The Era of Enterprise AI Has Arrived. How Can AI Better Empower Business Growth https://www.picit.ai/ https://www.picit.ai/features/ai-image-generator
If 2023 was the inaugural year for generative AI, then 2024 is set to become the year of enterprise-level AI. Over the past year, AI applications have been more prominent on the consumer side, with large language models (LLMs) generating text and multimodal models creating images. These applications mostly assist with simple tasks or provide entertainment like Picit.ai. However, generative AI is not yet mature enough for widespread enterprise-level use. As technology continues to advance, 2024 is expected to see more enterprise-level AI applications come to fruition. The integration of AI into industries will likely be a major topic in the tech world this year. Broad Application Prospects Consumer applications are just the tip of the iceberg when it comes to the value of generative AI. The real potential lies in enterprise and industry applications. Chen Xudong, Chairman and General Manager of IBM Greater China, has publicly stated that enterprise-level AI applications have broader demand and potential compared to consumer applications. According to IBM’s experience serving enterprise clients, generative AI has vast opportunities in areas like HR, finance, supply chain automation, IT development and operations, asset management, and data security. A report commissioned by IBM, titled the “2023 Global AI Adoption Index,” revealed that approximately 42% of surveyed companies worldwide have actively deployed AI in their operations. Notably, Chinese companies are at the forefront of enterprise-level AI adoption, with nearly half actively using AI and 85% planning to accelerate AI adoption over the next 2-3 years. Additionally, 63% are exploring generative AI. Industries such as finance, healthcare, legal consulting, and education, which are service-oriented, are likely to be early adopters of mature generative AI applications. For example, in the financial sector, large models are currently used for risk assessment, management, and knowledge graph platform development. In healthcare, industry-specific models can learn from and analyze vast amounts of medical data, assisting doctors in diagnosing diseases more accurately and efficiently. Enterprise AI Platforms as the Optimal Solution Apart from finance and healthcare, other industries are also exploring the integration of generative AI into their business processes. AI is expected to take over from cloud computing as the next focus of enterprise digital transformation. Chen Kedian, President of IBM Consulting Greater China, believes that while cloud computing has been the foundation of digitalization over the past 15 years, the next 15 years will be defined by AI empowering enterprises. He suggests that a hybrid cloud combined with AI will be the optimal solution for enterprises. Currently, there are three main ways enterprises can implement AI: embedding it into software, using API calls, or building their own enterprise-level AI platforms. Embedding AI in software is the simplest method but offers the least differentiation. API calls offer some customization, allowing enterprises to access external large language models to meet specific needs. However, the most effective approach is to build a custom enterprise-level AI platform, which, although more resource-intensive upfront, promises significant long-term returns. Challenges and the Road Ahead While building enterprise-level AI platforms is becoming a crucial strategy for enhancing competitiveness, generative AI applications on the enterprise side are still in their early stages. Several challenges remain: Model “hallucinations: Generative AI can produce inaccurate or misleading information, which poses a significant risk in enterprise applications where accuracy and security are paramount. Current capabilities: The primary strengths of large models lie in text and document processing, general chat, and basic professional Q&A. They still struggle with complex reasoning, specialized language tasks, and the processing of images and videos related to specific domains. Security concerns: As enterprises consider adopting generative AI, they are highly concerned about data security. Utilizing generative AI often requires training models with proprietary data, raising fears about data leakage or misuse by competitors. Companies must implement strong encryption, access controls, security audits, and privacy policies to mitigate these risks. While enterprises and AI service providers are actively exploring and investing in AI, widespread adoption of mature enterprise-level AI applications will take time.