---
id: "concept-experimentation-trap"
type: "concept"
source_timestamps: ["¶2"]
tags: ["ai-failure-modes", "scaling"]
related: ["concept-pilot-theater", "concept-performance-drive", "claim-95-percent-failure", "entity-nathan-furr", "entity-andrew-shipilov"]
definition: "A failure mode where AI pilots never connect to customer value or scale beyond the lab."
source_url: "https://hbr.org/2025/09/what-companies-with-successful-ai-pilots-do-differently"
source_title: "What Companies with Successful AI Pilots Do Differently"
sources: ["execution"]
sourceVaultSlug: "hbr-seg-execution"
originDay: 8
articleStem: "hbr-foci-60-successful-ai-pilots"
sourceUrl: "https://hbr.org/2025/09/what-companies-with-successful-ai-pilots-do-differently"
sourceTitle: "What Companies with Successful AI Pilots Do Differently"
---
# The Experimentation Trap

## The Experimentation Trap

A phenomenon where organizational AI pilots remain confined to the laboratory environment and never successfully connect to actual customer value or scale across the enterprise. It represents a state of perpetual testing without realizing bottom-line returns, contributing heavily to the 95% failure rate of generative AI programs (see [[claim-95-percent-failure]]).

**Definition:** A failure mode where AI pilots never connect to customer value or scale beyond the lab.

### Provenance of the term
The "experimentation trap" framing is attributed to [[entity-nathan-furr]] and [[entity-andrew-shipilov]], authors of a recent HBR piece warning that innovation activity that never leaves the lab produces the illusion of progress without value.

### Relationship to other concepts
- The trap manifests operationally as [[concept-pilot-theater]] — celebrating pilot launches and activity while never demanding scaled business results.
- The antidote is [[concept-performance-drive]] — the SHAPE dimension that enforces ROI discipline, execution rhythm, and cross-functional scaling.

### Enrichment context
External analyses of the underlying MIT Project NANDA / Media Lab research corroborate this pattern: organizations run many proofs-of-concept on weak data foundations, with undefined success criteria and assumed adoption, so POCs "never make it past the demo stage." Forbes' coverage contrasts visible demo "confetti" with foundational implementation, noting that only ~5% of pilots transition into production with quantifiable value.


## Related across articles
- [[concept-narrow-deep-use-cases]]
- [[claim-widening-performance-gap]]
- [[claim-marginal-business-impact]]