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| Title: | AI軟體新創公司之營運策略與募資研究 - 以G公司為例
Operational Strategies and Fundraising Research of AI Software Startups – A Case Study of G Company |
| Authors: | 林志聰
Lin, Chih-Tsung |
| Contributors: | 李志宏
Lee, Chih-Hung
林志聰
Lin, Chih-Tsung |
| Keywords: | 生成式AI
多模態AI
通用型人工智慧
Generative AI
Multimodal AI
Artificial general intelligence |
| Date: | 2025 |
| Issue Date: | 2025-08-04 13:04:47 (UTC+8) |
| Abstract: | 隨著人工智慧（Artificial Intelligence, AI）技術的快速發展，全球各地湧現大量以生成式AI(Generative AI)為核心技術的新創公司，這些AI新創企業不僅改變了各種產業的運作模式，也加速了數位轉型的腳步。尤其自2024年多模態AI能從大型語言模型生成到圖片、聲音、影片等等，慢慢朝向通用型人工智慧(AGI)前進，對於早期AI軟體新創公司而言，要如何從技術轉化為產品價值、建立穩定的營收模式，同時又能在競爭激烈的市場中順利募資，是學術上一個尚未被深入探討的實務議題。
本研究以新創公司(簡稱G公司)為例，探討早期AI軟體新創公司之營運策略與募資模式，研究這類企業如何在技術導向下，創造出產品差異化的實際落地商業應用，同時由於AI產品常伴隨高研發成本與長銷售週期，所以企業需在產品定位、客群選擇、營收模式（如SaaS訂閱制或API授權）等方面進行精準營運規劃。
另外，如何募得足夠運轉資金以利持續開發與擴展市場，以及如何以最有利方式取的充足資金且公司經營權又不受影響也是新創公司必須注意的重要事情。在過去學術論文上對於這樣快速發展的AI軟體產業，很少有任何案例可以參考，本論文將以個案G公司為例與次級資料分析為基礎，歸納出結果給AI軟體新創企業在初創階段常見的營運策略與募資和風險挑戰的建議，協助創業者、投資人與政策制定者更有效理解生成式AI軟體此類企業之成長狀況與投資機會。
The rapid advancement of artificial intelligence (AI) technologies has led to the emergence of numerous startups worldwide, particularly those leveraging generative AI as their core technology. These startups are reshaping industry operating models and accelerating the pace of digital transformation. Since 2024, the evolution of multimodal AI—capable of generating text, images, audio, and video through large language models—has further propelled the industry toward artificial general intelligence (AGI). For early-stage AI software startups, a critical yet underexplored challenge is how to translate advanced technologies into tangible product value, establish sustainable revenue models, and secure funding in an increasingly competitive market.
This study examines the operational strategies and fundraising approaches of early-stage AI software startups through an in-depth case analysis of a startup company (referred to as G Company). The research explores how technology-driven startups achieve product differentiation and commercialize their innovations despite facing high research and development costs and long sales cycles. It also analyzes how these companies strategically define product positioning, select target customer segments, and determine appropriate revenue models, such as Software-as-a-Service (SaaS) subscriptions or API licensing. Furthermore, the study addresses the critical issue of how startups can obtain sufficient funding for continuous product development and market expansion while maintaining control over corporate governance. The study offers recommendations regarding operational strategies, fundraising practices, and risk management challenges that are common during the early growth stages of AI software startups. These findings aim to assist entrepreneurs, investors, and policymakers in better understanding the growth trajectories and investment opportunities within the generative AI software industry. |
| Reference: | 1.McKinsey & Company (2024). The economic potential of generative AI: The next productivity frontier. McKinsey Global Institute.
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20.林志宏（2023）。〈生成式AI的商業應用與策略轉型〉。《數位時代》，第327期。
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22.吳恩達（Andrew Ng）關於 AI agent 能力與產業落地的演講與論述（DeepLearning.AI） |
| Description: | 碩士
國立政治大學
經營管理碩士學程(EMBA)
112932120 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0112932120 |
| Data Type: | thesis |
| Appears in Collections: | [經營管理碩士學程EMBA] 學位論文
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