---
id: "action-combine-systems"
type: "action-item"
source_timestamps: ["¶29", "¶30"]
tags: ["deployment-strategy", "vendor-management"]
related: ["framework-hybridization-steps", "concept-dual-track-ai-strategy"]
speakers: ["Amit Joshi", "Mark J. Greeven", "Sophie Liu", "Kunjian Li"]
action: "Deploy Western AI for highly regulated/frontier tasks and Chinese AI for cost-sensitive, routine, or consumer-facing vertical applications."
outcome: "Maximized operational efficiency, reduced inference costs, and compliance with diverse regional regulatory landscapes."
source_url: "https://hbr.org/2025/09/how-savvy-companies-are-using-chinese-ai"
source_title: "How Savvy Companies Are Using Chinese AI"
sources: ["tail2"]
sourceVaultSlug: "hbr-seg-tail2"
originDay: 2
articleStem: "hbr-tail-123-using-chinese-ai"
sourceUrl: "https://hbr.org/2025/09/how-savvy-companies-are-using-chinese-ai"
sourceTitle: "How Savvy Companies Are Using Chinese AI"
---
# Implement a Dual-Track AI Deployment

**Action (Step 3 of [[framework-hybridization-steps]]):** Run **parallel gen-AI applications** based on use-case requirements — the operational core of the [[concept-dual-track-ai-strategy|dual-track strategy]].

**Allocation rule:**
- **Western models** (ChatGPT, Gemini) → high-accuracy, highly regulated sectors: **pharma, banking, government**.
- **Chinese models** → routine tasks, consumer goods, **retail, customer service, basic coding** — where regulatory demands are lower and cost sensitivity is higher.

**Outcome:** maximized operational efficiency, reduced inference costs, and compliance across diverse regional regulatory landscapes.

**Enrichment caveat:** weigh the **total cost of ownership**, not just per-token cost — compliance costs (Chinese content/data/algorithm rules), evolving **U.S. restrictions** on Chinese AI in sensitive contexts, and **EU AI-Act-style** constraints may erode part of the cost advantage. See [[question-us-tariffs-impact]].