Jeff Strauss, Head of Imaging at Photoroom, provides key insights into the evolution of product imaging and its impact on the content production and commercial value of major retail brands.
Across North America, the retail sector is entering a new phase of competition as marketplaces, retail media networks, and paid social media become more automated, meaning the advantage is no longer defined by access to better tools or more advanced artificial intelligence (AI); most retailers are now operating with broadly similar capabilities across bidding, targeting, and optimization.
This is shifting the point of difference into the assets these systems rely on, where product imagery, listing quality, and the ability to adapt creatively and quickly are becoming decisive factors in performance.
Therefore, the constraint is no longer how efficiently retailers can spend, but how quickly and consistently they can produce content that converts.
In my experience, the biggest industry shifts are not driven by access to technology, but how businesses adapt their operating models around it.
This is not a new dynamic, as previous waves of change in retail imaging were not won by those who simply adopted new tools first, but those who reworked how they operated around them, and the same pattern is now playing out again at a much faster pace, where once a gap opens, it compounds quickly.
For many retailers, creative production has not evolved at the same pace as performance marketing. While media infrastructure has become highly automated, content production often remains manual, fragmented, and slow. This creates a disconnect where advanced distribution systems are limited by inconsistent or delayed inputs.
At enterprise scale, the challenge has never been creating images, but maintaining consistency, speed, and control across thousands of products moving through the system. From experience, this is where things start to break. Small inefficiencies do not stay small for long; they compound across categories, campaigns, and markets.
For example, Wolt, operating across more than 30 countries, processes images with fewer than 100 defects per 100,000 products monthly, a standard only achievable when production is embedded into the workflow from the start.
Delays or inconsistencies can prevent listings from going live on time, directly impacting revenue and campaign performance. In an environment where timeliness and visibility drive conversion, production is no longer a back-end function; it is directly tied to commercial outcomes.
The conversation around AI in retail often focuses on the capability of the models themselves; however, in practice, most retailers are working with broadly similar tools. The difference is not what technology they have access to, but how that technology is used.
In many cases, what appears to be a limitation of AI is actually a limitation of infrastructure: fragmented workflows prevent organizations from realizing the value of the tools they already have. AI only delivers value when it is embedded into how production actually happens, rather than applied as a layer on top of existing processes.
Competitive advantage now comes from integration – embedding AI into workflows, standardizing production, and creating systems that can deliver consistent output at scale.
Many organizations are still in transition. Photoroom Intelligence, which draws on analysis across more than seven billion images processed annually, finds that 87 percent of shoppers say product visuals are the most important factor in a purchase decision, yet 64 percent of retailers remain in experimentation or pilot phases with AI. The gap between consumers and production infrastructure represents the single-largest performance opportunity in retail right now.
The cost of producing product imaging has fallen significantly, but this is no longer the primary constraint. The greater commercial risk sits in delay. Retail operates in defined windows of demand, driven by seasonality, promotions, or external factors. If imaging is not ready when those windows open, the opportunity is missed.
From an operational perspective, this is where the real cost shows up; it’s not just what is spent producing an image, but what is lost when production cannot keep up with demand.
This is already being reflected in the market. Retailers such as Decathlon have reduced production timelines while improving consistency by embedding structured image workflows, allowing products to go live faster and respond more effectively to demand.
GoodBuy Gear, a recommerce platform, recorded a 23 percent uplift in conversion after making the same shift. In these environments, 51 percent of shoppers say they would switch to another marketplace if product information and visuals are clearer and more reliable elsewhere.
Retail still operates on a simple principle: customers buy with their eyes. The goal was never a beautiful image – it was an image that sells. The quality of what a customer sees shapes trust, selection, and performance. Poor imaging does not just reduce conversion; it increases returns, which remains one of the fastest ways to erode margin in e-commerce. Speed is no longer just an operational metric – it is a commercial one.
As content volume increases, consistency becomes critical. Customers are comparing products across multiple sellers and platforms in seconds and, within that environment, inconsistency in imaging quickly signals risk and reduces trust.
Effective product imaging follows three fundamentals: fidelity, realism, and consistency. Products must be represented accurately, placed naturally within their environment, and presented in a way that aligns across listings. When those conditions are met, conversion improves; when they are not, hesitation increases, and return rates often follow.
From my experience, this is where many systems fail – not in producing images, but in maintaining standards across volume. AI does not remove that requirement; it amplifies it.
When integrated properly, production shifts from a one-to-many system, where a single input can generate multiple outputs, variations, and formats without adding complexity or decreasing speed. This is what allows retailers to scale without losing control.
Product visuals can no longer be treated as a downstream creative task; they are not simply outputs. Images are part of the infrastructure that underpins how retail operates. This shift also changes the role of merchandising teams, moving focus away from repetitive production and toward setting standards, direction, and quality at scale. This is what visual commerce infrastructure means in practice, not a tool applied to images, but a system embedded into how retail operates.
Platforms such as Photoroom are increasingly being used for this reason, embedding directly into retail workflows to remove production friction and enable consistent output across channels. The broader trend extends beyond any single provider. It reflects a move toward treating content production as a core system rather than a supporting function.
The retailers that recognize this shift early will define the next phase of competition. Those that do not find their ability to scale are constrained, not by demand or budget, but by how effectively they have built the systems required to produce quality visuals at speed, scale, and with consistency. From what we have seen across previous cycles, once that gap opens, it becomes difficult to close.
