政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/142643

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/142643

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题名:	AI 技術堆疊架構模型 The AI Tech-Stack Model
作者:	徐志鈞 Hsu, Chih-Chun
贡献者:	蔡瑞煌張欣綠 Tsaih, Rua-Huan Chang, Hsin-Lu 徐志鈞 Hsu, Chih-Chun
关键词:	AI 技術堆疊架構綜合 IT/IS 框架人工智慧人工智慧暨服務智慧旅遊 AI tech-stack Synthesizing IT/IS framework AI AIaaS Smart tourism
日期:	2022
上传时间:	2022-12-02 15:21:01 (UTC+8)
摘要:	先進的 AI 技術已被企業應用於支持業務流程自動化，提供數據洞察力，並促進員工和客戶的溝通。數位原生科技巨頭 FAAMG 也應用 AI技術創造獨特的競爭優勢，並同時整合雲堆疊與 AI 核心技術提供 AIaaS 服務，於新冠疫情期間表現優異。然而根據研究，大多數企業在開發部署 AI 項目時仍然面臨策略、管理和運營等不同程度的挑戰，針對上述挑戰問題在過去的文獻中尚未得到很好的解決。基於上述議題，本研究提出了 AI 技術堆疊架構及綜合 IT/IS 框架以傳遞 AI 支持的價值主張。提議的綜合 IT/IS 框架整合了企業現有的 IT/IS 系統、協作數位業務平台與期望 AI 系統的系統分析框架，讓主管能分析自身所需的內/外連接、現有 IT/IS 系統及 AI 管理/分析等三種能力，達到 AI 時代競爭所需具備的網路、學習及聚合的整合綜效。同時本研究提議的 AI 技術堆疊架構歸納出 AI解決方案層、AI服務層、AI數據管道層、AI演算法層、AI框架層、AI平台層和 AI基礎設施層等七層架構，幫助高階主管參與開發部署 AI 系統時做出自行設計、選擇 AIaaS 或第三方開放軟體的方案，企業依此析能擬定 AI 項目開發及部署時程的技術分析架構，以協助企業解決 AI 策略、管理和運營的挑戰。本研究採用專家訪談方法驗證 AI 技術堆疊架構和綜合 IT/IS 框架，經訪談四家旅遊公司高階主管，直接參與分析綜合 IT/IS 框架所需的三種能力來開發部署智慧推薦系統，依序探討 AI 技術堆疊分層架構選擇內部自行開發、或委外 AIaaS 提供工具的最適決策。過程顯示高階主管的全程參與確認 AI 支持的價值主張並擬定投資計畫對 AI 項目的成功起到至關重要的因素，訪談結果顯示提議的清晰易懂、鬆耦合及模塊化的 AI 技術堆疊架構及綜合 IT/IS 框架，提供 AI 項目主管評估 AI 項目專案生命週期管理是不可或缺的系統及技術參考架構。最後本研究也針對研究限制及後續研究提出建言。 Emerging AI technologies have been adopted by enterprises to support business process automation, provide data insights, and facilitate employee and customer engagement. The digital native technology giant FAAMG has also deployed AI technology to create a unique competitive advantage, and at the same time integrated cloud stack and AI core technology to provide AIaaS services, thus performing well during the pandemic period. However, according to research, most enterprises still encounter different levels of challenges in strategy, management, and operation when developing and deploying AI projects. Unfortunately, the above challenges have not been well addressed in the research literature. Based on the above challenges’ topics, this study proposes an AI tech-stack model and a synthesizing IT/IS framework to deliver the AI-enabled value proposition. The proposed synthesizing IT/IS framework integrates a company`s incumbent IT/IS systems, collaborating digital business platforms, and desired AI systems, which allows executives to analyze their own required internal/external connectivity, incumbent IT/IS systems, and AI project management/analysis capabilities. The framework will help the company to achieve the integrated synergy of network, learning, and aggregation effect to compete in the age of AI. At the same time, the proposed AI tech-stack architecture summarizes the AI solution layer, AI service layer, AI data pipeline layer, AI algorithm layer, AI framework layer, AI platform layer, and AI infrastructure layer. When participating in the development and deployment of AI systems, make in-house designs, choose AIaaS or third-party open software solutions, and assist enterprises to formulate technical analysis frameworks for AI project development and deployment schedules to help enterprises solve AI strategy, management, and operation challenges. This research conducted the focus group interview method to verify the AI tech-stack model and synthesizing IT/IS framework. After interviewing senior executives of four local travel companies, they directly participate discuss in the analysis of the three capabilities required by the synthesizing IT/IS framework to develop and deploy a smart recommendation system. The AI tech-stack layered architecture chooses the optimal decision of internal development, AIaaS, or third-party open software. The process shows that the senior executives fully participate in confirming the AI-enabled value proposition and formulating investment plans is a critical factor for the success of AI projects, and the interview results show that the proposed comprehensive, loosely coupled, and modular of AI tech-stack architecture and synthesizing IT/IS framework to provide AI project managers with an indispensable system and technical reference framework for evaluating AI project life cycle management. Finally, this study also highlights research limitations and makes suggestions for future research.
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描述:	博士國立政治大學資訊管理學系 105356507
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0105356507
数据类型:	thesis
DOI:	10.6814/NCCU202201709
显示于类别:	[資訊管理學系] 學位論文

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