AI video generation enterprise marketing practical guide: costs, scenarios and implementation

AI video generation has reached a stage where it can be commercially used

If AI video generation in 2024 is still “stunning in demonstrations and embarrassing in implementation,” then by 2025, it has become a clear business in corporate marketing scenarios. For a 15-second product short video, the traditional outsourcing process is: 1 day for scripting, 1 day for shooting, 2 days for editing, and 2 days for editing. The quoted price is 3,000-20,000 yuan. With AI video generation, the script takes 10 minutes, the generation takes 10 minutes, and the editing takes 1 hour. The cost of a single video can be reduced to dozens of yuan.

The more critical change is not that it is cheap, but that it can be batched, tested, and localized. The marketing team can generate 20 versions of materials at one time for A/B testing, and can change the same video into 8 languages ​​and put it in different markets, which is almost impossible under the traditional production model.

It should be noted that the most mature AI video generation currently is not “generating a promotional video from scratch”, but the four tasks of converting images and text to video, digital voice broadcast, material re-editing, and multi-language dubbing. If you put your expectations in the right place, your landing success rate will be much higher. Relevant capabilities can also be connected with the AI marketing automation system to form a closed loop from material production to delivery optimization.

Traditional video production vs AI video generation

Four capabilities forms of AI video generation

1. Vincent Video and Tusheng Video

Use text descriptions or a product picture to generate dynamic video clips, suitable for atmospheric shots, product rotation displays, and scene transitions. Currently, mainstream models can generate 5-10 second clips at a time, and can complete 30-60 seconds of video through multi-clip splicing. In China, you can refer to the video generation capabilities of platforms such as Tencent Hunyuan Model.

2. Digital voice broadcast

Enter the copy, and the digital person will automatically announce it, aligning the mouth shape with the voice. It is suitable for “people speaking” content such as company introductions, product explanations, knowledge popularization, and internal announcements. Once modeling can be reused for a long time, the marginal cost is close to zero. For complete selection and cost analysis of digital humans, please see AI Digital Human Enterprise Application Guide.

3. Intelligent re-cutting of long videos

Automatically cut a live broadcast, a training, and an interview into dozens of short videos, automatically identify highlight clips, and generate titles and subtitles. This is the usage with the highest input-output ratio at present. It requires almost no creative ability and wins purely by efficiency.

4. Multi-language localization

Recognize the speech of the existing video into text, translate it, synthesize the dubbing in the target language and match the mouth shape. When overseas companies produce multi-language content, this process can reduce the cost to one-tenth of traditional dubbing.

Six landing scenes

1. Short video matrix operation

One account team manages 10-30 matrix numbers and relies on AI to generate differentiated materials in batches. The core is “same product, different angles of entry, different opening hooks”, trading quantity for the probability of hits.

2. E-commerce main image video and detail page video

The product image directly generates a dynamic display video, which is used for the second frame of the main image, the home screen of the details page, and information flow delivery. For merchants with many SKUs, AI is the only feasible batch solution.

3. Product demonstration and function explanation

Use a digital human to record the screen and add subtitles, and turn the product operation process into a standard demonstration film. After product iteration, you only need to change the copy and regenerate it, and there is no need to re-appoint the shooting.

4. Corporate training courseware video

Turn internal SOPs, systems, and operating specifications into short video courseware, and use them together with the training system in AI human resources intelligence. The coverage and update speed will be significantly improved.

5. Multi-language overseas content

The same product video is generated in English, Spanish, Arabic, and Japanese versions for use on overseas social media and independent websites. This is the most direct cost reduction method for cross-border teams.

6. Live slicing and secondary distribution

After the live broadcast ends, it will be automatically sliced, subtitled, and titled, and distributed to short video platforms and private domains. A 3-hour live broadcast usually produces 20-40 usable slices.

Five-step method for enterprise implementation of AI video

Step 1: Clarify content type boundaries

First, distinguish which content is suitable for AI (spoken broadcast, product display, material re-editing, multi-language) and which content must be shot in real life (brand image film, real factory scenes, emotional expression of characters). Forcing unsuitable things onto AI is the most common reason for failure.

Step 2: Create a database of materials and prompts

Precipitate product pictures, scene pictures, brand colors, font specifications, and proven and effective prompt word templates. The prompt vocabulary is the real asset of the AI ​​video team and is more important than model selection.

Step 3: Run through the production line in small batches

Select an account or SKU, run through the complete link of “topic selection – script – generation – editing – publishing” and record the time and cost of each video. The goal is to create a replicable standard operating procedure, not to create a stunning sample.

Step 4: Access delivery and data reflow

Connect the video with the delivery platform and data dashboard, and use actual conversion data to reversely filter the direction of the material. AI can generate unlimited materials, but only data can tell you which materials are worthy of mass production.

Step 5: Establish an audit and compliance mechanism

Establish a review process for AI-generated content: fact checking, checking of banned words in advertising laws, portrait and copyright confirmation, and AI-generated logos. The most common problem in mass production is not quality, but compliance.

Real implementation cases

Case 1: Home Brand Short Video Matrix

A certain home furnishing brand needs to continuously produce short videos for 600 SKUs. The original outsourcing was to produce 40 videos per month at a cost of about 120,000 yuan. After switching to AI generation and manual fine-cutting, the monthly output stabilized at 350 items, the comprehensive cost of a single item dropped to about 180 yuan, the total play volume of the account matrix increased by 4.6 times in three months, and the conversion rate of the details page brought by the main image video increased by 19%.

Case 2: Multi-language product video of an equipment manufacturing company

An equipment manufacturing company targets markets in Southeast Asia, the Middle East, and South America. Originally, each language had to be dubbed locally and re-edited. After switching to AI multi-language localization, one master video can be produced in 6 language versions, the delivery cycle is shortened from 3 weeks to 2 days, and the annual content localization cost is reduced by about 70%.

FAQ

Can the video quality generated by AI meet commercial standards?

Look at the scenes. The oral broadcast, product display, material re-editing, and multi-language dubbing have reached commercial standards; there are still obvious flaws in complex character movements, fine hand close-ups, and long-shot narrative. It is recommended to use AI for “information transmission” content and leave real shots to “brand emotion” content.

Will there be any copyright risks with content generated using AI?

Three aspects need to be confirmed: the compliance of the training data source, the similarity between the generated results and existing works, and the authorization of the character’s portrait. Before commercial use, it is recommended to conduct a similarity search and mark the AI-generated content according to platform requirements.

How long does a video take?

Digital live broadcasts can be completed in 10-30 minutes; graphic videos plus editing can take about 1-2 hours; films that require the splicing of multiple clips over 30 seconds usually take half a day to a day. A skilled team can produce three to five times more than a novice team.

What kind of team is needed?

You can start with two to three people: one person is responsible for topic selection and script, one person is responsible for generation and editing, and one person is responsible for placement and data. Professional photography and post-production are not required, but people who understand platform algorithms and user psychology are needed.

Will AI video replace video production companies?

It will replace part of the demand for standardization and batch production, but brand films, creative films, and complex real-life shooting still require professional teams. A more realistic relationship is: Enterprises use AI to undertake daily content production capacity and focus their budgets on a small number of high-quality brand content.
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