By Enrico Fantaguzzi – Co-founder Digital Fashion Academy

Reading time:

3–5 minutes

Key topics: AI image creation, fashion videos, oragnization and management

Today, AI solutions are transforming this heavy cost center into an agile, highly creative powerhouse, completely bypassing traditional logistical hurdles.

Elevating customer experience at accessible cost

Virtual Try-On: Elevating Customer Experience and Sustainability

One of the most impactful consumer-facing applications of AI in fashion is the “virtual try-on”. Startups like the Milan-based Dual Me have developed innovative tools that can be easily integrated into fashion e-commerce platforms, allowing users to upload a personal photo and see exactly how a garment will fit on them before making a purchase.

By enabling consumers to see how colors and fits suit them personally, brands are significantly improving their e-commerce conversion rates. Furthermore, this technology aims at lowering return rates, which not only mitigates heavy financial burdens for brands but also severely reduces the negative environmental impacts associated with reverse shipping.

Unprecedented speed and operational efficiency in image creation.

Photo Studio Quality in Seconds with AI

For fashion professionals, AI offers unprecedented speed and operational efficiency. Through smart, user-friendly dashboards, brand managers can create high-end campaign imagery with just a few clicks.

The workflow is remarkably simple:

1. Users upload standard “still-life” images of garments or accessories, frequently utilizing existing shots from their B2B virtual showrooms.
2. They then select a model from a preset roster of diverse virtual models, or upload photographs of their own contracted human models.
3. Finally, by typing a brief text prompt, they instruct the AI on the desired camera angle, background location, and creative direction.

The AI instantly merges these elements, delivering cohesive, professional images that rival traditional, high-end photoshoots. Beyond static images, these AI platforms can also generate short, highly consistent video clips starting from just five seconds in length, which are perfect for social media reels and e-commerce product pages. Because producing real video content is traditionally even more expensive than photography, AI video generation offers massive cost savings for the industry.

How much does AI photography cost?

Hybrid Models and Creative Flexibility

While many brands opt for a self-service software-as-a-service (SaaS) model, which can start at highly accessible prices like €19.99 per month, others prefer a hybrid or delegated approach.

Traditional product photography studios like Squareshot, which operates a fully equipped studio in Brooklyn, now offer AI-powered visual production alongside their physical model shoots and flat-lay photography services.

Squareshot allows brands to experiment with AI visuals based on their product shots or references, offering AI campaigns starting at $95 per image or $975 for a 5-image set.

Alternatively, AI providers can take a brand’s creative brief, still-life product photos, and brand guidelines to fully manage the generation of an entire customized package of images and videos, saving the brand immense time.

Crucially, these AI workflows offer immense versatility. Professionals can seamlessly adapt the same generated image to multiple formats and styles, ranging from a clean standard e-commerce look to a highly creative editorial style, tailoring the dimensions perfectly for a website homepage, a LinkedIn update, or an Instagram post, all within a single dashboard without external software.

Maintaining model consistency across these diverse assets is also becoming easier, with advanced methods like “Face Reference” ensuring the exact same model identity is kept across all visuals and poses.

Is AI photography law compliant?

What are the risks

Navigating AI Compliance As brands integrate these powerful tools, they must also navigate the evolving legal landscape, particularly in Europe. The EU Artificial Intelligence Act is the world’s first comprehensive regulation on AI by a major regulator, and it is already setting a global standard. The Act categorizes AI applications into three risk levels:

1. unacceptable risk (which is banned),
2. high-risk (subject to strict legal requirements), and
3. low risk for largely unregulated applications.

For fashion businesses adopting AI, understanding these legal obligations is critical. Small and medium-sized enterprises (SMEs) and startups can utilize tools like the online AI Act Compliance Checker to quickly discover how the regulation affects them. Furthermore, developers providing the General-Purpose AI (GPAI) models that power these fashion image generators must adhere to specific guidelines and a Code of Practice to ensure compliance and transparency under Chapter V of the Act.

Ultimately, the integration of AI in fashion photography is not a passing trend. It is a fundamental evolution that optimizes daily operational workflows, slashes production costs, and creates a more engaging, sustainable, and personalized retail experience for the modern consumer.

Photoshooting AI solutions

FAQs

Author: Enrico Fantaguzzi
Co-founder and Director
Digital Fashion Academy

Former Gucci, Yoox, Tod’s
PMP(r), Prince2

From Boutique to Algorithm: How AI is Redefining the Role of the Fashion Sales Assistant

In the age of AI, fashion eCommerce is finally catching up with the personalized attention shoppers once received in physical stores. At the heart of this transformation is Dressipi, now part of the MAP Digital, an AI-powered product data enrichment platform that helps fashion brands deliver personalised shopping experiences at scale.

Speaking during a Digital Fashion Academy session, Dressipi co-founder Sarah McVittie described how the platform was inspired by the intuitive skill of a brilliant shop assistant. “I once walked into a boutique in New York and a stylist picked out five outfits for me—all perfect. I bought them all. That kind of intuition is what we’ve worked to replicate using AI.”

I once walked into a boutique in New York and a stylist picked out five outfits for me—all perfect. I bought them all. That kind of intuition is what we’ve worked to replicate using AI.

Sarah McVittie

Decoding Style with Data

Initially a B2C project, Dressipi shifted to B2B after major UK retailers expressed interest in licensing its taxonomy system. Today, Dressipi enhances product feeds with a deep layer of styling, contextual, and dynamic data—including neckline types, fabric flows, and trend alignment—based on thousands of attributes.

This data doesn’t just optimize websites for conversion. It helps retailers understand what drives customer preferences, return rates, and even emotional resonance. For example, women with larger busts are more likely to return a crew-neck top than a scoop neck, these details impact algorithmic product recommendations.

Real-World Impact: The House of Bruar

John Hodge from The House of Bruar, a Scottish luxury retailer, shared how integrating Dressipi’s technology improved their recommendations. “We tested Dressipi against our existing engine and saw a 5% uplift in incremental value,” he said. “Outfits now reflect our brand DNA and are personalized to the customer’s preferences and context.”

Their team also benefited operationally—reducing manual effort for product tagging and visual merchandising while improving consistency across imagery and attributes.

“Outfits now reflect our brand DNA and are personalised to the customer’s preferences and context.”

John Hodge

Returns, SEO, and the Future of Discovery

Beyond styling, Dressipi tackles a critical challenge in fashion eCommerce: returns. By identifying patterns, such as whether returns are due to size, style, or stock fragmentation—the platform optimises for kept items, not just purchases.

Moreover, as search evolves to prioritize natural language (think “What should I wear to a summer wedding in Edinburgh?”), enriched product data becomes crucial. Dressipi supports brands in creating semantic layers for SEO, marketplaces, and even AI chat interfaces, bridging brand language with customer language.

The evolution of Customer Journey with AI

Drawing on the discussion with Sarah and John, the introduction of AI like Dressipi is significantly changing the customer journey in online fashion retail, aiming to replicate and enhance the personalised experience previously only available in physical stores.

Here are some key ways the customer journey is evolving:

Natural language search

Shift to Natural Language Interaction: The way customers search is changing from traditional keyword-based queries (like “black dress wedding”) to more natural language phrases (such as “looking for a pencil skirt for work that’s smart but a bit glamorous”). AI helps retailers optimise their product data and metadata to be discoverable through these natural language searches.

Highly personalised recommendations

Generic recommendations like “customers like you bought X” are ineffective in fashion due to variations in size, shape, height, weight, gender, and changing preferences. AI uses detailed product data (physical attributes, context, dynamic trends) and understands how specific features look or feel good on different people. This enables the system to create tailored outfit and product suggestions based on the customer’s taste, lifestyle, colour palette, and even physical characteristics, showing them their “best possible shop window”. The system learns from customer interactions over time.

Reduce ecommerce returns

Improved Fit and Reduced Returns: Understanding factors that contribute to returns, such as sizing issues (15-20% of returns) and style suitability (30-50% of returns), is crucial11. By incorporating fit data and understanding how attributes (like necklines) impact suitability for different body types, AI can help predict return likelihood2…. Algorithms can be optimised for “kept items” rather than just conversion rate, meaning customers are shown products they are more likely to keep12. This leads to a more positive post-purchase experience and reduces the frustration associated with returns.

Increase engagement

Style Guidance and Outfit Inspiration: Customers often need guidance on how to put items together. AI creates on-brand outfit suggestions that help customers visualise how to wear products for different occasions (e.g., work, evening, weekend). This helps customers see the versatility of items and encourages investment. The system understands what occasions are applicable for a specific product and suggests items that go well together8.

Consistent and Relevant Browsing: AI ensures that recommendations and product listings use consistent imagery, avoiding a jumbled look1314. Crucially, it takes into account current stock levels, including specific sizes, so customers are only shown products that are actually available to them. The rich, consistent data also allows for more effective and consistent filters and facets on the website, improving the browsing experience.

Marketing analysis

Deeper Customer Understanding and Segmentation: The detailed data generated by AI allows retailers to move beyond basic demographics and create powerful customer segments based on behaviour, preferences, and lifestyle (e.g., equestrian customer, pro farmer). This understanding enables the delivery of experiences and language tailored to these specific segments, making the customer feel understood and increasing engagement.

Integrated Brand Experience: The AI works closely with the brand’s creative team to absorb and reflect the brand’s DNA and styling principles. This ensures that the personalised recommendations and guidance remain true to the brand’s identity and help build resonant brand experiences online. Tailored landing pages can also be created based on customer intent and context.

Final Takeaway

Fashion retailers are realising that data is not just about optimization—it’s about storytelling, styling, and service. As AI reshapes the fashion landscape, tools like Dressipi are making it possible to scale the art of the sales assistant and build genuinely personal digital shopping experiences.

In essence, AI like Dressipi moves the online customer journey from a potentially overwhelming, static catalogue experience to a dynamic, personalised, and guided interaction that better reflects the nuances of fashion and aims to build a stronger connection with the customer

Watch the webinar