Artificial Intelligence is transforming product photography into a scalable, precision-driven process. By automating background removal, optimising lighting and colour, and enabling advanced scene generation, AI empowers brands to deliver high-quality visuals faster and more efficiently. This evolution supports strategic objectives such as cost reduction, accelerated go-to-market timelines, and audience personalisation.
The use of AI-generated imagery must be carefully managed to avoid reputational risks and loss of consumer trust.
Editing
AI solutions now handle core image refinement processes previously performed manually. These include background isolation, exposure correction, and visual enhancement. The automation of these tasks reduces production timelines and ensures consistent compliance with commercial imagery standards.
Background
Using deep learning models trained on large datasets, AI can identify and separate foreground subjects from complex backgrounds with high accuracy. This capability enables businesses to rapidly prepare marketplace-ready product images. Background automation supports uniformity across catalogues and meets the specific requirements of platforms demanding white or neutral backgrounds.
Notable tools in this domain include:
- Photoroom: Offers instant background removal and replacement, streamlining the editing process for e-commerce images.
- Spyne: Provides AI-driven background removal tailored for product photography, enhancing image quality for online listings.
- Pixyer: Specialises in generating high-quality, realistic product images with AI-powered background editing.
Correction
AI-powered tools perform dynamic exposure adjustment, colour temperature balancing, and artifact removal. These features ensure that images reflect accurate product characteristics and enhance perceived quality. Algorithms account for natural lighting inconsistencies and apply uniform grading, minimising the need for manual intervention.
For instance, Fotographer.ai utilises advanced image generation and editing AI to enhance lighting and colour, ensuring professional-grade product visuals.
Operational
Implementing AI-based editing tools results in substantial cost savings, reported by some industry vendors to be as high as 80%. Design teams benefit from increased throughput, enabling strategic reallocation of resources toward creative planning and campaign development.
Scenes
Scene generation capabilities use AI to place products in custom-designed environments without requiring physical photoshoots. This allows brands to generate seasonal, thematic, or lifestyle images cost-effectively.
Composition
Text-to-image and template-based AI models enable product placement in digitally rendered settings. For example, a home decor item can be shown in a stylised living room, or outdoor gear can appear on a trail, all synthesised from a base product image and user prompts.
Tools facilitating this include:
- Packify.ai: Generates AI backgrounds for product photos, enhancing visual appeal without the need for physical staging.
- SellerPic.ai: Offers lifestyle scene generation, integrating products seamlessly into various environments.
Uniformity
Consistent application of brand guidelines is achievable through AI tools that standardise style elements, colour schemes, lighting, and framing across entire catalogues. This level of uniformity reinforces brand identity while reducing design complexity.
Flair.ai, for example, allows the creation of reusable templates, ensuring brand consistency across all product images.
Benefits
AI-generated visuals eliminate the need for traditional set construction, location logistics, and seasonal reshoots. Marketing teams can produce content for localised campaigns or global launches with rapid turnaround, maintaining quality and thematic relevance.
The overuse of synthetic settings without anchoring them in real-world imagery may lead to authenticity concerns and undermine brand perception.
Generative
Generative AI platforms create images entirely from text descriptions or concept inputs. These tools support product development cycles, visual prototyping, and expanded catalogue visualisation.
Conceptual
Marketing teams use generative AI to visualise product concepts in aspirational settings before physical prototypes exist. This capability supports pre-launch testing, content marketing, and social media engagement.
CreatorKit stands out by generating realistic product images, reducing the time and cost associated with traditional photoshoots.
Training
Custom-trained AI models based on proprietary product imagery enhance fidelity. Brands fine-tune open-source models to capture specific textures, finishes, and design features. This ensures outputs align with product specifications and brand aesthetics.
Compliance
While generative tools offer flexibility, they currently struggle with replicating proprietary elements such as logos and intricate textures. As such, they are more suited for conceptual content rather than official product listings. It is advisable to use these outputs alongside manual verification protocols. Over-reliance on synthetic imagery without validation against physical visuals can diminish credibility and reduce long-term campaign effectiveness.
Image disclosure protocols should be implemented to distinguish AI-generated content in high-risk categories or regulated markets.
Models
AI-generated human models and composite photography allow brands to display products in use without traditional modelling sessions. These synthetic visuals promote inclusivity and expand creative options at reduced cost.
Synthetic
By generating models of varied body types, ages, and ethnicities, AI improves diversity in representation. This capability enhances consumer relatability while reducing the logistical complexity and cost of model casting.
Platforms like Photoroom and Botika offer tools to create AI-generated fashion models, enabling brands to showcase apparel on diverse virtual models.
Scene
AI tools can insert products into images featuring human interaction, such as apparel on a walking model or kitchenware in use. These composites support storytelling and demonstrate functional use, strengthening emotional resonance.
Justification
Marketers benefit from greater agility in testing visual strategies. The low marginal cost of producing additional AI-based variations enables broader experimentation and faster adaptation based on consumer feedback or performance data.
Campaigns built exclusively on virtual models may lack the authenticity required for high-trust consumer segments, particularly in premium categories.
Optimisation
AI extends beyond creation into the strategic deployment of visual assets. By analysing performance data, it can optimise images for different user profiles, devices, and marketing channels.
Performance
AI systems conduct real-time A/B testing and identify high-performing visuals based on metrics like conversion rate and engagement. This adaptive presentation improves content relevance and maximises ROI.
SEO
Computer vision algorithms auto-generate metadata, including descriptive alt-text and keyword-rich tags. These improvements enhance image indexing, search performance, and category filtering, particularly for retailers managing large inventories.
Personalise
Emerging capabilities in generative AI allow dynamic image generation tailored to individual users. This includes visual variations based on browsing behaviour, demographic signals, or seasonal relevance, aligning imagery with personal preferences to improve engagement.
Statistics
The integration of AI into product photography is supported by notable industry trends:
- The AI image generation market was estimated at $299.2 million in 2023 and is expected to reach $917.4 million by 2024, increasing at a CAGR of 17.4%.
- Approximately 80% of millennial consumers prefer to purchase products that have professionally taken photographs, highlighting the importance of high-quality visuals in e-commerce.
- The global photography equipment market is projected to reach $15 billion by 2025, indicating sustained investment in visual content creation tools.
AI in product photography delivers measurable business value by enhancing image quality, improving operational efficiency, and enabling scalable personalisation. However, to safeguard brand integrity, organisations must integrate AI visuals with authentic product imagery, monitor for over-reliance on synthetic content, and deploy verification workflows. A phased adoption strategy, aligned with regulatory standards and brand governance, ensures performance gains and reputational resilience.
AI will not make traditional product photography obsolete. Instead, it complements human creativity by automating repetitive tasks and enabling rapid concept visualisation. Photographers will continue to play a crucial role in crafting authentic brand narratives, capturing proprietary detail, and ensuring aesthetic fidelity, especially in campaigns requiring emotional storytelling or regulatory compliance.
While AI can handle routine edits and layout generation, it does not replace the strategic thinking and creative direction offered by professional designers and visual artists. These roles evolve into curatorial and supervisory functions, guiding AI outputs to align with brand identity, audience expectations, and campaign objectives. Skills in art direction, branding, and human-centred design remain critical.
Yes, but with caveats. AI-generated images are suitable for conceptual marketing, A/B testing, and campaign previews. However, for official listings, especially on regulated marketplaces, product images must accurately represent the item. Brands should validate AI outputs against physical samples to avoid misrepresentation and non-compliance.
The key is to blend AI-enhanced visuals with genuine product photography. Use AI for scalability and testing, but ground hero visuals and key listings in real imagery. Establish verification workflows and implement internal guidelines for ethical image use to protect brand integrity.
AI introduces greater speed, flexibility, and data-driven decision-making in visual content creation. Teams will shift from execution to strategy, focusing on narrative development, creative testing, and audience personalisation. Investments in AI literacy, tool integration, and brand governance will define success in the evolving visual production landscape.
Author
Vincent VuVincent is the founder and director of Rubix Studios, with over 20 years of experience in branding, marketing, film, photography, and web development. He is a certified partner with industry leaders including Google, Microsoft, AWS, and HubSpot. Vincent also serves as a member of the Maribyrnong City Council Business and Innovation Board and is undertaking an Executive MBA at RMIT University.