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