生成式人工智慧企業生產力工具比較分析–以聯發科DaVinci與微軟Copilot為例

Abstract

隨著生成式人工智慧技術於2022年底迅速崛起，企業正迎來新一波生產力轉型的契機。生成式人工智慧具備學習資料模式並產出內容的能力，已逐步應用於企業業務中，展現出優化既有流程甚至重塑營運模式的潛力。在企業導入此類生產力工具以提升競爭力的過程中，需同時面對技術架構選擇，以及內部技術能力與外部市場條件等多重考量。 本研究以聯發科技DaVinci平台與Microsoft Copilot為研究個案，探討兩款企業級生成式人工智慧生產力工具於部署架構與應用模式上的差異。研究採用文獻回顧與個案分析法，首先梳理生成式人工智慧的技術發展脈絡、企業導入架構與應用策略，再進一步分析全球市場趨勢與臺灣企業的實際應用現況。在個案分析部分，深入比較聯發科技DaVinci平台與Microsoft Copilot在功能模組設計與技術特性上的表現，並從部署彈性、資料安全、系統擴展性、法規遵循與應用場景等層面進行探討。 研究結果顯示，DaVinci平台在資料管理與工具擴展性方面表現較為優異，特別是支援地端部署的特性，有助於企業強化資料主權與隱私；而Copilot則憑藉其高度與既有生態系的整合，在導入簡便、成本與法規相容性方面具備明顯優勢，較適合需要快速導入的企業。根據臺灣產業在AI應用成熟度與數位化程度上的差異，兩者各有適用情境，研究結果也給予各產業導入工具建議。 最後，研究亦針對生成式人工智慧工具的供應商與導入企業提出進一步建議。供應商應積極強化資料治理模組與法規透明度，主動建立並公開其遵循的國際合規框架，協助臺灣企業提升AI治理能力並降低導入門檻。同時，企業導入者亦應將生成式人工智慧納入中長期發展藍圖，明確設定應用目標與成效評估機制。此外，供應商若能強化軟硬體整合能力並推動使用者友善策略，將有助於拓展企業應用市場並提升整體競爭力。

With the rapid emergence of generative artificial intelligence technologies in late 2022, enterprises are facing a new wave of productivity transformation. Generative AI, which is capable of learning data patterns and producing content autonomously, has been gradually integrated into various business operations, demonstrating its potential not only to optimize existing workflows but also to reshape business models. As organizations seek to adopt such productivity tools to enhance competitiveness, they must consider multiple factors, including application models, deployment infrastructure, internal technical capabilities, and external market conditions. This study focuses on two enterprise-level generative AI productivity tools—MediaTek’s DaVinci platform and Microsoft Copilot—as case studies to explore differences in their deployment architectures and application models. Adopting a methodology that combines literature review and case analysis, the research first outlines the development of generative AI technologies, enterprise implementation frameworks, and application strategies. It then examines global market trends and the current state of adoption among Taiwanese enterprises. The case study further compares the functional module designs and technical characteristics of the DaVinci platform and Microsoft Copilot, analyzing them in terms of deployment flexibility, data security, scalability, regulatory compliance, and practical use cases. The findings indicate that the DaVinci platform performs well in data management and tool extensibility, particularly through its support for on-premise deployment, which enhances enterprise data sovereignty and system integration. In contrast, Microsoft Copilot leverages strong integration within Microsoft’s ecosystem, offering notable advantages in process automation, cost-effectiveness, and regulatory compatibility, making it more suitable for organizations requiring rapid implementation. Based on varying levels of AI maturity and digitalization across Taiwanese industries, the study provides contextual recommendations for selecting appropriate tools. Lastly, this research offers strategic suggestions for both AI solution providers and adopting enterprises. Providers are advised to strengthen data governance modules and improve regulatory transparency by establishing and publicly disclosing international compliance frameworks, thereby helping Taiwanese companies enhance AI governance capabilities and reduce adoption barriers. Enterprises are encouraged to incorporate generative AI into their mid- to long-term strategic planning, with clearly defined objectives and performance evaluation mechanisms. In addition, providers that enhance software-hardware integration and develop ecosystem strategies will be better positioned to expand enterprise applications and improve overall competitiveness.