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AI tools help brand teams create more product-image variations, adapt backgrounds and scenes, enhance existing assets, and turn still images into short videos. Their clearest value is speeding up repetitive production and adaptation—not guaranteeing stronger sales or making every generated asset ready to publish.
What AI tools can do with product media
Most workflows begin with an existing product image. A team can remove or replace its background, place the item in a new scene, adjust the composition, or create additional versions for different channels. Some tools can also generate short motion assets from still images.
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Create or adapt product scenes
Google describes Product Studio as a way to create tailored product imagery, remove or change backgrounds, increase resolution, and generate video. Its example combines a product image with an inspiration image to create a new scene. Product Studio is documented as free through Merchant Center or the Google & YouTube Shopify app, but feature availability varies by region and product type. Google’s announcement and Merchant Center Help describe the workflow and its constraints.
Generate product-shot variations
Adobe Firefly’s product-shot workflow lets users prompt for the product, angle, lighting, and background, then refine the composition and export a PNG. Adobe lists ecommerce listings, social media, email, advertising, packaging mockups, presentations, and pitches as potential uses. These controls can help a team explore creative alternatives, but each result still needs a fidelity check. Adobe’s product-shot documentation explains the feature.
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Make motion from still images
Google says Product Studio can turn still product images into short videos or GIF-like social content. Do not assume the feature is available for every region, product category, or account; check Google’s current Product Studio documentation before building a workflow around it.
Where the efficiency claims come from
Published examples suggest AI-assisted production can reduce some campaign work, but the figures below are company case-study claims, not independent benchmarks or guaranteed outcomes.
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| Example | Reported result | What the source establishes |
|---|---|---|
| Newell Brands, 2025 | Elmer’s campaigns produced five times faster than previous productions | Adobe’s customer story attributes the result to Newell’s AI-assisted production workflow; it is a single company example, not a general performance estimate. Adobe/Newell Brands case study |
| Authentic Brands Group, year not stated on the page | 60% higher return on ad spend for an AI-enhanced Reebok campaign than regular product imagery | Google Cloud quotes the company’s chief digital officer. The page does not state the measurement period or show that the result generalizes. Google Cloud customer story |
These examples do not establish how much AI generally improves brand product media. Results will depend on the source assets, creative brief, review effort, audience, channel, and measurement method.
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Compare tools against the production job your team needs to complete rather than treating one product as a universal winner.
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- Starting material: Can the tool adapt a real product photo, generate from a prompt, or do both?
- Product fidelity: Can reviewers preserve labels, color, shape, proportions, and included accessories?
- Creative control: Can the team specify angle, framing, lighting, background, and style?
- Deliverables: Does it support still images, background removal, resolution enhancement, short video, or multiple outputs?
- Integration: Can the workflow connect to the product catalog, commerce feed, asset library, or approval system?
- Governance: Can the team track source files, rights, approval status, and permitted channels?
- Availability: Are the features available for the team’s geography, language, and product category?
- Cost and scale: Compare included features with paid plans, volume, review time, and integration effort; verify current terms directly with the provider.
Google Product Studio is positioned as a free Merchant Center workflow, while Adobe documents detailed product-shot controls alongside broader creative tools. Those are differences in workflow and feature emphasis, not proof that either option is best for every brand. See Google Merchant Center Help and Adobe Firefly’s product-shot page for their respective details.
How to review AI-generated product assets
Treat a generated scene as a proposal until it has been checked against the real item and the intended channel. Adobe Research identifies preserving an inserted object’s identity as a technical challenge: generative systems may change details while creating or adapting the surrounding scene. Adobe Research’s explanation describes why object fidelity matters.
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- Start from a clear source image. Use an accurate, well-lit image that shows the actual product and relevant packaging or accessories.
- Generate within a defined brief. Specify the desired setting and composition while avoiding changes to product characteristics that must remain exact.
- Compare the output with the real item. Check labels, text, color, shape, proportions, packaging, and included parts. Reject or correct outputs that imply a product feature or configuration that is not real.
- Review channel and brand requirements. Confirm the image meets the destination’s specifications and the brand’s visual standards before publishing.
- Keep provenance and approvals. Retain source assets and track rights, metadata, review status, and approved uses when content moves between teams.
Google says Product Studio is experimental and may produce unexpected images or videos; its guidance also describes unsupported imagery and categories, including regulated goods, as well as geographic differences. Check the current Merchant Center Help page for the relevant feature and category. Adobe says users are responsible for deciding whether partner-model output is appropriate for their work, so confirm the applicable model terms and rights rather than assuming broad legal clearance. Adobe’s documentation sets out that responsibility.
Why governance matters alongside generation
At scale, producing an image is only one part of the job. Teams also need to know which source file it came from, who has rights to it, whether it has been approved, and where it may be used. Adobe’s Best Buy case study describes a connected asset system using metadata, rights information, and approval states across teams. Best Buy’s Associate Director of Product Management said the creative team could use AI to get an asset “80% of the way there,” with designers refining the final details. That example illustrates a workflow with human review and distribution controls, not a claim that every team will reach the same result. Read the Best Buy case study.
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Newell Brands’ marketing leader likewise described using Firefly for repetitive work such as resizing and adapting content, while teams focus on creative ideas and brand storytelling. That is the company’s account of its workflow, not evidence that human creative review is no longer needed. Adobe’s Newell Brands case study provides the account.
Quick Recap
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