---
id: "action-secure-proprietary-data"
type: "action-item"
source_timestamps: ["§ Opportunities"]
tags: ["data-strategy", "moats"]
related: ["concept-competitive-moats"]
action: "Aggressively capture, structure, and legally secure proprietary, hard-to-simulate datasets specific to your industry."
outcome: "Creation of a defensible data moat that allows for superior fine-tuning of industry-specific AI agents."
speakers: ["Toby E. Stuart"]
sources: ["futures"]
sourceVaultSlug: "hbr-seg-futures"
originDay: 2
articleStem: "hbr-nm-99-genai-end-incumbent-advantage"
sourceUrl: "https://hbr.org/2024/11/could-gen-ai-end-incumbent-firms-competitive-advantage"
sourceTitle: "Could Gen AI End Incumbent Firms’ Competitive Advantage?"
---
# Secure and Expand Proprietary Datasets

**Action.** Aggressively capture, structure, and legally secure proprietary, hard-to-simulate datasets specific to your industry.

**Detail.** Because foundation models are becoming **commoditized**, the true competitive edge will belong to companies with large, hard-to-simulate, **legally obtained** datasets. Organizations should aggressively capture and structure proprietary data — particularly in regulated fields like **healthcare and finance** — to fine-tune agentic AI models. This is the primary surviving moat in [[concept-competitive-moats|the moat picture]] and directly enables competitive [[concept-service-as-software|Service as Software]] offerings.

**Outcome.** A defensible data moat that allows superior fine-tuning of industry-specific AI agents.

*(Enrichment: industry analyses consistently cite proprietary, hard-to-simulate data as the main durable advantage once foundation models commoditize.)*


## Related across articles
- [[contrarian-moat-workflow-not-tech]]
- [[concept-brand-as-coordinator]]