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If 2023 was the “Year One of Generative AI,” then 2024 is set to be the “Year One of Enterprise AI.” Looking back over the past year, AI technology has been more focused on consumer-facing applications. Whether it's large language models that generate text or multimodal models that generate images from text, these tools have primarily been used to assist with simple office tasks or provide entertainment. However, generative AI still has a way to go before it is mature enough for enterprise-level applications. With ongoing advancements in technology, 2024 is expected to see more enterprise AI applications come to fruition, making the integration of AI into various industries a key topic in the tech world. For example, you can use Picit.ai to generate images with AI. Broad Application Prospects Consumer applications represent only the tip of the iceberg for generative AI. Its true value lies in enterprise and industry applications. Chen Xudong, Chairman and General Manager of IBM Greater China, has publicly stated that enterprise AI applications have broader needs and potential compared to consumer applications. According to IBM's extensive experience in serving corporate clients, Chen believes that generative AI has significant opportunities in automating HR, finance, and supply chain processes, enhancing IT development and operations, managing corporate assets, and ensuring data security. The 2023 Global AI Adoption Index report, commissioned by IBM and completed by Morning Consult, shows that approximately 42% of surveyed companies worldwide have actively deployed AI in their operations. Notably, the report indicates that Chinese companies are at the forefront of enterprise AI adoption. Nearly half of Chinese companies have already adopted AI, 85% plan to accelerate AI adoption within the next 2-3 years, and 63% are actively exploring generative AI. From an industry perspective, sectors like finance, healthcare, legal consulting, and education are likely to see mature generative AI applications first. For example, in the finance sector, large models are currently used primarily for risk assessment and management, as well as building knowledge graph platforms. In risk assessment, large models can analyze vast amounts of historical and real-time data to predict market risks, evaluate credit risks, and provide more accurate and timely risk management decision support. In healthcare, industry-specific large models can learn from and analyze vast amounts of medical data to automatically identify pathological features, assisting doctors in diagnosing diseases more accurately and efficiently. Lin Daozhuang, Vice Chair of the IEEE Standards Association's New Standards Committee and Chair of the IEEE Digital Finance and Economy Standards Committee, noted that China's healthcare resources are relatively scarce. Many people have to wait a long time to see a doctor, and doctors are very busy. AI can assist doctors in quickly identifying medical images like X-rays and CT scans, significantly improving their efficiency and reducing patient wait times. Enterprise AI Platforms as the Optimal Solution Beyond finance and healthcare, other industries are also actively exploring the integration of generative AI with their business operations. The current approach to AI in various industries is reminiscent of the early attitudes toward cloud computing. AI is poised to “take over” from cloud computing and become the focal point of future enterprise digitalization efforts. According to Chen Kedian, President of IBM Consulting Greater China, cloud computing has driven enterprise digitalization over the past fifteen years, and the next fifteen years will be crucial for enterprise AI empowerment. He believes that hybrid cloud and AI will become the optimal solutions for enterprise digital transformation for a long time to come. Currently, there are three main ways enterprises are adopting AI: embedding it in software, using APIs, and building enterprise AI platforms. Embedding AI in software is the simplest way to enable business through AI, but it offers the least differentiation and doesn't allow for higher-level exploration tailored to the company's specific needs. API integration provides some level of differentiation, allowing companies to call external large language models according to their needs and achieve unique results. “This method is relatively economical, convenient, and allows for a certain degree of differentiation,” Chen Kedian explained. However, as more companies use the same large language models, the differentiation will gradually diminish over time. Currently, the best approach for enterprise AI is to build proprietary AI platforms, which is also IBM's main focus in the enterprise AI field. Chen Kedian believes that cloud computing will remain the primary means of enterprise digitalization for a long time, and AI's development trend is irreversible. “AI has already become a core competitiveness for enterprises, so companies need to build their own enterprise AI platforms,” he said. Although this approach requires more investment in the short term compared to the other two methods, the long-term return on investment is significant. A Growing Trend Building proprietary enterprise AI platforms has already become an important means for companies in various industries to enhance their competitiveness and differentiation. However, generative AI applications in the enterprise sector are still in their early stages. Looking at the current state of generative AI in enterprise applications, several issues still need to be addressed. First, the “hallucination” problem of large models is a primary factor limiting the development of enterprise AI. Unlike consumer applications, enterprise scenarios often require higher accuracy and security. The hallucination problem poses numerous risks in decision-making and safety, which is why generative AI has been slow to gain traction in industry-specific applications. Secondly, the current capabilities of large models are primarily in text and document processing, general conversation, and basic professional Q&A, as well as general visual tasks. However, they still lack the capability for high-level logical reasoning, accuracy in specialized language fields, recognition of discipline-specific images and videos, and “text-to-video” generation. According to Zhou Hua, head of large model industry applications at the Zhiyuan Research Institute, multimodal models are currently difficult to implement in industry applications, but he believes that multimodal models will be a key area of competition in 2024. As multimodal models become more mature.