AI 店員幫手創新解決方案 - 以零售產業 2B 企業服務為例

Abstract

本研究旨在探討以 B2B 企業服務模式，運用人工智慧技術解決實體零售現場服務效率不足與勞動力斷層的創新解決方案。隨著 2025 年台灣零售業面臨「認知過載（Cognitive Overload；Sweller, 1988）」與「勞動力斷層（Labor Gap）」雙重結構性壓力，加上 Z 世代消費者對服務品質要求日益精明，傳統人力補足模式已難以為繼。

本計畫以「AI 店員幫手（AI Store Assistant）」為核心解決方案，透過建構零售業專屬垂直領域大型語言模型（Vertical Domain LLM）與檢索增強生成（Retrieval-Augmented Generation, RAG）架構（Lewis et al., 2020），協助一線店員即時取得精準商品資訊、動態促銷規則與情境化搭配建議，實現服務品質的標準化與規模化。本計畫採取 BTA (Business-to-Associate）先行的導入策略，優先賦能店員，建立內部使用信任與量化成效數據，再以可見的 ROI 指標驅動高層決策者擴大投資。

透過深度訪談與市場分析，本研究識別出消費者端三大心理門檻（強迫推銷焦慮、新手知識落差、自主探索被中斷）與品牌端三大結構問題（人力依賴與知識斷層、系統穩定性疑慮、ROI 可見性不足），並以資生堂東京櫃（Shiseido Tokyo Counter）作為旗艦場域驗證。財務規劃顯示，本計畫 Y1 以單一品牌 POC 建立信任，Y2 透過多品牌擴展使虧損大幅收斂，並在 Y3 達成單年 EBITDA 轉正（+938,000 元），Y3 單年營收達 NT$10,680,000，預計於第 22 至 26 個月達到損益平衡，對應門市規模約 25 家。本計畫採 Pre-A 策略輪定位，目標募資 NT$2,000 萬，優先引入具零售通路資源之策略性投資人。

結論強調，透過創新的 AI-First 業務模式、垂直領域知識資產累積，結合市場需求與品牌端數位轉型趨勢，本計畫具備顯著的競爭優勢與長期發展潛力，能夠在台灣美妝零售 AI 服務市場率先建立難以複製的競爭護城河。

This study explores innovative solutions leveraging artificial intelligence to address service inefficiency and labor force gaps in physical retail through a B2B enterprise service model. As Taiwan's retail industry faces dual structural pressures of "Cognitive Overload" and "Labor Gap" in 2025, compounded by increasingly discerning Generation Z consumers, traditional labor-supplementation models have become unsustainable. This plan centers on "AI Store Assistant," built upon a Vertical Domain LLM and Retrieval-Augmented Generation (RAG) architecture, enabling frontline store associates to instantly access accurate product information, dynamic promotion rules, and contextual recommendation suggestions, thereby standardizing and scaling service quality. The project adopts a BTA (Business-to-Associate) first deployment strategy, prioritizing associate empowerment to build internal usage trust and quantifiable performance data, subsequently driving management investment decisions through demonstrable ROI metrics. Through in-depth interviews and market analysis, this research identifies three consumer-side psychological barriers and three structural challenges on the brand side, validated through a flagship proof of concept at Shiseido Tokyo Counter. Financial projections indicate the venture achieves breakeven at approximately 25 store units around months 22 to 26. Year 3 EBITDA turns positive (NT$938,000), marking the first stage of self-sustaining cash flow. The fundraising target is set at NT$20,000,000 (Pre-A strategic round), prioritizing investors with established retail channel networks. The conclusion emphasizes that through an innovative AI-First business model and vertical domain knowledge asset accumulation, this project possesses significant competitive advantages and long-term development potential, capable of establishing an early, difficult-to-replicate competitive moat in Taiwan's beauty retail AI service market.