---
id: "contrarian-precision-in-measurement"
type: "contrarian-insight"
source_timestamps: ["§ Measure and Quantify the Impact"]
tags: ["metrics", "roi", "contrarian-insight"]
related: ["action-baseline-measurement", "claim-ai-saves-prospecting-time"]
challenges: "The expectation that enterprise technology investments require precise, granular ROI attribution models."
sources: ["commercial"]
sourceVaultSlug: "hbr-seg-commercial"
originDay: 5
articleStem: "hbr-foci-64-ai-broaden-customer-base"
sourceUrl: "https://hbr.org/2025/03/how-one-company-used-ai-to-broaden-its-customer-base"
sourceTitle: "How One Company Used AI to Broaden Its Customer Base"
---
# Precision is Overrated in AI ROI Measurement (Contrarian)

**Contrarian insight.** When isolating and quantifying the business impact of AI investments in a large organization, the authors assert that **"precision is less important than a reasonable evidence-backed approximation."** This challenges the rigorous, highly granular financial attribution models often demanded by CFOs for new technology investments.

It is the philosophical backing for the pragmatic baseline method in [[action-baseline-measurement]], which produced the "~40% time saved" figure in [[claim-ai-saves-prospecting-time]].

> **Counter-perspective (from enrichment):** Finance leaders and risk committees increasingly demand **granular, traceable ROI and risk metrics** for AI, especially for agentic systems with regulatory exposure. In heavily regulated or capital-intensive sectors, more precise attribution may be essential even if costly — so "precision is overrated" may **not generalize** beyond low-risk, high-volume workflows like prospecting.