AI Agent應用於企業銷售流程之研究 ─對工作流程影響與功能價值評估

Abstract

本研究整合產業分析與問卷實證，揭示了SalesTech產品正從單純提供建議的「副駕駛」演進為具備自主執行力的「代理人（Agentic AI）」，市場也從工具碎片化走向生態系整併的「營收作業系統」。然而，實證數據顯示企業端存在顯著的「數位斷層」，儘管Salesforce與Microsoft Dynamics 365在中大型企業市場平分秋色，但 Excel 與試算表依然是市場的絕對主力，佔比高達 54.1%。凸顯現有CRM系統錄入門檻過高的困境。此外，市場存在約 25.4%的「數位處女地」，這群無系統使用的群體多集中於小微企業與傳統產業，是 AI 銷售助理切入市場的絕佳藍海。值得注意的是，如 Apollo、ZoomInfo 等國際開發工具在亞太區滲透率極低（<7%），驗證了在地化語境與數據精準度缺失所導致的市場缺口。 在功能價值評估方面，不同職位與產業對 AI 的期待呈現明顯分歧。業務開發人員（BDR/SDR/AE）作為重度需求者，對「AI 潛在客戶搜尋」與「購買意圖偵測」表現出極高的渴望，平均分均超過4.5分。相較之下，銷售營運（Ops）最重視「CRM 自動紀錄」，以確保數據一致性；而高階主管則更關注「市場情報」與「任務自動管理」。從產業維度觀察，科技業側重於「成長導向（Growth-driven）」，追求漏斗前端的商機極大化；製造業則偏向「轉換導向（Conversion-driven）」，對 RFQ（詢報價）流程中的對話分析與報價優化具備更高評價。雖然「CRM 自動紀錄」是跨職能共認的效率基石，但「AI 潛在客戶搜尋（AI Prospect）」才是推動採購決策的「殺手級應用」。高達58.2% 的受訪者將其選為最具價值功能之首，顯示在競爭激烈的市場中，AI自動化獵取高精準名單的能力，被視為最能直接產生業績的核心價值。然而，對於「知識傳承與洞察管理」等長線指標，受訪者雖然在理論上給予高分，但在實際選擇時，仍排在「增加營收」與「行政減負」等燃眉之急之後。 針對研究結果應用於軟體開發商的建議，產品設計應採取「AI原生一體化平台」的演進策略，首先建立自動化紀錄的數據底座以徹底消除業務對 Excel 的依賴。開發商應積極擁抱 MCP（Model Context Protocol）等標準化協議，讓AI代理能跨越破碎的系統邊界共享上下文，並實現無痕數據錄入以打破資訊孤島。同時，面對國際通用型情報工具在亞太區滲透率極低與在地化數據缺乏的市場真空，軟體商可鎖定特定垂直產業，開發預設具備該產業邏輯的 Vertical AI CRM，並隨著 AI 代理承擔多數行政開發工作，將商業模式逐步轉向以執行成果或使用量為導向的新型定價機制。 對於企業導入AI 銷售科技的管理建議，組織應依據自身規模採取「雙軌分層」策略。五十人以下的小型企業由於沒有沉重的系統包袱，應直接導入具備自主開發能力的輕量化AI代理，以彌補人力短缺並實現規模化開發；而一千人以上的大型企業則應將AI定位為既有CRM的增強外掛，優先導入自動活動捕獲與對話情報工具，填補內部數據斷層並提升營收預測的準確度。在推動數位轉型時，管理層必須重新對齊績效考核，將「AI 輔助下的行為改變」納入一線業務指標，同時明訂清晰的人機交接邊界，讓AI負責漏斗前端的名單篩選，由人類專注於決策判斷與信任維護，以降低內部抗拒。

Integrating industry analysis with empirical survey data, this research reveals that SalesTech products are evolving from mere advisory “Copilots” into autonomous “Agentic AI,” signaling a market shift from fragmented tools toward integrated “Revenue Operating Systems.” However, empirical data highlights a significant “Digital Fault Line.” While Salesforce and Microsoft Dynamics 365 dominate the mid-to-large enterprise segment, Excel and spreadsheets remain the absolute market mainstay with a 54.1% share, underscoring the high entry barriers of existing CRM systems. Furthermore, a “Digital Virgin Territory” of approximately 25.4% exists—a group primarily composed of micro-enterprises and traditional industries—presenting a “Blue Ocean” opportunity for AI sales assistants. Notably, the penetration of international prospecting tools like Apollo and ZoomInfo is extremely low in the APAC region (<7%), validating a market gap caused by the lack of localized context and data precision. Regarding functional value assessment, expectations for AI diverge significantly across roles and industries. Business Development professionals (BDR/SDR/AE), as heavy users, show a high demand for “AI Prospecting” and “Buyer Intent Detection,” with average scores exceeding 4.5. In contrast, Sales Operations (Ops) prioritize “Automated CRM Logging” to ensure data consistency, while Executives focus on “Market Intelligence” and “Automated Task Management.”From an industry perspective, the Technology sector is “Growth-driven,” seeking to maximize top-of-funnel opportunities, whereas Manufacturing is “Conversion-driven,” placing higher value on conversation intelligence and quote optimization within the RFQ process. Although “Automated CRM Logging” is recognized cross-functionally as an efficiency cornerstone, “AI Prospecting” emerges as the “Killer App” driving procurement decisions. A staggering 58.2% of respondents ranked it as the most valuable feature, indicating that the ability to autonomously hunt for high-precision leads is viewed as the core value most directly linked to revenue generation. However, long-term metrics such as “Knowledge Transfer and Insight Management,” while theoretically scoring high, are prioritized below immediate needs like “Revenue Growth” and “Administrative Relief.” For Software Vendors, the study suggests an evolution toward “AI-Native All-in-One Platforms,” starting with an automated data foundation to eliminate dependency on Excel. Developers should embrace standardized protocols like the Model Context Protocol (MCP), enabling AI agents to share context across fragmented system boundaries and achieve seamless data entry to break down information silos. Given the market vacuum for localized data in APAC, vendors should target specific verticals by developing Vertical AI CRMs with pre-configured industry logic. As AI agents assume more administrative and prospecting workloads, business models should transition toward outcome-based or usage-based pricing. For Enterprises adopting SalesTech, a “Dual-Track” strategy based on organizational scale is recommended. Small enterprises (<50 employees), lacking legacy system burdens, should directly adopt lightweight AI agents with autonomous prospecting capabilities to compensate for labor shortages. Conversely, Large enterprises (>1,000 employees) should position AI as an enhancement layer for existing CRMs, prioritizing automated activity capture and conversation intelligence to bridge internal data gaps and improve forecasting accuracy. To drive digital transformation, management must realign performance evaluations to include “AI-assisted behavioral changes” as key metrics for frontline sales, while defining clear boundaries for human-AI handoffs—allowing AI to handle top-of-funnel filtering so humans can focus on high-stakes judgment and relationship building.