I process between thirty and eighty product photos a week for two e-commerce stores and a personal blog. Three years ago that was a full afternoon in desktop software. Today it is about forty minutes, and the difference is not a better tool, it is a fixed sequence of steps executed the same way every time. The workflow matters more than the editor. Here is mine, step by step, with the reasoning that makes each step worth doing in order.
Step one: cull and correct before editing
Before any AI touches a photo, two manual decisions take thirty seconds each and determine everything downstream. First, reject the unusable shots: blurred, overexposed, wrong angle. Editing a bad photo produces an edited bad photo, and no AI fixes composition after the fact.
Second, correct exposure and crop. The AI editor reads the image you give it, not the image you meant to give it. An underexposed photo produces noisy background removal. An over-cropped photo gives the generative expand nothing to work with. Thirty seconds of light correction before editing saves three minutes of AI cleanup after.
Step two: background removal or replacement
The first AI step, and the one with the biggest visual impact. Using the background removal in my usual AI photo editor workflow, I process the entire batch at once rather than one image at a time. Batch processing is not just faster; it produces more consistent results, because the AI applies the same edge detection parameters across all images.
For product photos going to a store listing, I replace with pure white, which meets marketplace requirements. For blog and social images, I replace with a contextual background, which means writing one background description per batch rather than per image, keeping the visual language consistent across the set.
Step three: cleanup and retouch
Background removal exposes problems the original background hid: dust on the product, fingerprints on glossy surfaces, uneven lighting on the edges. This is where the object eraser earns its place. I zoom to 200 percent on edges and high-visibility surfaces, and remove anything that would distract a buyer.
The discipline here is restraint. It is tempting to erase every perceived flaw, but over-cleaned photos look artificial and erode trust. I remove distractions, not character. A minor scuff on a used product stays. A fingerprint on a new product goes.
Step four: upscaling and enhancement
After cleanup, images often need resolution adjustment. Free tiers of most editors export at their cap, usually 1024 or 2048 pixels, which is sufficient for web but not for zoom features on marketplaces. Running the finished image through AI upscaling, even one level, adds enough detail for the zoom without introducing artifacts, provided the source was clean before upscaling.
The order matters: upscale after cleanup, not before. Upscaling magnifies everything, including the dust and fingerprints you meant to erase. Clean first, then enlarge.
Step five: export with intent
The final step is also the most often done wrong. Export settings determine file size, loading speed, and visual quality, and they differ by destination.
For marketplace listings: JPEG at quality 80, sRGB color profile, exact pixel dimensions required by the platform. Naming follows the SKU convention so the image maps to the product in bulk uploads.
For blog and social: JPEG at quality 75 for images above 1200 pixels wide, PNG only when transparency is needed. I generate three sizes, 1600, 800, and 400 pixels, because responsive websites serve the smallest adequate size, and serving a 1600-pixel image to a phone wastes bandwidth and slows the page.
For archival: the original RAW or high-resolution JPEG, untouched, in a dated folder. This is the recovery path when a platform changes requirements or a client needs the full-resolution version eighteen months later.
The weekly rhythm
Monday: shoot or receive new photos. Tuesday: batch process through steps one through three. Wednesday: upscale and export. Thursday: upload to platforms. Friday: review analytics on last week's images and note any that need reshooting.
The batch rhythm is the point. Processing all images through all steps in one session maintains consistency, builds momentum, and prevents the context-switching cost of editing one image at a time spread across the week.
The bottom line
A repeatable AI photo editing workflow, cull, remove backgrounds, clean up, upscale, export with intent, turns an afternoon of individual corrections into forty minutes of batch processing. The tools are good enough now that the bottleneck is process, not capability, and a fixed sequence executed weekly beats ad-hoc editing even with better software.
FAQ
What resolution should I export product photos at?
Marketplace listings need at least 1000 pixels on the longest side for zoom features. Blog images at 1200-1600 pixels work well. Export at the largest size the platform will display, not larger.
Should I remove backgrounds from all product photos?
For marketplace listings, yes, pure white backgrounds are often required. For blog and social media, contextual backgrounds usually perform better than pure white, because they add visual interest and context.
How do I batch process photos efficiently?
Group photos by product or shoot session, process all backgrounds first, then all cleanup, then all upscaling. Handling one step across the entire batch is faster than completing one photo at a time, because the AI applies consistent parameters across similar images.