---
id: "claim-multidimensional-experimentation"
type: "claim"
source_timestamps: ["§ Stage 3: Experimental/Prototyping Portfolio (Experiment)"]
source_url: "https://hbr.org/2026/01/manage-your-ai-investments-like-a-portfolio"
source_title: "Manage Your AI Investments Like a Portfolio"
tags: ["experimentation", "adoption", "integration"]
related: ["concept-ai-learning-journeys", "contrarian-learning-vs-validation"]
confidence: "high"
testable: true
speakers: ["Faisal Hoque", "Erik Nelson", "Tom Davenport", "Paul Scade"]
sources: ["spine"]
sourceVaultSlug: "hbr-seg-spine"
originDay: 1
articleStem: "hbr-foci-61-ai-investments-portfolio"
sourceUrl: "https://hbr.org/2026/01/manage-your-ai-investments-like-a-portfolio"
sourceTitle: "Manage Your AI Investments Like a Portfolio"
---
# AI Experiments Must Test Beyond Technical Feasibility

> **Confidence:** high · **Testable:** yes

Testing only whether an AI model works technically is insufficient for enterprise success. AI experiments must be multidimensional, testing **enterprise viability** (integration costs and process fit) and **human desirability** (user adoption and perceived value) in addition to technical feasibility.

If an AI can perform its function but users refuse to adopt it, or integration costs are prohibitive, the experiment should **not** pass the stage gate to production. This claim is the evidentiary core of [[concept-ai-learning-journeys]] and the contrarian stance [[contrarian-learning-vs-validation]].

**External grounding:** Echoes IDEO's desirability–feasibility–viability triad; HBR/Appian research confirms process integration and modernization — not raw technology — drive realized AI value.