亿欧智库2024年企业AI大模型应用落地白皮书

Industry and Company Involved * Industry: AI Large Model Application * Company: Not specified, but the report discusses various players in the AI large model application industry, including cloud providers, AI application development companies, and emerging large model application development service providers. Key Points and Arguments 1. Driving Factors for AI Large Model Application: * Policy Support: Governments at both national and local levels have accelerated the issuance of policies related to AI application and large models, focusing on data security, technological innovation, and application落地. * Technological Breakthroughs: Advances in deep learning, natural language processing, and multi-modal technologies have provided strong support for the development and application of AI large models. * Transformation Needs: In the face of fierce market competition and changing demands, enterprises urgently need AI to empower the improvement of operational efficiency and innovation capabilities. 2. Challenges in AI Large Model Application: * Lack of Tools and Solutions: Enterprises face challenges in data processing, lack of end-to-end solutions, and data privacy and security issues. * Diverse and Complex Computing Power: The diversity of computing power and models makes it difficult for enterprises to adapt and match. * Complex and High Barriers: The entire process from development to deployment of AI large model applications is complex and has high barriers. 3. Solutions and Practices: * Full-Process Solutions and Professional Capabilities: Service providers need to provide full-process solutions and comprehensive professional capabilities to help enterprises solve problems in all aspects of AI large model application development and deployment. * One-Stop Solutions: One-stop solutions covering data preparation, model selection, training, customization, deployment, integration, testing, and operation and maintenance can help enterprises efficiently implement AI large model applications. * Benchmark Case Analysis: By analyzing benchmark cases, enterprises can better understand the application path of AI large model applications and formulate their own strategies. 4. Future Trends and Strategies: * Trend 1: Intelligent Agents Develop towards Single Intelligent Agent Ability Expansion and Multi-Agent Collaboration: The application scenarios of intelligent agents are extensive, and the future will see the expansion of the boundaries of single intelligent agents and the construction of multi-agent collaboration mechanisms. * Trend 2: Data-Driven Decision Making: Enterprises will increasingly rely on data analysis and prediction models to provide decision-making support for management and promote digital transformation and intelligent upgrading. * Trend 3: Deep Integration with Business: Enterprises will explore the integration of AI large model applications with core business processes to achieve automation and intelligence, further enhancing competitiveness and innovation capabilities. * Strategy 1: Focus on Business Scenario Demand and Reasonably Select Models: Enterprises should analyze their business needs and select appropriate models based on their specific scenarios. * Strategy 2: Evaluate Data Richness and Quality: Enterprises need to ensure that they have sufficient data to support model training and evaluation, and pay attention to data diversity, timeliness, and privacy protection. * Strategy 3: Establish a Continuous Learning and Iteration Mechanism: Enterprises need to establish performance monitoring and feedback mechanisms for AI large model applications and continuously optimize models based on business needs and user feedback. * Strategy 4: Explore Deep Integration with Business: Enterprises should explore the integration of AI large model applications with core business processes to achieve automation and intelligence. * Strategy 5: Clarify Task Nature and Optimize Processes: Enterprises need to clarify the nature of tasks and optimize processes based on business logic. * Strategy 6: Clarify Technical Selection and Compatibility: Enterprises need to consider factors such as framework maturity, ease of use, scalability, and compatibility with existing systems when selecting AI large model frameworks. * Strategy 7: Cultivate Independent AI Talent and Teams: Enterprises need to introduce and cultivate AI professionals to support the continuous development and innovation of AI large model applications.