---
id: "action-use-proprietary-slms"
type: "action-item"
source_title: "Don't Let AI Slop Muck Up Your Company's Processes"
source_url: "https://hbr.org/2026/06/dont-let-ai-slop-muck-up-your-companys-processes"
source_timestamps: ["§ 3. Define what value is being added."]
tags: ["ai-strategy", "model-deployment"]
related: ["claim-public-llms-low-value"]
speakers: ["Matthias Holweg", "Thomas H. Davenport"]
action: "Use proprietary SLMs on internal data for insights, relegating public LLMs to formatting tasks."
outcome: "Generates genuine business value and competitive advantage rather than generic, error-prone prose."
sources: ["execution"]
sourceVaultSlug: "hbr-seg-execution"
originDay: 8
articleStem: "hbr-sig-54-ai-slop-processes"
sourceUrl: "https://hbr.org/2026/06/dont-let-ai-slop-muck-up-your-companys-processes"
sourceTitle: "Don’t Let AI Slop Muck Up Your Company’s Processes"
---
# Deploy Proprietary SLMs for Insights

**Action.** Shift away from relying on public LLMs to generate core business content. Deploy proprietary Small Language Models (SLMs) — or customize larger models on your organization's proprietary data — to generate actual insights. Use public models such as [[entity-chatgpt-d54|ChatGPT]] and [[entity-claude-d8|Claude]] only for downstream formatting and styling.

**Outcome.** Generates genuine business value and competitive advantage rather than generic, error-prone prose.

This is **Step 3** of [[framework-four-steps-knowledge-decay]], grounded in [[claim-public-llms-low-value]]. Caveat (enrichment): the strategic emphasis on domain-tuned, proprietary models is well aligned with governance guidance, but the blanket claim that public LLMs 'add little value' is overstated — many low-risk tasks (drafting, summarization, brainstorming) gain real value from public models under human review.


## Related across articles
- [[claim-proprietary-models-not-competitive-advantage]]
- [[contrarian-off-the-shelf-over-proprietary]]
- [[concept-ai-orchestration-layer]]