content can be incredibly challenging to discern, raising ethical questions regarding authorship and copyright.
Data security and privacy are equally paramount when engaging with generative AI. Giri cautioned that uploading personal or proprietary information for model training purposes can inadvertently expose sensitive details, demanding robust safeguards and protective measures.
The Dynamic Landscape of Generative AI Players
The generative AI landscape encompasses a diverse array of stakeholders, each contributing in distinct ways. Among them, AI engineering companies stand out for their pivotal role in developing and refining models tailored to specific use cases. Simultaneously, processor and coprocessor manufacturers endeavor to create host CPUs capable of efficiently 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 robust infrastructure as a service. This seamless collaboration enables the transition of models from development to production, allowing businesses to leverage generative AI’s full potential.
Additionally, vendors specializing in servers, storage, and networking supply the necessary infrastructure to support model training at scale. Their contributions ensure the availability of the computational resources required for running complex AI workloads.
An essential segment of this ecosystem comprises AI application development companies that utilize APIs to craft innovative applications based on generative AI models. Moreover, large software vendors have started integrating these models into their products, witnessing the growing influence of generative AI across various industries. A noteworthy trend is the increasing adoption of generative AI by CRM and ERP vendors, who recognize its capacity to enhance their offerings.
Venture capital firms also play a pivotal role in this vibrant landscape, fueling innovation and breakthroughs by providing crucial funding for generative AI technology. These firms anticipate significant investment returns, reflecting the immense market potential of this groundbreaking field.
Generative AI Regulations: APAC’s Perspective
As the widespread application of generative AI continues to unfold, concerns about regulation and oversight are mounting globally. Regulatory bodies face the challenge of addressing critical areas such as data privacy, security, intellectual property rights, and the potential misuse of AI-generated content.
Giri explained that governments are grappling with the issue, striving to align existing policies with the ever-evolving landscape while simultaneously formulating new regulations. For instance, the Indian government has opted against stringent regulation of AI in the digital economy, asserting that overly restrictive laws could stifle innovation and research. On the other hand, the Cyberspace Administration of China has introduced security assessments to evaluate the impact of generative AI services before their public release.
In the APAC region, specific legislation specifically addressing generative AI is yet to materialize. Giri concludes that such regulations are often perceived as barriers to the spirit of innovation that drives the region’s thriving digital economy. As APAC continues to navigate this dynamic landscape, stakeholders must balance the need for regulatory frameworks with the promotion of innovation, ensuring responsible and ethical use of generative AI.
Conclusion:
APAC’s business landscape is abuzz with the potential of generative AI. Organizations are actively exploring and investing in this technology, with a keen focus on product design, software development, and compelling use cases like knowledge management, code generation, and marketing applications. As the APAC region embraces generative AI, it must also address challenges related to accuracy, deep fakes, data security, and privacy. Balancing regulation with innovation is crucial for harnessing the full potential of generative AI in a responsible and ethically conscious manner.
