隨著AI技術快速迭代發展成熟，企業AI應用服務導入已逐步落地，然而中小企業在評估導入時，往往面臨技術不熟悉、導入風險難以評估及長期維護成本不確定等障礙。這些障礙本質上正是C2至C4所描述的隱性交易成本，若無法有效降低，即使產品的C1效益再明顯，最終仍難以讓中小企業主促成實際採購決策。
本研究以一帆科技自主開發之AI應用產品「帆智通」為研究個案。帆智通以LINE為前端介面，串接大型語言模型及企業後端系統，提供自然語言驅動的客服、訂單處理、教育訓練等互動服務，並提供適度的客製化調整空間，目標市場為具數位轉型需求之中小型企業。本論文研究方法採個案研究法，透過內部訪談與產品文件分析，並輔以各潛在客戶之需求訪談，建構帆智通在不同應用場景下的4C架構。
本研究發現，帆智通以 LINE 取代 APP 與 Web 開發，可顯著降低客戶的 C1 建置與人力成本；然而客戶對 LLM 準確性的疑慮與資料安全顧慮構成 C3 的核心障礙，帆智通透過 RAG 技術搭配來源連結確保回覆可追溯，並提供本地部署方案使資料不出客戶機房，以層疊式信任機制回應上述疑慮；中小企業主對 AI 技術及其潛力了解不足，則易使 C2 偏高，帆智通以具體應用場景取代抽象技術術語，結合 LINE 的高普及率與即掃即用的體驗展示，有效縮短客戶從認知到決策的評估路徑；C4 在導入前形成客戶的心理阻力，導入後則轉化為產品的客戶黏性與競爭護城河。本研究據此提出帆智通的市場推廣策略建議，並為同類型軟體服務商發展企業 AI 應用產品提供具實務操作性的行銷策略。

This study adopts the "4C Strategic Marketing Analysis Framework" proposed by Chiu Chih-Sheng (2020) — comprising the Buyer's Explicit Unit Benefit Cost (C1), Buyer's Information Search Cost (C2), Buyer's Moral Hazard Cost (C3), and Buyer's Asset Specificity Cost (C4) — as the primary analytical framework to explore how enterprise AI application services can create 4C value from the customer's perspective.
As AI technology continues to mature at a rapid pace, enterprise AI application services have gradually moved from concept to practical implementation. However, small and medium-sized enterprises (SMEs) evaluating the adoption of such services frequently encounter barriers including unfamiliarity with the technology, difficulty in assessing implementation risks, and uncertainty regarding long-term maintenance costs. These barriers are essentially the hidden transaction costs described by C2 through C4. If they cannot be effectively reduced, even the most apparent C1 benefits will ultimately fail to drive actual purchasing decisions among SME owners.
This study takes "Lumio," an AI application product independently developed by E-Fun Technology Corporation, as its research subject. Lumio uses LINE as its front-end interface, connecting large language models with enterprise back-end systems to deliver natural language-driven services including customer support, order processing, and employee training, while also offering a degree of customization. Its target market consists of SMEs with digital transformation needs. The research adopts a case study methodology, collecting data through internal interviews and product document analysis, supplemented by needs-assessment interviews with potential customers, to construct a 4C analytical framework for Lumio across different application scenarios.
By replacing traditional APP and Web development with LINE as the primary interface, Lumio can significantly reduce customers' C1 implementation and labor costs. However, customers' concerns regarding LLM accuracy and data security constitute the core barriers to C3; insufficient understanding of AI technology and its potential among SME owners tends to elevate C2; and C4 presents a psychological barrier prior to adoption, which subsequently transforms into customer stickiness and a competitive moat following successful implementation. Based on these findings, this study proposes market promotion strategy recommendations for Lumio and offers actionable marketing strategies for software service providers of a similar nature seeking to develop enterprise AI application products.

未來商務（2024年7月16日）。Micro SaaS 是什麼？專注特定痛點、輕巧易擴充，企業導入應注意哪些事？未來商務。https://fc.bnext.com.tw/articles/view/3133
邱志聖（2020）。策略行銷分析：架構與實務應用（五版）。智勝文化。
財團法人人工智慧科技基金會（2025）。2025台灣產業AI化大調查暨AI落地指引。財團法人人工智慧科技基金會。https://aif.tw/event/ai-research/
經濟部中小及新創企業署（2025）。2025年中小企業白皮書。經濟部中小及新創企業署。https://www.sme.gov.tw/category-tw-2852
LINE台灣（2024年12月17日）。LINE App 2024使用數據。LINE台灣官方新聞稿。https://www.linecorp.com/tw/pr/news/2024/1217/
Aarstad, J., & Saidl, A. (2020). Barriers to adopting AI technology in SMEs [Master's thesis, Copenhagen Business School]. Copenhagen Business School Research. https://research-api.cbs.dk/ws/portalfiles/portal/60704162/790410_Aarstad_Saidl_Barriers_to_Adopting_AI_Technology_in_SMEs.pdf
ARK Invest. (2023). Big Ideas 2023. ARK Investment Management LLC. https://www.ark-invest.com/big-ideas-2024
KPMG. (2025). 台灣產業AI應用趨勢與展望報告. KPMG Taiwan. https://assets.kpmg.com/content/dam/kpmg/tw/pdf/2025/07/tw-sectors-ai-application-survey-in-taiwan.pdf
Menlo Ventures. (2025, December 9). 2025: The state of generative AI in the enterprise. Menlo Ventures. https://menlovc.com/perspective/2025-the-state-of-generative-ai-in-the-enterprise/
Oldemeyer, L., Jede, A., & Teuteberg, F. (2024). Investigation of artificial intelligence in SMEs: A systematic review of the state of the art and the main implementation challenges. Management Review Quarterly, 75, 1185–1227. https://doi.org/10.1007/s11301-024-00405-4