AI Strategy: How to Choose What AI Product to Implement

2026-07-26

AI Summary

This study addresses the challenge enterprises face in evaluating the true returns of AI initiatives due to uncertain feasibility, a context where traditional ROI methods often fail. To overcome this limitation, the authors propose an expected Return on Investment (eROI) framework that decouples AI project assessment into three independently evaluable business dimensions: value upon success, probability of success, and required investment. This approach enables efficient pre-implementation decision-making and facilitates the construction of diversified AI project portfolios. Notably, the framework requires no complex modeling—only qualitative executive judgments on key dimensions—thereby circumventing the common “build-to-evaluate” dilemma. Empirical application at Compass demonstrated its practical utility: high-value projects such as the Likely-to-Sell recommendation system, which generated nine-figure annual revenue, were successfully prioritized, while low-potential initiatives were terminated early, validating the framework’s discriminative power and operational effectiveness.

Abstract

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.

Problem

• AI project selection
• return on investment
• decision-making framework
• artificial intelligence implementation
• business value assessment

Innovation

• expected ROI
• AI project selection
• value decomposition
• investment decision framework
• AI portfolio management