Checklist: Preparing Your Creative Assets for AI-Driven Video Ad Platforms

Stop losing performance to bad inputs: a practical asset checklist for AI-driven video ad platforms

If your AI video experiments are churning out low-CTR variations, high CPAs, or hallucinated brand elements, the problem is usually the inputs — not the model. In 2026 the platforms are smarter, but they still rely on well-prepared assets, clear metadata, and structured shot direction to produce scalable, high-performing creative variations. This checklist is a hands-on playbook you can use today to hand your AI video platform the raw material it needs to win.

Why asset prep matters now (and what changed in late 2025)

By early 2026 nearly 90% of advertisers use generative AI for video advertising. That means adoption is no longer a differentiator — creative inputs and governance are. Platforms introduced scene-level metadata acceptance and multi-asset manifests in late 2025, which increases what AI can do with good inputs and punishes messy ones.

"Nearly 90% of advertisers will use gen-AI to build video ads" — IAB (2026 reporting)

Put simply: high-quality masters, thoughtful shot lists, and machine-readable metadata let AI generate meaningful creative variations instead of random recombinations. The checklist below gives you file specs, a metadata schema, practical shot lists for common ad formats, compression rules, governance guardrails, and a QA playbook.

The one-page prep summary (use this before upload)

• Master files: Deliver highest-quality masters (ProRes / DNxHR) + web-ready MP4s
• Aspect ratios: Provide at least 3: 16:9, 4:5 (or 1:1), and 9:16
• Shot list &scene markers: Timecoded CSV or EDL with short directional copy for each scene
• Metadata manifest: JSON or CSV with tags for product, hero, CTA, emotion, audience
• Brand assets: Vector logos, color palette HEX, fonts (OTF/TTF) or font-family names
• Legal & rights: Releases for faces/music, licensed audio stems, and allowed/forbidden generation rules
• QA checklist: Cropping tests, captions, audio levels, privacy checks

Section 1 — File formats, resolution & compression: technical must-haves

AI platforms will accept many formats, but they perform best when you supply both an untouched master and specific compressed deliverables. Always keep your master archives uncompressed for re-versioning.

Masters (archive-grade)

• Container: MOV
• Codec: Apple ProRes 422 HQ or ProRes 4444 (for alpha channel)
• Resolution: native capture (e.g., 4K or 2.8K). Keep original aspect ratio.
• Frame rate: native (23.976/24/25/30). Note this in metadata.
• Audio: 48 kHz, 24-bit WAV or AIFF, separate stems for VO, music, SFX

Deliverables (platform-ready)

• Container: MP4 (H.264) for broad compatibility; AV1/HEVC optional for supported DSPs
• Bitrate targets: 16:9 @1080p → 6–12 Mbps; 9:16/720p → 4–8 Mbps (adjust higher for product detail)
• Keyframe interval (GOP): 2s—4s; variable bitrate preferred
• Color: Rec.709 for SDR, Rec.2020/PQ for HDR assets (label clearly)
• Alpha/overlays: Provide PNG/ProRes 4444 layers for logos/animated overlays

Compression & quality-control tips

• Deliver masters and then pre-compressed variants — let the platform use your compressed version for quick iterations.
• Avoid double-compression — supply highest-quality master, export final MP4 once with the exact settings, and keep checksums.
• Include silent audio stems (or 0 dB reference) for platforms that automatically add music.

Section 2 — Aspect ratios, safe areas & timing for performance

AI platforms will crop and reframe footage to fit social placements. Give them options so they can pick the best crop.

• Provide at least three crops: 16:9 (YouTube), 4:5 or 1:1 (feed), 9:16 (Reels/Stories/TikTok)
• Safe action area: Keep critical elements (product, text, CTA) inside the central 80% of the frame
• Title safe for mobile: Important text remains within 12% inset from all edges
• Shot durations by ad length:
  • 6s: 1–3 shots. Instant problem + product or CTA.
  • 15s: 3–6 shots. Hook (0–3s), solution (3–10s), CTA (10–15s).
  • 30s: 6–10 shots. Allow a quick argument or short story arc.

Section 3 — Shot list templates (timecoded & AI-friendly)

AI needs human direction. Don’t just hand it a folder of clips — give a shot list with purpose, preferred dialogue/VO, and emotion tags. Below are ready-to-use templates.

Short-form product ad — 15s

1. 00:00–00:02 — Hook shot: Close-up of the product solving a visible problem. Direction: "surprise, delighted"
2. 00:02–00:07 — Feature shot: Quick demo (hands, quick UX scroll) with on-screen text. Direction: "clarity, simple"
3. 00:07–00:12 — Lifestyle shot: Person using product in context. Direction: "relief, confidence"
4. 00:12–00:15 — CTA: Product + headline + CTA button animation. Direction: "urgent, easy"

Testimonial-style lead-gen ad — 30s

1. 00:00–00:03 — Hook: Headline stat on-screen + cut to participant
2. 00:03–00:12 — Problem: Participant describes pain point (VO). Direction: "empathic"
3. 00:12–00:22 — Solution: Product/service in action + short proof points (metrics overlay)
4. 00:22–00:30 — CTA: Testimonial voiceover + headline + clear next step

Unboxing / Product detail — 60s (for commerce catalog)

1. 00:00–00:05 — Branding intro: Logo + product name
2. 00:05–00:20 — Unboxing: Hands reveal product; 3 close-ups of features
3. 00:20–00:40 — Use-case montage: Product used in 3 environments (short cuts)
4. 00:40–00:55 — Social proof/ratings overlay
5. 00:55–01:00 — CTA: Offer and URL or QR

Section 4 — Metadata & manifest: make your assets machine-readable

Successful AI reversion depends on structured metadata. Include a manifest.json (or CSV) with scene-level, asset-level, and campaign-level fields. Below is a practical schema you can paste into your content operations.

Minimal manifest schema (recommended fields)

{
  "campaign_id": "BRAND_SPR_2026",
  "asset_id": "PROD_CAMPAIGN_MASTER_v01",
  "format": "16:9",
  "resolution": "3840x2160",
  "frame_rate": "30",
  "scenes": [
    {
      "scene_id": "s1",
      "start": "00:00:00",
      "end": "00:00:03",
      "shot_type": "close_up",
      "hero": "product",
      "emotion": "surprise",
      "voiceover_text": "Tired of X?",
      "on_screen_text": "Instant fix",
      "priority": 10
    }
  ],
  "tags": ["skincare","fast_results","promo"],
  "cta_text": "Shop now",
  "audience": "lookalike_2025",
  "legal_flags": {"faces_released": true, "music_license": "inc"}
}

Key fields to include:

• scene_id, start, end: timecodes for each usable segment
• shot_type: (close_up, medium, wide) helps AI prioritize crops
• hero / product_tag: identifies key objects for overlays
• emotion: short tags (joy, trust, urgency) used by creative models
• voiceover_text & on_screen_text: exact strings for captioning and A/B copy tests

Section 5 — Naming conventions, versioning & checksums

Use predictable names so programmatic loaders and reporting can match creative assets to outcomes.

• Filename pattern: BRAND_CAMPAIGN_ASSETTYPE_SHOT_v01_YYYYMMDD.ext
• Example: Acme_SPR23_Product_Master_s1_v01_20260110.mov
• Include a manifest checksum (SHA256) in the manifest file to ensure integrity
• Keep a human-readable changelog (CSV) with who changed what and why

Section 6 — Governance: prevent hallucinations and brand mistakes

AI can hallucinate logos, create unlicensed faces, or invent false claims. Protect performance and brand safety with explicit rules.

• Provide official logo files and forbid logo generation: include a "forbid_generation": ["logo","person"] field in manifest
• Supply approved product images and forbids: "allowed_substitutes" and "forbidden_elements" lists
• Use negative prompts or a governance.json to block unwanted creative directions (e.g., "no medical claims")
• Include legal metadata: face releases, model releases, music licenses, and usage windows

Section 7 — Captions, transcripts, and accessibility

Always include machine-readable transcripts and caption files. Platforms will use these to generate localized versions and improve personalization.

• Provide SRT and VTT files aligned to master timecodes
• Include a plain-text transcript for prompt seeding and semantic indexing
• Provide pre-translated captions for top markets where possible (or provide glossary terms)
• Flag non-speech audio cues in transcripts (e.g., [music swell], [door slam])

Section 8 — Creative variations strategy & experiment playbook (practical)

Feed the AI with structured variables and limit the experiment scope so you can identify winners quickly.

1. Seed set (baseline): 3 master creatives (product, lifestyle, testimonial)
2. Variation grid: iterate on 3 variables only — headline, CTA placement, music track (3x3x3 = 27 variations)
3. Platform run: Ramp 50% budget to AI-generated variants, keep 50% to best performing human-cut
4. Analyze: Use scene-level tagging to see which shot types and emotions drove the highest CTR/ConvRate
5. Scale: Lock top 3 combinations and expand to new audiences

Tip: In 2026, include signal-level feedback (UTM parameters, conversion micro-events) in your manifest so the platform can correlate creative elements with downstream events.

Section 9 — QA checklist before upload

• All masters present and checksummed
• Scene-level manifest matches timecodes exactly
• All logos and fonts included; generation forbidden where required
• Captions/transcripts uploaded and checked for typos
• Face and music releases attached
• Three crop variants rendered and reviewed for important content in safe area
• Audio levels normalized: -14 LUFS (streaming) with true-peak -2 dB
• Shortlist of 3–5 high-priority shots labeled as "hero" in the manifest

Real-world example: how a DTC brand cut CPA by 28% with better inputs

Case study (anonymized): a DTC skin-care brand was feeding a mix of inconsistent UGC and studio footage to an AI platform with minimal direction. The platform produced dozens of creative variations but the best performers had no consistent patterns. After implementing the checklist above the team:

• Delivered three masters + scene-level metadata
• Provided transcript and three crops
• Flagged priority shots (close-ups of product usage)

Result: within two weeks the platform produced higher-CTR variations and the campaign CPA dropped by 28% while ROAS improved 38%. The difference was traceable to scene-level tags and uniform captioning — which improved personalization and reduced wasted spend.

Troubleshooting common problems

Hallucinated logos or invented claims

Solution: Add explicit governance rules in the manifest and include vector logos. Forbid logo generation and insist on using provided assets.

AI crops out the product in 9:16

Solution: Mark product as "hero" with bounding box coordinates in scene metadata, or supply a 9:16-focused crop where the product is centered.

Generated voiceover sounds synthetic or off-brand

Solution: Supply approved voiceover stems or preferred voice samples and include voice characteristics in metadata (gender, age range, tone, accent).

Measurement templates (what to track)

• CTR by shot_type and emotion tag
• View-through rate (VTR) for each aspect ratio
• CPA and ROAS segmented by creative variant and audience
• Attribution: scene-level events correlated with micro-conversions (add-to-cart, sign-up)
• Negative signals: user-reported feedback and platform disapprovals

Wrapping up — the checklist you can copy/paste

Before you hand assets to an AI video platform, run this checklist:

1. Master file (ProRes) and web-ready MP4
2. At least 3 aspect ratios (16:9, 4:5/1:1, 9:16)
3. Scene-level manifest (JSON/CSV) with shots, emotions, and CTAs
4. Captions (SRT/VTT) and transcript (plain text)
5. Legal docs: releases and music licenses
6. Logo vectors, fonts, HEX palette, and forbidden-generation flags
7. Filename convention and SHA256 checksum in manifest
8. QA: cropping test, audio -14 LUFS, caption accuracy

Final thoughts — trends to watch in 2026

In 2026 the advantage will be with teams that treat AI as a creative multiplier and not an autopilot. Expect the next 12–18 months to bring:

• More scene-level bidding and attribution — platforms will let you bid on specific moments that drive conversions
• Standardized creative manifests across DSPs — making cross-platform handoffs smoother
• Better governance tooling baked into platforms to reduce hallucinations and legal risk
• Closer integration of creative analytics with bidding algorithms — creative-level signals will influence automated bidding

Invest time upfront in disciplined asset prep and metadata. The ROI is quick: faster iteration, fewer wasted spend, and higher win rates across placements.

Get the editable checklist & shot-list templates

Want the downloadable manifest template, shot-list CSV, and naming-convention generator? Visit ad3535.com/checklist or contact our Creative Ops team for a free asset audit. We’ll map your current library to the checklist above and show a 30/60/90 day rollout plan to increase creative velocity and lower CPA.

CTA: Prepare your assets the right way — upload a sample asset bundle to ad3535 and get a free prep scorecard and next-step playbook.

Related Reading

• Hybrid Micro-Studio Playbook: edge-backed production workflows
• Versioning Prompts & Model Governance
• Creator Commerce & Rewrite Pipelines
• Studio-to-Street Lighting & Spatial Audio
• Net Carbs, Sugar Alcohols and Syrups: Label Reading for Low‑Carb Mixers
• Building Discovery Loops: How Comments Help (or Hurt) Your 2026 Digital PR
• Amiibo Collector's Playbook: Where to Hunt Splatoon & Zelda Figures and When to Buy
• Store an Electric Bike in a Studio: Sofa-Friendly Racks, Covers, and Layouts
• Monitoring News to Avoid Dangerous Torrents: From Patches to Profit Scams