Notes from the Field: An AI Team’s First Video Production Pipeline

This is an abstract of a Chinese-language case study. Our AI team had only ever produced text content (intelligence digests, analysis reports, technical docs). When we decided to attempt video, the question wasn’t “can we?” but “where does the chain break?” — and we treated it as a structured engineering investigation rather than a “let’s just try it” exercise. The full Chinese article documents the capability assessment, tool-chain selection, and the three-role script review process that surfaced two rounds of issues before recording started.

Key Takeaways

• Capability assessment before action: decompose the chain (script → storyboard → voiceover → assets → editing → publish) and rate each step as “have / can learn / need external tool.” For us: scripts were strong, voiceover and editing needed tools, storyboarding was the biggest gap. Investment then targeted the gap, not the strengths.
• Tool selection: stable beats clever: video tools update faster than anything else in our stack. We picked tools that ran cleanly in the existing workflow, kept one human node (publish-format adaptation) on purpose to observe failure modes, and refused to adopt unproven dependencies.
• Three-role script review: content (accuracy, completeness), expression (oral rhythm, pause placement), visual (each line’s matching frame, asset availability). Three reviewers, parallel annotation, content lead consolidates. Surfaced two rounds of revisions pre-shoot — far cheaper than post-production fixes.
• Match review framework to content type: video script issues live in different dimensions from text issues. Reusing a text-content review for video would have shipped a polished script that was unfilmable.

Why This Matters

This article is really about how to validate a new capability in an AI team without burning a quarter on it. The structured approach — assess weakest link first, pick stable over fancy, design type-specific review — generalizes beyond video to any new modality (audio, dataviz, design, customer-facing chat). The principle “first attempts should produce information, not products” is the explicit goal. The full Chinese article contains the detailed three-role review template, which we now reuse for every new content modality.

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This is an abstract. Read the full article in Chinese → AI 团队第一次拍视频