发表机构
Stern School of Business, New York University（纽约大学斯特恩商学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究企业选择人工智能项目的难题，提出预期投资回报率（eROI）框架，将赌注分解为成功价值、成功可能性和所需投资三部分分别评级，打破困境，指导组建项目组合，以Compass候选产品为例说明该框架可区分人工智能项目优劣。
AI 中文摘要
企业在选择有回报的人工智能项目时面临困难：两个项目对聪明且积极的利益相关者来说可能看起来同样有前景，但却值得做出相反的决策。在住宅房地产经纪公司Compass，一个人工智能产品（可能出售推荐）标记了销售拓展机会，年总佣金收入达九位数，而另一个备受推崇的人工智能产品（上市时间定价工具）却被搁置。简单的投资回报率估计无法区分两者。我们提出了预期投资回报率（eROI）框架，将每个赌注分解为三个部分并分别评级：成功时的价值、成功的可能性和所需投资。这三个部分分别对应高管在构建项目前可回答的问题。分离这三个部分打破了常见的困境：团队在知道项目是否可行之前无法估计投资回报率，但不构建又无法知道是否可行。仅判断成功时的价值可解决循环问题。该框架还在排序前询问是否有足够好的想法，排序后指导组建一系列赌注组合而非只资助排名最高的单个项目。我们用Compass的候选人工智能产品说明了eROI。鉴于人工智能项目的固有不确定性，精确的投资回报率估计很难做出，对三个部分进行粗略的业务层面评级就足以区分优劣。
英文摘要
Firms struggle to choose AI projects that pay off: two projects can look equally promising to smart, motivated stakeholders and yet deserve opposite decisions. At the residential real-estate brokerage Compass, one AI product (Likely-to-Sell recommendations) flagged sales outreach opportunities and went on to account for nine figures in annual gross commission revenue. Another championed AI product (a Time-on-Market pricing tool) was rightly shelved. A simple ROI estimate could not distinguish the two. We present expected ROI (eROI), a framework that decomposes each bet into three components and rates them separately: Value if Successful, Likelihood of Success, and Investment Required. Each maps to a question executives can answer before building: How valuable would it be if it worked? How likely is it to work? And what would it cost to implement? Separating the three breaks a common catch-22: teams cannot estimate ROI until they know whether a project will work, yet cannot know whether it will work without building it. Judging Value if Successful on its own dissolves the loop, letting a team argue that a product would be valuable if it worked while it weighs how likely that is. The framework also asks, before ranking anything, whether there are enough good ideas on the table. After ranking, it guides assembling a portfolio of bets rather than funding only the single top-ranked project. We illustrate eROI on Compass's candidate AI products. Precise ROI estimates are hard to make given the inherent uncertainty of AI projects. Coarse business-level ratings of the three components are enough to tell strong bets from weak ones.
Comments27 pages, 2 figures, 1 table. Submitted to Big Data (SAGE)
