Core Insights - Generative AI is transitioning from pilot projects to production phases, with enterprise spending on AI skyrocketing to $13.8 billion in 2024, up from $2.3 billion in 2023, indicating a shift towards embedding AI into core business strategies [3][6][4] - 72% of decision-makers anticipate broader adoption of generative AI tools in the near future, reflecting a strong optimism within organizations [3][6] - Despite the positive outlook, over one-third of respondents are still unclear on how to deploy generative AI across their organizations, highlighting the early stages of this transformation [3][5] Investment Trends - 60% of investments in generative AI come from "innovation budgets," while 40% are from more conventional budgets, with 58% of that being reallocated from existing funds, indicating a growing commitment to AI transformation [5][6] - In 2024, enterprises are expected to invest $4.6 billion in generative AI applications, a significant increase from $600 million in the previous year [11] Application Areas - The leading use cases for generative AI include code collaboration assistants (51% adoption), customer service chatbots (31%), enterprise search (28%), information retrieval (27%), and meeting summaries (24%) [12][16] - Organizations are focusing on use cases that provide measurable ROI, with the top five use cases aimed at enhancing productivity and efficiency [16] Industry-Specific Applications - The healthcare sector is leading in generative AI adoption with $500 million in spending, utilizing tools for clinical documentation and workflow automation [32] - The legal industry is also embracing generative AI, with $350 million in spending, focusing on managing unstructured data and automating complex workflows [33] - Financial services are investing $100 million in generative AI to enhance accounting and compliance processes [34] - The media and entertainment industry is seeing $100 million in spending, with tools that support content creation and production [35] Technology Stack and Trends - The modern AI technology stack is stabilizing, with $6.5 billion in enterprise investment in large language models (LLMs) [37] - A multi-model strategy is becoming prevalent, with organizations deploying three or more foundational models for different use cases [41] - The adoption of retrieval-augmented generation (RAG) design patterns is rising, now at 51%, while fine-tuning remains rare at only 9% [45] Future Predictions - The emergence of AI agents is expected to drive the next wave of transformation, automating complex multi-step tasks [49] - Traditional vendors may face challenges from AI-native challengers, as dissatisfaction with existing solutions grows [23] - A significant talent shortage in the AI field is anticipated, with demand for skilled professionals expected to outstrip supply [51]

大模型作为产业变革的核心引擎。通过RAG、Agent与多模态技术正在重塑AI与现实的交互边界。三者协同演进，不仅攻克了数据时效性、专业适配等核 心挑战，更推动行业从效率革新迈向业务重构。本文将解析技术演进脉络、实战经验与未来图景，为读者提供前沿趋势的全局视角与产业升级的实践指 引。 作者 | 蒋进 出品丨腾讯云开发者 大模型技术正加速渗透至产业核心场景，成为驱动数字化转型的智能引擎。全球机器学习大会（ML-Summit）聚焦大模型技术的创新突破与产业实 践，深入探讨其前沿方向与落地路径。作为AI发展的核心驱动力， 检索增强生成（RAG） 通过动态知识融合技术突破大模型的静态知识边界； 智能体 （Agent） 借助自主决策与多任务协同能力重构人机协作范式； 多模态大模型 则依托跨模态语义理解技术解锁复杂场景的落地潜力。三者协同演进， 不仅攻克了数据时效性、隐私安全与专业适配等关键难题，更在医疗诊断、金融风控、智能制造等领域催生从效率革新到业务重构的行业级变革。 ML-Summit会议大模型内容分布 RAG： 大模型的动态知识引擎，解决模型静态知识边界、时效性与可信度问题。 大模型在很多领域表现出色，但依然存在局 ...