AI暴露与AI韧性：软件及基于软件的业务模型的二维评估框架
AI Exposure and AI Resilience: A Two-Dimensional Assessment Framework for Software and Software-Based Business Model
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中文总结 AI 辅助
本文提出AI-ER二维评估框架，分别衡量AI对软件业务模型的变革压力与企业吸收压力、经济利用AI的韧性，并支持基于证据的评估与比较。
中文摘要 AI 辅助
人工智能正在改变软件生产以及基于软件的业务模型的经济性。经典的技术尽职调查主要考察架构、可扩展性和技术债务等技术属性。这些标准未能完全捕捉AI如何影响公司的价值主张、竞争地位、利润率或客户获取渠道。本文开发了人工智能暴露与韧性（AI-ER）作为二维评估框架。AI暴露描述了AI为业务模型带来的变革压力。AI韧性描述了公司吸收该压力、适应变化条件并以经济上可行的方式利用AI的能力。两个维度的指标源自当前AI能力、其部署条件以及关于业务模型和组织适应性的相关研究。该模型将暴露与韧性分开，并增加了对证据质量和置信度的明确评估。它可首先使用公开信息应用，随后使用内部证据进行细化。结果是一个可追溯的公司概况，支持比较而不掩盖潜在证据中的不确定性。本文还指定了初始评分逻辑和实证验证程序。
英文摘要
Artificial intelligence is changing both software production and the economics of software-based business models. Classical technology due diligence mainly examines technical properties such as architecture, scalability, and technical debt. These criteria do not fully capture how AI can affect a company's value proposition, competitive position, margins, or access to customers. This paper develops Artificial Intelligence Exposure and Resilience (AI-ER) as a two-dimensional assessment framework. AI exposure describes the pressure for change that AI creates for a business model. AI resilience describes the company's ability to absorb that pressure, adapt to changed conditions, and use AI in an economically viable way. Metrics for both dimensions are derived from current AI capabilities, their deployment conditions, and relevant research on business models and organizational adaptability. The model keeps exposure and resilience separate and adds an explicit assessment of evidence quality and confidence. It can be applied first with public information and later refined with internal evidence. The result is a traceable company profile that supports comparison without concealing uncertainty in the underlying evidence. The paper also specifies an initial score logic and a procedure for empirical validation.
发表机构
- Findustrial GmbH（芬德斯特里亚尔有限公司）
- Munich University of Applied Sciences（慕尼黑应用科学大学）
机构由 AI 辅助整理，请以论文原文为准。
