政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/158347
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  全文筆數/總筆數 : 118260/149296 (79%)
造訪人次 : 76933774      線上人數 : 396
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜尋範圍 查詢小技巧：
  • 您可在西文檢索詞彙前後加上"雙引號"，以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋，建議至進階搜尋限定作者欄位，可獲得較完整資料
    •  進階搜尋


    請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/158347


    題名: 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
    作者: 林志聰
    Lin, Chih-Tsung
    貢獻者: 李志宏
    Lee, Chih-Hung
    林志聰
    Lin, Chih-Tsung
    關鍵詞: 生成式AI
    多模態AI
    通用型人工智慧
    Generative AI
    Multimodal AI
    Artificial general intelligence
    日期: 2025
    上傳時間: 2025-08-04 13:04:47 (UTC+8)
    摘要: 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.
    參考文獻: 1.McKinsey & Company (2024). The economic potential of generative AI: The next productivity frontier. McKinsey Global Institute.
    2.PwC (2024). Sizing the prize: What's the real value of AI for your business and how can you capitalise?
    3.Gartner (2024). Forecast Analysis: Artificial Intelligence Software, Worldwide.
    4.CB Insights (2024). The State of AI: Investment Trends & Startup Landscape.
    5.OpenAI (2024). GPT-4 Technical Report. OpenAI Research.
    6.Anthropic (2024). Claude model capabilities and roadmap.
    7.Google DeepMind (2024). Gemini Technical Paper.
    8.Meta AI (2024). LLaMA Model Release and Roadmap.
    9.xAI (2024). Grok Model Overview. xAI Research.
    10.Crunchbase (2024). Global AI Startup Funding Trends.
    11.PitchBook (2024). Venture Capital Investments in AI Startups.
    12.NVIDIA (2024). AI Enterprise and Inference Acceleration Whitepaper.
    13.Harvard Business Review (2023). How AI Agents Will Transform the Future of Work.
    14.Appier Group Inc. (2024). Annual Financial Report and Investor Presentation.
    15.意藍資訊股份有限公司 (2024). 年度財報與公開說明書。
    16.國家發展委員會 (2024). 台灣人口統計及科技人才供需分析報告。
    17.行政院衛生福利部 (2024). 全國醫療機構統計年報。
    18.台灣醫療器材工業同業公會 (2024). 2024醫美市場分析報告。
    19.台灣經濟部統計處、衛福部、國發會相關產業調查資料（2023–2024）
    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。
    21.吳恩達（2023）。《AI轉型之路》。臺北：天下文化。
    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）
    描述: 碩士
    國立政治大學
    經營管理碩士學程(EMBA)
    112932120
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0112932120
    資料類型: thesis
    顯示於類別:[經營管理碩士學程EMBA] 學位論文

    文件中的檔案:

    檔案 描述 大小格式瀏覽次數
    212001.pdf951KbAdobe PDF0檢視/開啟


    在政大典藏中所有的資料項目都受到原著作權保護.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回饋