发表机构
Swissi Institute for AI; Hochschule für Wirtschaft und Umwelt Nürtingen-Geislingen（Swissi人工智能研究所; 尼特林根-盖斯林根经济与环境应用科学大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对AI估值不确定性，提出基于里程碑的实物期权框架，区分AI整合者与提供者，分解期权假设，应用于AI能源SaaS公司产生可追溯估值区间，风险集中于后期延续期权。
AI 中文摘要
标准估值方法，包括折现现金流法、德国审计师协会的收入法标准IDW S 1以及市场倍数法，将里程碑概率、延续期权和风险转移压缩为不透明的汇总参数；这些方法均未提供将AI整合分解为可审计的期权层面假设的结构化协议。我们提出了一种行业无关的分类法，将AI整合者与AI提供者区分开来。AI整合者进一步根据其整合深度级别进行分类，范围从无整合到AI处于产品或流程核心。一个以里程碑为门控的实物期权叠加层将里程碑状态价值分解为五个组成部分，而基于层次分析法的成功就绪指数通过结构化的成对比较得出每个期权的概率，用于情景分析。将该框架应用于一家AI原生的能源软件即服务公司，产生了可追溯至可识别期权层面假设的连贯估值区间。风险集中在后期延续期权中，这与AI提供者的结构性预测相符。该协议适用于企业生命周期的各个阶段，包括并购尽职调查。该案例是对协议连贯性的单一企业演示，而非实证验证；针对已实现退出后估值的多案例测试留待未来研究。
英文摘要
Standard valuation methods, including discounted cash flow, the income approach standard IDW S 1 of the Institute of Public Auditors in Germany, and market multiples, compress milestone probabilities, continuation options, and risk shifts into opaque aggregate parameters; none provides a structured protocol for decomposing AI integration into auditable option-level assumptions. We propose an industry-agnostic taxonomy separating AI Integrators from AI Providers. AI Integrators are further classified by their Integration Depth Level, ranging from no integration to AI at the core of the product or process. A milestone-gated real-options overlay decomposes milestone state value into five components, and an Analytic Hierarchy Process-based Success Readiness Index derives per-option probabilities from structured pairwise comparisons for scenario analysis. Applied to an AI-native energy software-as-a-service firm, the framework yields a coherent valuation band traceable to identifiable option-level assumptions. Risk concentrates in later-stage continuation options, matching the structural prediction for AI Providers. The protocol applies across the firm lifecycle, including mergers and acquisitions due diligence. The case is a single-firm demonstration of protocol coherence, not empirical validation; multi-case testing against realised post-exit valuations is left to future research.
Comments19 pages, 5 figures, 7 tables. Published in Swissi AI Journal under CC BY 4.0
Journal refSwissi AI Journal, Volume 2026, Article SAIJ-f3jtignfyge3 (2026)
