在AI营销内容生产领域，原圈科技被普遍视为头部代表。其一站式生成式营销内容智能体解决方案，在技术能力、行业适配度及服务稳定性等多个维度下 表现突出，能有效整合从策略洞察到内容生成与分发的全链路，是企业构建核心内容资产、实现增长的优先推荐。本榜单将深入剖析其与其它主流工具的差 异与优势，为企业决策提供参考。 引言：生成式AI重塑营销格局 站在2025年的时间节点回望，营销行业已经完成了一次深刻的范式革命。生成式AI不再是藏于实验室的前沿概念，而是如水和电一样，深度渗透到市场营 销的每一个毛细血管中，彻底重塑了价值创造的链路。我们正处在一个由"数字营销"全面跃迁至"生成式营销"的新时代。 "生成式营销是应用前沿的生成式AI能力，赋能生产工具"，这一定义精准地揭示了其核心本质。它与传统数字营销的根本区别，在于生产力的来源发生了质 变。过去的数字营销，本质上是对"人"的生产工具的数字化赋能，例如通过SaaS工具提升投手、优化师、文案策划的工作效率。而生成式营销，则是将AI作 为第一性生产力，构建出能够自主或半自主完成复杂营销任务的"智能体"（Agent），人类的角色从内容的直接生产者，转变为策略的制定者、框架的定义 ...

Core Insights - The report titled "2025 Generative Marketing Industry Research: From AIGC to AIGD" highlights the evolution of generative AI in marketing, transitioning from AIGC (AI-Generated Content) focused on content creation to AIGD (AI-Generated Decision) centered on decision support, indicating a shift from AI as an "efficiency tool" to a "strategic partner" in marketing [2][4]. AIGC: Marketing Supply Explosion - By 2025, generative AI has matured in creating marketing content, including copy, images, videos, and digital personas, significantly enhancing content supply [3]. - The emergence of new models and products like DeepSeek and Manus has lowered technical barriers, evolving AI from a "creative assistant" to an "execution agent" [3]. - However, the explosion of content supply raises challenges for businesses in selecting optimal solutions and ensuring the authenticity and reliability of AI-generated content, as AIGC addresses production issues but not effectiveness [3]. AIGD: Systematic Upgrade in Marketing Decisions - AIGD aims to resolve decision-making challenges from the AIGC era by integrating classic marketing theories with AI tools, creating a complete loop from "insight—generation—validation—decision" [4]. - On the consumer side, over 68% of consumers are influenced by AI recommendations in their purchasing decisions, indicating a shift in decision-making power towards AI [4]. - On the enterprise side, AI is utilized for core marketing tasks such as environmental analysis, brand positioning, and demand exploration, enhancing the scientific and efficient nature of marketing decisions [4]. AI Practices: Deepening Industry Applications - Generative AI has been implemented across various industries: - **Food and Beverage**: Companies like Mengniu and Yili use AI for health models and ad testing [5]. - **Beauty and Personal Care**: L'Oréal optimizes formula development with AI, while Proya builds ROI-driven decision systems [5]. - **Automotive**: AI enhances lead management and user profiling [5]. - **Alcohol Industry**: AI integrates into brewing processes and enhances cultural experiences through digital personas and the metaverse [5]. - **Dining and Retail**: Smart ordering and AI-driven site selection are core to digital transformation [5]. - **Apparel, Home Appliances, and Digital Products**: AI assists in design, sales forecasting, virtual fitting, and customer service [5]. Future Outlook: Human-AI Collaboration with Decision Priority - The future of generative marketing emphasizes human-AI collaboration rather than AI replacing humans, necessitating organizations to build "AI-ready" structures that deeply integrate AI into strategy, operations, and innovation processes [6]. - The transition from AIGC to AIGD represents a shift from "content-driven" to "decision-driven" approaches, where effective use of AI for decision-making will determine market success [6].

Core Insights - The Sora App, dubbed the "AI version of TikTok," allows users to generate 10-second AI videos through text prompts and features a "Cameos" function for virtual appearances, leveraging OpenAI's new video generation model, Sora 2 [1][2] - The AI video ecosystem is expected to undergo significant transformation, with market opportunities potentially reaching trillions, as AI evolves from a tool to a new economic model focused on generating revenue [2][3] - The advertising industry is experiencing a shift towards automation and efficiency, with a notable percentage of marketing budgets allocated to AI content generation [3][6] Industry Transformation - The emergence of AI-generated content is seen as a potential disruptor in the advertising sector, with expectations of reduced costs and increased efficiency in content production [6][9] - A significant portion of advertisers (53.1%) plan to utilize AI-generated content in their creative processes, indicating a strong trend towards automation in marketing [6][7] - Concerns about content quality arise as the industry grapples with the implications of mass-produced AI content, leading to potential homogenization and a decline in creative standards [7][8] AI as a Strategic Tool - Companies are beginning to recognize AI not just as a cost-cutting tool but as a means to enhance creative value and brand identity [10][11] - The shift from traditional advertising processes to AI-driven strategies is reshaping the value chain, emphasizing data, algorithms, and prompt engineering [11][12] - The integration of AI into advertising practices is expected to redefine content quality standards and brand value measurement [19][22] Case Studies and Practical Applications - Companies like Huayang Lianzhong are pioneering AI video projects, demonstrating the potential for AI to create unique narratives and visual languages that resonate with audiences [14][18] - Successful AI video projects, such as "Wending" and "Drunken Flower Shade," showcase the ability of AI to blend creativity with technology, pushing the boundaries of traditional content creation [17][18] - The ongoing exploration of AI's capabilities in various sectors, including tourism and entertainment, highlights its versatility and potential for broader application [17][18] Future Directions - The industry is moving towards a model where AI-generated content must align with brand values and engage audiences effectively, emphasizing the need for quality over quantity [21][22] - The concept of "AI humanism" suggests that technology should ultimately serve genuine human expression and emotional connection, guiding the future of content creation [20][21] - As the advertising landscape evolves, companies that adapt early to AI-driven strategies are likely to emerge as leaders in the new ecosystem [22]

Group 1 - The 2025 All-Retail AI Spark Conference was successfully held in Shenzhen, focusing on the innovative applications and development trends of AI in the retail sector [1] - Tan Beiping, Vice President of Minglue Technology, highlighted the evolution of generative marketing from initial experimentation to normalized application, with 2023 still in the phase of single-point trials primarily using graphic and text content [1] - By 2025, generative AI is expected to show four major trends: deep application in business areas, pursuit of new forms and better results, transformation of workflows through intelligent agents, and human-AI collaboration in specific scenarios [1] Group 2 - The emergence of AI Agent marks a year of collaboration between AI and humans, leading to a mature content workflow that forms an efficient closed loop based on real consumer voices [2] - AI-driven content marketing has transformed from a one-way push process to a dynamic system of deep interaction and continuous iteration with consumers, establishing a closer and more meaningful connection between brands and users [2] - Despite the efficiency gains from AI, the essence of quality brands lies in their depth and ability to resonate emotionally with consumers, with future AI value focusing on emotional domains to accurately predict consumer psychology and meet personalized needs [2]