Your product images are failing AI. Most brands have no idea - and the conversion data proves it.

Here's a question for every head of ecommerce and creative director in consumer electronics: when did you last test your product images against an AI model?

If the answer is never (and for brands it is) you have a blind spot that's actively costing you conversions. Not hypothetically. Measurably. We've seen a 69% improvement in retail conversion rate from aligning creative assets with SoM AI-driven visibility signals for Performance Max campaigns. The images looked almost identical to human eyes. To AI, they were completely different products.

The multimodal shift that changes everything

Amazon Rufus, ChatGPT's shopping integration, Google AI Mode, these AI shopping assistants are now part of the electronics purchase journey. They don't just read your product copy. They evaluate your imagery. They assess whether your visuals clearly communicate what the product does and whether it matches the query being asked.

Your images built for human conversion - lifestyle context, beautiful lighting, aspirational environments - were designed around human psychology. AI systems evaluate for attribute clarity: is the key feature visible? Are differentiators legible at processing size? Does the visual evidence match the text claims? These are questions that SoM identify and answer.

STEP 1

STEP 2

STEP 3

01. Test

Test Assets Across AI Models

Upload text, images, or video assets to see how leading AI models interpret, describe, and rank them - identifying strengths, weaknesses, and misalignments before launch.

Test every asset - web, social, display, video - before it goes live

Share of Model's Asset Evaluation module tests how your creative assets perform when evaluated by AI models, before you commit production budget. Images, videos, PDPs, social content - the platform scores each asset, compares alternatives, and generates specific optimisation recommendations. The AI Readiness Audit goes further: it evaluates whether AI agents can actually access, crawl, and structurally interpret your digital assets - identifying technical barriers that limit AI visibility that most ecommerce teams have never checked.

What "failing AI" actually looks like in electronics

We've seen product images that perform brilliantly on Amazon - strong click-through, high conversion - score poorly when evaluated by AI shopping assistants. This brings back up the point that you are marketing to two audiences, the human eye and AI. The most common issues: the product's key feature isn't visible in the hero shot; lifestyle context obscures the product itself; text overlays legible to humans are illegible at AI processing sizes; the visual framing that pops on a bright retail screen flattens when AI processes it for recommendation matching.

When someone asks Amazon Rufus "which wireless headphones are best for gym use under $200", Rufus evaluates both text and visual content to determine its recommendation. The brand whose product imagery clearly communicates sweat resistance, secure fit, and sport use case in a way AI can process - that's the brand that appears in the recommendation. The brand with beautiful lifestyle imagery that doesn't foreground those attributes? It may not appear at all, regardless of how well the copy is written.

"The same images. Different performance. The gap between AI-optimised creative and human-optimised creative is real, measurable, and commercially significant."

The SoM + Pencil workflow: test before you shoot

The most powerful application of Share of Model for creative teams is pre-flight testing. Before the next product launch -before production budget is committed - Share of Model evaluates your creative concepts against AI models and identifies which visual direction performs better in AI-mediated discovery. The platform integrates directly with Pencil to generate AI-optimised asset variations at scale, taking the insight and turning it into production-ready alternatives.

Your creative team is talented. Your assets are beautiful. But if nobody has tested them against AI shopping assistants, you don't know how they're performing in the channel that's increasingly deciding which products get recommended.