First, determine whether an AIGC commercial is worth entering your project workflow.
The most common mistake brands make during the project initiation phase is treating AIGC as a universal tool and asking the production team to generate visuals directly without first sorting out the requirements. AIGC commercials are suitable for scenarios that require rapid creative validation, highly conceptualized visual styles, or where live-action shooting costs are too high. If your product's selling points rely on authentic materials, real human expressions, or strict physical laws, then AIGC may not be the first choice. The criteria for judgment are simple: list three core pieces of information, namely the communication goal, the attention habits of the target audience, and the visualization difficulty of the product's selling points. If at least two of these three point to abstract concepts or fantasy scenarios, AIGC has value for project initiation. Conversely, if the selling points require precise demonstration of product size, weight, or operational feel, it is recommended to return to traditional live-action or CGI workflows.
Another preliminary judgment is the maturity of the internal decision-making chain. Visuals generated by AIGC often carry a stylized tendency, and if the brand lacks a unified expectation for the final visuals, a large amount of subjective disputes will arise during the review process. You need to prepare a visual reference board in advance, containing at least five AI-generated style images you approve of, as well as three style images you explicitly reject. This reference board must be handed over to the production team as the baseline for all subsequent review stages. Without this material, a manual review sequence cannot be established, because everyone's tolerance for AI visuals varies greatly.
The levels and rhythm of human review must be determined during the project initiation phase.
Human review is a management mechanism that must be designed from the very first day of project initiation. It is recommended to divide the review into three levels. The first level is the brand's internal decision-making committee, responsible for confirming whether the communication goals and core messages are accurate. The second level is the creative lead of the production team, responsible for technical feasibility and style consistency. The third level is the final decision-maker, usually an executive who can assume brand risk. Each review level must have clear inputs and outputs, and they should not be mixed into a single meeting.
It is recommended to set four checkpoints for the review rhythm based on project stages: script confirmation, storyboard confirmation, first-round generated sample, and final delivery. Leave a buffer of at least two working days between each checkpoint, as both AI generation and manual modifications require time. If the brand wishes to compress the timeline, they must accept either reducing the review levels or increasing the duration of a single review session; they must choose one and cannot compress both simultaneously. Otherwise, repeated revisions will occur later, ultimately extending the overall project duration.
During project initiation, it must also be clarified who has the authority to propose revisions. The biggest fear in AIGC projects is that everyone has a say, but no one is responsible for the final result. It is recommended to specify in the contract or statement of work that there is only one final decision-maker, and the opinions of other members serve as reference inputs and do not constitute mandatory revisions. This rule can prevent the project from falling into endless version iterations later on.
During the shooting phase, the focus of human review is on the raw footage rather than the final video.
Although many frames in AIGC commercials are generated by algorithms, shooting some live-action footage as a foundation is usually still required, such as product close-ups, character movements, or environmental B-roll. The quality of this live-action footage directly affects the AI synthesis results, so human review at this stage should focus on the raw footage, not the preliminary synthesized samples. You need to check whether the composition of each shot leaves room for AI generation, whether the lighting is even, whether the subject is clear, and whether the background is clean.
Specifically, the production team should provide a footage inventory on the day of the shoot or the following day, listing the timecode, content description, and shooting parameters for each shot. When the brand reviews the footage, the focus should be on three types of shots: first, shots highlighting the product's core selling points; second, shots of character expressions or interactions; and third, background shots that may be used for AI style transfer. If any of these three types of footage are blurry, overexposed, or poorly composed, they must be flagged on the spot and reshot; otherwise, AI generation will amplify these flaws later.
The risk is that AIGC's tolerance for flawed footage is not as high as advertised. A slightly shaky handheld shot might cause the AI to generate distorted object edges. Therefore, when reviewing footage, insist on viewing it in full-screen mode rather than just looking at thumbnails. Additionally, if there are actors on set, confirm whether their performance pacing is suitable for AI post-production; for example, rapid movements may lead to incoherence between generated frames. It is recommended to shoot a few extra slow or static backup shots during filming as insurance for post-production corrections.
In the post-production phase, the human review sequence should be broken down by visual elements.
Post-production review should not just look at the overall effect, but must check the visual elements layer by layer. The recommended sequence is to first check if the subject's shape is stable, then see if the edges are clear, then check lighting consistency, and finally evaluate style uniformity. This sequence moves from the most fundamental technical issues to the most subjective artistic ones, helping you quickly pinpoint where the problems lie.
Step one, main shapes. When playing the sample video, pause at the frame with the largest movement amplitude to check if the product, characters, or main objects are deformed, distorted, or flickering. If they are, it indicates that the AI generation model lacks sufficient constraints on moving objects, requiring adjustments to generation parameters or the addition of keyframe constraints. Step two, edge details. Zoom in the image to 150% to check for jagged edges, fringing, or semi-transparent artifacts on object edges. This usually occurs on hair, fabrics, or complex geometric shapes. Step three, lighting and shadow consistency. Observe whether the shadow direction, brightness, and color temperature of different objects in the same scene are unified. AI generation often produces inconsistent light sources, making the image look like a collage. Step four, style uniformity. Compare the first and last frames side by side to confirm there are no obvious jumps in overall color tone, texture, and grain.
Every review step must record specific issues rather than vaguely stating that something feels wrong. It is recommended that the production team provide an issue log containing timecodes, issue descriptions, severity levels, and suggested revisions. When the brand reviews the video, they should only confirm items line by line from the log and not introduce new ideas outside of it. This prevents the scope of post-production revisions from spiraling out of control. If a specific issue repeatedly appears across three consecutive versions, adjustments must be made at the generation parameter level rather than continuing to patch it during the compositing stage.
Audio and subtitle reviews must not be left until the end.
Audio and subtitles in AIGC commercials are often overlooked and rushed right before delivery. However, audio and subtitle reviews should run parallel to video reviews, intervening at least two versions in advance. Because AI-generated visuals may not match the pacing of the voiceover or music, modifying the audio at the final version is extremely costly.
Audio reviews must check three dimensions: whether the voiceover lip movements sync with the character's actions on screen, whether the background music's mood matches the visual atmosphere, and whether sound effects have clipping or are missing. Pay special attention to AI-generated scenes of people speaking, as lip-sync is a common issue; if the lip deviation exceeds two frames, the audience will clearly notice. Subtitle reviews must check whether text placement obscures key visuals, whether font sizes are suitable for different playback platforms, and whether the timing of subtitle appearance and disappearance aligns with the vocal rhythm.
It is recommended to schedule voiceover and music pre-mixing immediately after the initial video sample is approved. Then, during the next review, play the video and audio simultaneously rather than reviewing them separately. If audio-visual desynchronization is found, prioritize adjusting the audio track, as video modifications are more costly. Additionally, subtitle files should be exported separately and not burned directly into the video, facilitating later revisions and adaptation for different platforms.
The acceptance checklist must verify each deliverable item by item.
Final delivery does not end with simply watching the finished video once. You need an acceptance checklist to verify each deliverable item by item. The checklist must include at least the final video file (with resolution and encoding meeting publishing platform requirements), the master file (a clean version without subtitles or effects), the subtitle file (in SRT or ASS format), the source project file (for future modifications), and a production documentation file (recording generation parameters, revision logs, and version history).
Each deliverable must have clear acceptance criteria. The final video file must be checked for smooth playback without stuttering or screen tearing. The master file must be confirmed to have no residual subtitles or watermarks. The subtitle file must be checked for timeline accuracy and text spelling. The source project file must be ensured to open properly and contain all assets and compositing nodes. The production documentation must allow others to understand the generation method and revision logic for each shot; otherwise, future handovers will be very difficult.
Copyright issues must also be noted during acceptance. AIGC-generated content may involve copyrighted elements from training data, so the brand must confirm that the production team has provided traceable generation models and asset source declarations. If they cannot provide these, it is recommended to refuse acceptance. Additionally, confirm whether the delivered version includes commercial usage rights and whether it is permitted for multi-platform distribution. These must be explicitly stated in the contract and cannot be verbal promises.
Identify in advance when AIGC commercials are not suitable.
Not all brand projects are suitable for AIGC commercials. It is recommended not to use them in the following situations: the product needs to demonstrate real usage effects and users rely on touch or smell, the brand image highly depends on real-life celebrities or specific historical figures, or the project timeline is extremely short with no time reserved for revisions. In these cases, AIGC may bring more risks than efficiency.
Additionally, if the brand lacks internal consensus on the visual style, or if the decision-making chain involves more than three people, AIGC projects can easily fall into endless revisions. Because AI-generated results are random, different people may prefer different versions, making it impossible to reach a consensus. In this case, it is recommended to first create a short style test video, spend a week verifying internal acceptance, and then decide whether to proceed. If the test video fails to pass, decisively switch back to the traditional production process.
Also note that AIGC commercials are not suitable for scenarios requiring strict data accuracy, such as medical devices, financial products, or the display of legal terms. AI-generated visuals may omit details or produce misleading imagery, which is fatal in industries with high compliance risks. If the brand belongs to these industries, it is recommended to use AIGC only for concept demonstrations, not for official releases.
The next step is recommended to start with a minimum viable sample video.
If you are still hesitating about whether to adopt AIGC commercials, the safest approach is to first create a thirty-second minimum viable sample video. Select a core selling point of the product, use AI to generate three versions in different styles, and pair them with simple voiceovers and subtitles. Then organize an internal review to observe the reaction of the decision-making chain. This sample video does not need to be perfect, but it can expose communication issues and technical bottlenecks in the process. Based on the feedback from the sample video, decide whether to expand the project scope. This approach requires low investment and carries low risk, while also allowing the team to accumulate AIGC review experience.
If you are preparing an AIGC commercial project, you can first organize the brief, reference visuals, product or company materials, delivery platforms, and copyright scope, and then view theAIGC video services pageto ground the communication from abstract preferences to executable production boundaries.
