在AI浪潮以排山倒海之势重塑每一个行业的今天，AI相关的创业项目也如同"雨后春笋"般的疯狂生长。 在AI创业的过程中，创业者们站在了一个充满无限可能却又遍布暗礁的十字路口。 9月27日~28日（美西时间）由钛媒体集团携手NextFin.AI、GALA（全球亚裔领袖联盟）、Shanda Group（盛大集团） 与 Barron's China，在美国斯坦福大学成功举办的首届硅谷未来峰会——NEX-T Summit 2025上举办了以"AI应用创业与创新"为主题的圆桌对话环节，此次圆桌对话环节汇集了特朗普 媒体与科技集团董事、Renatus Tactical Acquisition Corp首席执行官埃里克·S·斯维德（Eric S. Swider）， Fundamental Research Labs联合创始人兼CEO杨光宇（Guangyu Robert Yang），AdTech、创意和增长领 导者、AppLovin前产品副总裁、投资者和董事会成员爱丽丝·艾哈迈德 （Alice Ahmed），Genspark.ai联 合创始人兼首席运营官桑文（Wen Sang），Mindstorm Studios 首席执行官巴 ...

Core Insights - The article discusses the challenges faced by companies during digital transformation, highlighting the disconnect between IT, business, and management perspectives, which leads to ineffective technology investments and unsatisfactory outcomes [1][5]. IT Thinking - IT thinking is characterized by a focus on advanced technology and system architecture, often leading to over-engineered solutions that do not align with actual business needs [3][5]. - An example is given of a pancake shop that invested in a fully automated pancake-making robot, which resulted in long wait times for customers and underutilized technology [3][4]. Business Thinking - Business thinking prioritizes immediate results and user experience, often at the expense of proper system implementation and data management [4][5]. - The pancake shop's manager demanded quick solutions, leading to manual processes that were error-prone and inefficient [4][5]. Management Thinking - Management thinking focuses on cost control and short-term returns, often neglecting the need for long-term investment in technology [4][5]. - The shop owner opted for the cheapest cash register, which led to operational issues and ultimately hindered the digital transformation efforts [4][5]. Babel Tower Dilemma - The article introduces the "Babel Tower Dilemma," where miscommunication between departments leads to wasted resources and stalled projects [6][8]. - Each department blames the others for failures, resulting in a lack of accountability and progress in digital initiatives [8]. Solutions to the Dilemma - To resolve the Babel Tower Dilemma, companies should align goals, mechanisms, and culture among IT, business, and management [9][12]. - Establishing a common language and shared vision can help bridge the gap between technical capabilities and business needs [10]. - Creating cross-departmental teams can ensure effective communication and execution of digital transformation projects [11]. Conclusion - The article emphasizes the need for a unified approach where IT, business, and management work together to create a cohesive digital strategy, transforming the "three kingdoms" into a collaborative entity [15].

Strategic Insights - The key to a successful AI strategy is not technological superiority but deep integration with business processes [2] - Not all problems are suitable for AI solutions; traditional methods can often provide more efficient and cost-effective results [3] - Pursuing long-term value in AI strategies often leads to greater success, as seen in the example of Amazon's investment in recommendation systems [4] - The ultimate measure of AI project success is the enhancement of business value, not the advancement of technology [5] Technical Considerations - The biggest barrier to AI implementation is not talent or funding, but "data silos" that hinder effective training and deployment of AI models [6] - Purchasing existing AI solutions is often more suitable for most companies than developing everything in-house [7] - Simpler, interpretable models are often more practical than complex models with large parameters [8] - The safety, ethics, and accountability of AI models are critical concerns that must be prioritized [9] Talent and Organization - Companies need talent that understands both business and AI, acting as a bridge between the two [10] - AI empowerment requires a culture where all employees understand AI's capabilities and limitations, rather than relying solely on a few experts [11] - Failures in AI projects are often due to organizational, cultural, and communication issues rather than technical shortcomings [12] - Cross-disciplinary talent is essential in the AI era to address the complexities of business [13] Implementation and Operations - AI deployment is not a one-time investment but requires ongoing optimization and monitoring [14] - Focusing on clearly defined small problems is often more successful than attempting to disrupt entire industries [15] - The user experience of AI tools is more important than the intelligence of the models themselves [17]