---
id: "action-fine-tune-internal-data"
type: "action-item"
source_timestamps: ["§ Seven Imperatives for Creating Diverse Agentic Teams"]
tags: ["fine-tuning", "hr-tech"]
related: ["framework-seven-imperatives", "concept-structural-ai-diversity"]
action: "Use internal HR and employee survey data to fine-tune agent models."
outcome: "Aligns the agentic workforce with the specific cultural and demographic composition of the enterprise."
speakers: ["Mark Purdy"]
sources: ["agentic"]
sourceVaultSlug: "hbr-seg-agentic"
originDay: 6
articleStem: "hbr-new-28-agent-teams-different-models"
sourceUrl: "https://hbr.org/2026/06/the-strongest-teams-of-ai-agents-will-be-built-using-different-models"
sourceTitle: "The Strongest Teams of AI Agents Will Be Built Using Different Models"
---
# Fine-Tune with Internal HR Data

**Imperative 3 of the [[framework-seven-imperatives]].**

**Action:** Transnational enterprises should use their vast internal datasets — **HR systems, employee surveys, and psychometric evaluations of employees' personal styles** — to fine-tune **small-language models (SLMs)**, so the agentic workforce reflects the unique composition and culture of the human workforce it supports (see [[concept-agentic-workforce]]).

**Outcome:** Aligns the agentic workforce with the specific cultural and demographic composition of the enterprise.

**Enrichment validation — HIGH-RISK / CONTROVERSIAL:** Enterprise fine-tuning on proprietary corpora is common, but using **HR data and psychometric assessments** as training data raises serious issues: consent and purpose-limitation under privacy law (GDPR), and the risk of **encoding internal biases, stereotypes, or sensitive attributes**. Bias/fairness audit frameworks caution against uncritical use of sensitive HR data. Embedding HR data in models can *undermine* diversity objectives by codifying existing power structures. Treat as feasible but requiring **strong legal/ethical review**.