First, determine whether an AIGC commercial is worth greenlighting.
When brands see AI-generated visuals, they are often captivated by the visual impact and overlook the project's communication goals. AIGC commercials suit projects requiring rapid multi-version output, exploration of surreal scenes, or limited budgets with high visual density. If core selling points rely on authentic product details, precise textures, regulatory compliance, or real human interaction, traditional filming or CGI may be safer.
Three questions must be answered before project initiation. First, is the communication goal to build awareness, explain features, or drive conversion? Different goals determine the level of abstraction in AI visuals. Second, what is the audience's acceptance of AI-generated content? Tech audiences are typically more tolerant, while maternal, infant, or medical brands require caution. Third, is the delivery platform social media short-form video, website long-form video, or overseas advertising? Platform specifications affect aspect ratio, title-safe areas, and duration.
The benchmark is whether the brief includes verifiable visual references. If there are only written descriptions and no reference images or competitor visuals, the AI workflow can easily waste a lot of time exploring styles. We recommend the brand prepare at least three sets of references: one for the ideal style, one for the absolutely forbidden style, and one for the similar but acceptable style. Based on these references, the production team can evaluate the controllability of AI-generated results.
The risk lies in the randomness of AI-generated images. Even with the same prompt, different seed values produce different results. The brand needs to accept a process of multiple generation rounds plus curation, rather than expecting a single generation to be final. If the project timeline is extremely tight and multiple rounds of revisions are unacceptable, we recommend returning to traditional shooting.
The exception is when the brand already has mature 3D assets or a product digital twin. In this case, AI can be used for background generation, lighting enhancement, or motion extension, and the risk is relatively controllable. Conversely, if the product exists only as a physical object without a digital model, AI-generated product images may be distorted, requiring additional product scanning or modeling.
Key actions for turning a brief into an executable script
The key is to break the brief down into an actionable task list. The brand should provide communication objectives, target audience personas, core selling point priorities, brand tone, reference visuals, delivery platforms, copyright ownership, the maximum number of revision rounds, and the final approver. If any of these is missing, the production team needs to ask for clarification before kickoff.
After the production team receives the brief, the first step is to produce a creative brief that translates communication goals into visual strategy. For example, if the goal is to explain complex features, the visuals should emphasize structural breakdowns or process diagrams, and AI generation is suitable for creating abstract icons or dynamic data flows. If the goal is to build emotional connection, live-action performances or anthropomorphic characters are needed, and AI-generated human figures still carry an 'uncanny valley' risk.
The second step is to write a storyboard script, with each shot annotated for image content, motion style, duration, audio cues, and AI generation parameters. The storyboard does not need to be film-grade detailed, but it must define each shot's start and end frames. For AI-generated portions, the storyboard should note 'generation style reference' and 'allowable variation range'—for example, background elements can be swapped, but the main product must not be distorted.
The third step is to create style frames. The production team uses AI to generate static reference images, and only after the brand side confirms the style direction does the team move into dynamic generation. This step prevents a large amount of ineffective generation. Style frames need to cover main scenes, characters, props, and color palettes, and the brand side should confirm frame by frame rather than giving only verbal feedback.
The delivery consequence is that if the brief stage does not clearly state 'prohibit use of real-person portraits' or 'prohibit appearance of competitor branding,' later AI-generated content may unintentionally include these elements, leading to rework or legal risk. Therefore, the brief must include a negative list, such as 'do not show any recognizable brand logos' and 'do not use real celebrity faces.'
Balancing Filming and Asset Collection
AIGC commercials do not eliminate the need for filming entirely. Many projects require live-action footage as a foundation for AI generation, such as product close-ups, human movements, and environmental textures. Pre-production must determine which elements require filming and which can be fully AI-generated. The criteria are realism and interactivity.
The product itself must be filmed, especially when showcasing texture, reflections, button feedback, or scale. AI-generated product images often suffer from detail distortion, such as blurred text edges or incorrect structural proportions. After filming product footage, AI can replace backgrounds, add lighting, or generate dynamic environments, preserving realism while expanding creative possibilities.
Live-action filming is recommended for performances involving subtle expressions or gestures. AI-generated human motion often appears unnatural during complex interactions, such as interlacing fingers, grasping objects, or making eye contact. After filming talent, AI can change outfits, alter scenes, or create slow-motion effects while keeping the subject authentic.
Footage purity is critical during shooting. Live-action assets intended for AI processing should use solid-color backgrounds or green screens to facilitate keying and compositing. Additionally, capture high-resolution RAW formats because AI generation compresses details; clearer source footage yields better final quality.
A risk is that production crews may misunderstand AI workflows, lighting and framing traditionally and rendering footage unsuitable for AI processing. For example, traditional shoots favor shallow depth of field, but AI compositing requires more background information. Communicate with the post team before shooting to confirm framing margins, dynamic range, and color space.
Exceptions include purely virtual scenes or abstract visual projects, such as tech concept videos or fashion brand mood films. These projects can rely entirely on AI generation without live-action filming. However, brands must still provide line drawings or 3D models; otherwise, AI cannot generate accurate product representations.
AI and Human Collaboration in Post-Production Workflows
Post-production focuses on establishing controllable workflows for generation, selection, repair, and compositing. The first step is batch generation, creating multiple candidate clips using various prompts and parameters. The second step is selection, where editors and directors jointly choose clips matching storyboard intent and flag issues requiring repair.
Common issues include flickering, object distortion, garbled text, and inconsistent motion. Teams must combine traditional post-production tools to fix these problems. For example, use stabilization plugins for jitter, masks for local distortion, and inpainting tools to regenerate unstable areas. This process requires manual intervention and cannot be fully automated.
During editing, AI-generated clips often have inconsistent pacing and must be rearranged according to musical beats and narrative logic. Editors should monitor visual density to avoid viewer fatigue from consecutive high-information frames. Mark generation parameters for each clip on the timeline to enable quick adjustments later.
During color grading, AI-generated assets may have inconsistent color spaces and must be unified under a single color management workflow. First, establish the project's color tone, such as warm, cool, or high saturation; then perform primary grading on each clip before applying a unified stylistic grade. Protect skin tones during grading, as AI-generated characters often appear grayish or greenish.
Sound design is the most overlooked aspect of AIGC commercials. Since AI-generated visuals lack native audio, sound effects, ambience, and music must be designed from scratch. Add temporary audio tracks during editing to prevent mismatches between sound and visual rhythm later. During the final mix, balance the visual impact of AI footage with the audio, avoiding excessive bass.
Subtitles and motion graphics also require separate handling. Text within AI-generated footage is typically unreliable, so all subtitles must be recreated in post-production software. Subtitle styling must adhere to brand guidelines, including font, size, safe margins, and animation. For overseas marketing videos, prepare multilingual subtitle versions and ensure translation accuracy.
Without proper version control in post-production, teams may struggle to locate specific generation parameters when clients request changes, resulting in extensive regeneration. Adopt a naming convention like "ProjectName_SceneNo_VersionNo_Date" and update it after every revision.
Acceptance Checklist and Deliverable Standards
Acceptance checks should be conducted stage by stage. Clients should require a complete deliverables list from the production team, including scripts, storyboards, style frames, generation parameters, edit timelines, color grades, audio mixes, subtitle files, masters, and source files. Each item requires individual confirmation.
Script acceptance verifies coverage of all key selling points; storyboard acceptance checks shot continuity; and style frame acceptance ensures a unified visual direction. Generation parameter acceptance confirms that prompts, seed values, and model versions are recorded for each shot to facilitate future replication or modification. Edit timeline acceptance ensures all assets and revision history are preserved, rather than delivering only the final video.
Provide at least two color grade versions: one matching brand standards and one optimized for specific platforms. Audio mixes must include stereo and loudness-normalized versions, checked for distortion or noise floor issues. Subtitle files must be delivered in SRT or ASS format with verified timing accuracy. Masters must be uncompressed and textless, while source files must include all raw assets and project files.
Acceptance criteria must be contractually defined, including revision limits, response times, and the final approver. Clients should designate a single decision-maker at project kickoff to prevent conflicting feedback. With each delivery, the production team should provide an acceptance report detailing completion status and known issues, e.g., "Minor background flicker fixed; recommend mobile verification."
A key risk arises when clients approve only the final video while neglecting intermediate stages, leading to high revision costs. For example, failing to confirm storyboards early can result in massive rework if AI generates off-brand visuals. Therefore, clients should formally approve each stage and maintain written records.
An exception applies if the project is entirely AI-generated with no live-action footage, in which case acceptance focuses on generation parameters and style consistency. Brands may request a Generation Parameter Report from the production team, detailing prompts, negative prompts, sampling steps, and seed values for each shot to retain as knowledge assets.
Applicable Boundaries and Exclusions
AIGC commercials are unsuitable for projects requiring precise depiction of product dimensions, materials, functions, or safety certifications. For example, AI generation can be misleading for medical devices, automotive safety systems, or food packaging information. Such projects should use traditional filming or 3D rendering combined with authentic test footage.
This approach is unsuitable for scenarios requiring real celebrity endorsements or genuine customer testimonials. AI-generated characters cannot replace real endorsers, especially where legal disclaimers or brand trust are involved. If brands wish to feature real people, live-action filming is mandatory; AI face-swapping or generation is prohibited.
It is unsuitable for projects requiring real-time interaction or dynamic data, such as livestream ads, personalized videos, or user-input-driven content. AI generation currently suits offline production better, as real-time generation requires additional tech stacks and lacks stability.
It is unsuitable for industries with documentary, serious, or trust-based brand tones, such as law, finance, and healthcare. Audiences in these sectors value authenticity and authority, and AI-generated visuals may undermine trust. If AI must be used, limit it to backgrounds or auxiliary elements while keeping the main subject live-action.
It is unsuitable for projects with extremely tight deadlines that cannot accommodate multiple revision rounds. Although AI generation is fast, screening and fixing outputs take time. If a brand requires delivery within one day, traditional filming or template-based editing may be more reliable.
An exception exists if the brand already has mature AI generation workflows and an internal team capable of rapid iteration. In this case, AI commercials can test multiple creative directions or generate various versions for A/B testing. However, this requires the team to have experience with AI tools and post-production correction capabilities.
Recommended Next Steps
If the brand determines AIGC commercials suit the current project, we recommend starting with a short test clip, such as a 15-second social media video, to validate the workflow and team collaboration. The testing phase need not aim for perfection; the focus is confirming that brief breakdowns, generation parameters, post-production fixes, and acceptance processes run smoothly.
After testing, decide whether to scale to the full project. Meanwhile, brands should retain all generation parameters and style frames as AI visual assets for reuse in future projects. Do not expect instant success; AI workflows require continuous optimization.
If test results are unsatisfactory, do not force progress. Revisit the brief to reevaluate communication goals and visual references, or consider hybrid approaches such as live-action footage with AI-generated backgrounds. The ultimate goal is to serve brand communication, not to showcase technical prowess.
If you are preparing an AIGC ad project, first organize your brief, visual references, product or company materials, delivery platforms, and licensing scope before visiting theAIGC Video Services pageto translate abstract preferences into actionable production parameters.
