During project initiation, first determine whether communication goals allow for pixel-level imperfections.
The core advantages of AIGC commercials are generation efficiency and visual spectacle, but generative imagery inherently lacks detail stability. During project initiation, brands must first answer one question: does this campaign tolerate anomalies such as incorrect finger counts, distorted text, inconsistent lighting, or flickering object edges? If the commercial is for a brand anniversary, corporate image film, or premium product launch, these flaws will directly undermine brand trust. In such cases, AIGC commercials are unsuitable for direct release; unless extensive manual post-production correction is planned, traditional filming or CGI workflows should be prioritized.
The evaluation criterion is to create a checklist of visual representations for core selling points. For example, itemize acceptable defect levels for product packaging logos, facial close-ups, mechanical precision, and environmental reflections. If over 30% of listed items require pixel-perfect accuracy, it is recommended to abandon direct AIGC release in favor of live-action filming combined with post-production compositing. Brands should prepare a communication objectives brief specifying whether the commercial aims for brand awareness, product demonstration, or sales conversion, as tolerance for visual imperfections varies significantly across different objectives.
The risk is that teams may be misled by AIGC's quick previews and overlook detail issues when zoomed in. It is recommended to schedule a magnification test during the kickoff meeting, projecting key AIGC-generated frames at 200% for all decision-makers to jointly approve. An exception applies if the ad is used only as small thumbnails in social media feeds with a run of less than one week, where pixel-level flaws have limited impact and cautious experimentation is acceptable. However, for outdoor screens, cinema pre-rolls, or website homepages, any flaw will be magnified, and traditional workflows must be followed.
During production, clearly define which assets must be filmed rather than generated.
AIGC commercials are not fully generated; many scenes require live-action footage as a foundation. Brands and production teams must jointly define the boundaries between generated and live-action content before shooting. For example, product packaging, real-world usage scenarios, human performances, and animated brand logos are prone to asset distortion or unnatural expressions if entirely AIGC-generated. In such cases, live-action should be scheduled to serve as base plates for AIGC post-processing, rather than generating final frames directly.
Specifically, create a shot list identifying footage that must be captured live, including multi-angle product close-ups, facial expressions, hand movements, and environmental cutaways. The list should also specify each shot’s purpose: direct editing or input for AIGC stylization. The production team must verify that the resolution, frame rate, and color space of live-action assets meet AIGC post-production workflow requirements, typically requiring higher resolution than the final output to allow processing headroom.
The criterion is that if a brand logo or product packaging occupies more than 10% of the frame and must remain clearly legible, it must be filmed. The risk is that teams may rely entirely on AIGC generation to cut costs, resulting in distorted brand assets. An exception applies when the product is virtual or a digital service without a physical form; in this case, full generation is permissible, provided official brand visual guidelines are supplied as reference. If live-action assets are missing, they cannot be remedied in post-production, potentially causing project delays or rework.
In post-production, identify the upper limit of repair costs for AIGC-generated content.
Post-production for AIGC commercials focuses heavily on repairing generated content. During acceptance, brands should assess the stability, consistency, and physical plausibility of generated elements. If character faces vary noticeably across shots or object motion violates physics, manual correction is required. Repair costs may exceed reshoot expenses; in such cases, the AIGC commercial is unsuitable for release and teams should evaluate regenerating or adopting alternative approaches.
The production team must compile a repair checklist, inspecting generated content frame by frame for anomalies such as continuity errors, flickering, distortion, and texture drift. Each anomaly should be tagged with repair difficulty and estimated hours. The brand should participate in prioritizing repairs, focusing first on issues affecting core messaging, such as product feature areas, eye lines, and subtitle readability. If repair hours exceed 30% of the total project budget, it is advisable to pause and reassess the approach.
The benchmark is whether the anomaly rate in generated content is below 1%. If the anomaly rate exceeds 3% and is concentrated in key frames, the piece is unsuitable for release. The risk is that post teams may overlook details to meet deadlines, leading to rejection during professional review. An exception applies to ads intended solely for internal sharing or testing, where a higher anomaly rate is acceptable. After repairs, the master must undergo color and audio recalibration; otherwise, visual or auditory inconsistencies may occur.
During acceptance, brand assets and legal compliance must be verified item by item.
Acceptance of AIGC commercials requires more than reviewing overall quality; brand assets and legal compliance must be verified item by item. Brands should prepare an asset checklist covering logo colors, typography standards, packaging design, likeness rights, music licensing, and font licenses. Before delivery, the production team must cross-check each item to ensure generated brand elements match official guidelines.
Specifically, establish an acceptance matrix across four dimensions: visual, audio, text, and legal. The visual dimension checks for logo distortion, color deviation, and product proportions; the audio dimension verifies background music licensing, voiceover authorization, and sound effect sources; the text dimension reviews subtitle typos, translation accuracy, and terminology consistency; the legal dimension confirms likeness rights, location permits, and copyright ownership of generated content. Set pass/fail criteria for each dimension; failure in any single item prevents publication.
The standard requires the brand to designate an acceptance lead and maintain written records. If real person likenesses appear in generated content, a signed release form is mandatory. Third-party music or assets require proof of licensing. A key risk is that AIGC output may inadvertently mimic existing copyrighted works, creating infringement liability. An exception applies if the commercial is strictly for internal training or non-public use, where legal risk is lower, though basic compliance must still be confirmed. Consequently, versions failing acceptance cannot enter the publishing workflow and must be reworked or regenerated.
The delivery phase confirms version control and source file ownership.
Delivering an AIGC commercial involves not just the final video but also source project files, generation parameters, style references, and asset libraries. During delivery, brands must define version control rules and source file ownership. If the commercial targets multiple platforms such as TV, social media, and outdoor screens, each requiring different resolutions and durations, a version matrix should be planned upfront to avoid quality loss from ad-hoc transcoding.
The production team must provide a version manifest detailing each version’s purpose, resolution, frame rate, codec, and file size. The brand must verify whether source files include editable project data, such as AIGC generation parameters, layer structures, and effect nodes. If source files are non-editable, future modifications will be impossible, making the AIGC commercial unsuitable for direct release unless the brand anticipates no further edits.
The standard dictates that source files must contain all original assets and generation parameters with clear naming conventions. A risk arises if the team delivers only the final video, preventing the brand from making adjustments. An exception applies to one-off campaigns where the brand confirms no edits are needed, allowing delivery of the final video alone. If source files are missing, any subsequent modification requires full regeneration at significant cost.
Applicable boundaries define which industries and scenarios should avoid AIGC.
AIGC commercials are not suitable for every industry or scenario. Before project initiation, brands must assess their industry attributes and communication contexts. In sectors heavily reliant on trust and precision, such as healthcare, finance, law, and aviation, AIGC-generated content may prompt user skepticism regarding authenticity. Such content is unsuitable for direct release unless clearly labeled as a concept demonstration or artistic creation.
Specific scenarios include product function demos, data presentations, safety guides, and customer testimonials, all of which demand credible visuals that AIGC may misleadingly generate. The standard holds that if content involves factual claims—such as performance specs, service workflows, or user reviews—live-action footage or data visualization is mandatory; AIGC generation is prohibited. The risk is that unrealistic visuals may trigger complaints or returns, damaging brand reputation.
An exception applies if the brand clearly labels the ad as "AI-generated" or "conceptual," and the content makes no specific factual claims. Even then, we recommend conducting audience research before launch to test viewer acceptance of generated content. If the platform removes the ad for misleading content, the brand bears all losses and risks damaging its reputation for future campaigns.
Recommendation: Establish an AIGC Suitability Assessment Form.
When deciding whether to use AIGC for ads, brands should create a suitability assessment form covering six dimensions: communication goals, content type, defect tolerance, budget, timeline, and legal risk. Set scoring criteria for each dimension; if the total score falls below 60, direct publication is not recommended. The brand's marketing lead and production team should complete the form jointly to ensure shared risk awareness.
The specific action is to hold a project review meeting with creative, technical, legal, and marketing teams. Discuss each item on the assessment form during the meeting and document the decision rationale. If scores are near the threshold, produce a 30-second test video for limited user testing before committing to full production. Test videos are low-cost but effectively reduce decision-making risks.
If any item on the assessment form triggers a red warning, mandatory re-evaluation is required. For example, abandon AIGC if the content involves factual claims or if legal risks cannot be mitigated. The risk is that brands may ignore assessment results in pursuit of innovation, leading to project failure. An exception applies if the brand has a dedicated AI content team with remediation and compliance capabilities, allowing for slightly relaxed standards. The completed assessment form serves as archived project documentation for future reference.
As a next step, brands should first clarify their communication goals and content types, then conduct preliminary discussions with the production team to confirm AIGC suitability for the current project. If suitable, proceed to the detailed project initiation process. If not, pivot promptly to traditional filming or CGI to avoid wasting resources. Only through restrained evaluation—without overstating AIGC applicability—can technology truly serve the brand.
If you are preparing an AIGC ad project, start by organizing your brief, visual references, product or corporate materials, delivery platforms, and copyright scope before reviewing theAIGC Video Services pageto translate abstract preferences into actionable production parameters.
