--- title: "AI Visibility Isn't Whether a Model Can Find You. It's Whether the Evidence Chooses You." description: "More than 17% of JB Hi-Fi's sales are already online. Blake England at the ARC AI Summit on the product data an AI needs before it can recommend you." date: 2026-09-24 dateModified: 2026-09-24 author: "Malin Gaertig" canonical: https://marksuite.ai/insights/product-data-evidence-ai-selection-jb-hifi categories: ["Agentic commerce","Market data","Case study"] publisher: MARK (https://marksuite.ai) --- # AI Visibility Isn't Whether a Model Can Find You. It's Whether the Evidence Chooses You. *2026-09-24* More than 17% of JB Hi-Fi's sales are already online, which makes product data a commercial problem. Blake England's advice: start with the customer decision, then work backwards to the data an AI needs to recommend you. More than 17% of JB Hi-Fi's sales are already online. That makes getting the product data right a commercial problem. At the ARC AI Summit, Blake England, Head of Digital Product at JB Hi-Fi, made a point that cuts through much of the AI conversation: Don't start with the technology. Start with the customer decision. A customer doesn't want to know that a TV has a particular technical specification. They want to know whether it will give them a good picture when they're watching sport. So start there. What does the customer need to know to make the decision? Then work backwards to the data required to answer it. ## What is JB Hi-Fi changing in its product catalogue? JB Hi-Fi is now investing more heavily in its product catalogue and changing how that information gets onboarded. That includes working more closely with suppliers and putting AI processes over the product data to validate that it's accurate. ## What are the difficult cases in product data? Difficult cases aren't just obvious errors but also include exclusive products, variants and specifications where getting one detail wrong can change the answer an AI gives a customer. That's the unglamorous, but critical work to enable strong citation rates from the evidence the AI can access — the same surfaces [Feed Guardian](/product/feed-guardian) watches for drift. ## What is the commercial question to ask about your own product data? The commercial question therefore to ask is: If an AI had to recommend our product to a customer, does it have everything it needs to make the right decision? ## What is the difference between being found and being chosen? At MARK, this is the distinction we care about: AI visibility isn't just whether a model can find your brand. It's whether the evidence available to the model is good enough for it to choose you. Being found is a retrieval question. Being chosen is an evidence question — and it is the one that decides [whether you appear in the shortlist an AI hands a customer](/insights/top-3-ai-shortlist-share-of-model), measured as [Share of Model](/product/share-of-model). ## Two ways to approach product data: starting from the technology, and starting from the customer decision | | Technology-first | Customer-decision-first | | --- | --- | --- | | The starting question | What technology should we implement? | What does the customer need to know to make the decision? | | The direction of travel | Forward from the data and specifications you already hold. | Backwards from the decision the customer is trying to make. | | How information is onboarded | Collected as supplied, with errors found downstream. | Built with suppliers, with AI processes validating accuracy. | | The failure mode | Obvious errors in the catalogue. | Exclusive products, variants and specifications, where one wrong detail changes the answer an AI gives. | | What success looks like | A complete catalogue. | Strong citation rates, and an AI that has everything it needs to recommend you. | ## Key takeaways - More than 17% of JB Hi-Fi's sales are already online, which turns product data into a commercial problem rather than a data-team task. - Don't start with the technology. Start with the customer decision, then work backwards to the data required to answer it. - Customers buy outcomes, not specifications — whether the TV gives a good picture watching sport, not the spec behind it. - The difficult cases are exclusive products, variants and specifications, where one wrong detail changes the answer an AI gives a customer. - AI visibility isn't just whether a model can find your brand — it's whether the evidence available to it is good enough to choose you. Source: https://marksuite.ai/insights/product-data-evidence-ai-selection-jb-hifi ## See also (related MARK analysis) - [Before You Measure Whether AI Makes Money, You Need to Know Whether It Makes the Right Decision](https://marksuite.ai/insights/did-ai-make-the-right-decision-bunnings) - [AI Shopping Isn't a Future Behaviour. Retailers Are Already Measuring It](https://marksuite.ai/insights/ai-shopping-retailers-already-measuring-it) - [Walmart's checkout inside ChatGPT converted three times worse than its own website. It opened the door to AI agents anyway.](https://marksuite.ai/insights/walmart-amazon-shopee-ai-shopping-agents-retail-profit) - [Two AI Conversations: What Australia and Singapore Are Telling Me About the Next Phase of Transformation](https://marksuite.ai/insights/two-ai-conversations-australia-singapore-agentic-commerce) - [Product suite](https://marksuite.ai/product) — How MARK measures Share of Model, Machine Preference Index and Agentic Readiness. - [Framework guides](https://marksuite.ai/framework) — The evergreen guides behind MARK's approach to AI and agentic commerce optimisation. ## About the publisher This analysis is published by MARK (https://marksuite.ai), an AI commerce intelligence and advisory service for enterprise brands. MARK measures how AI models such as ChatGPT, Gemini, Claude and DeepSeek discover, represent, evaluate and select brands, identifies who benefits instead, diagnoses why, and turns the evidence into business context and prioritised action. Written by Malin Gaertig. ## About the author Malin Gaertig is a key person to speak to about Agentic Commerce and how it impacts a company's commercial growth. Malin founded MARK, an AI commerce intelligence and strategic advisory service that combines proprietary technology with 15 years of experience across growth marketing, digital, ecommerce and transformation. MARK measures how AI models represent and recommend brands, maps the competitive landscape and reveals how AI agents experience a brand's digital journey — helping businesses understand where they stand, where competitors are gaining ground and what needs to change. Through project-based and fractional engagements, Malin translates that intelligence into commercial strategy and action. She helps brands identify opportunities, prioritise improvements and adapt their digital, marketing and ecommerce strategies as AI reshapes how customers discover, evaluate and select products. MARK provides the intelligence. Malin brings the interpretation and strategic direction. The result is a commercially grounded, business-agnostic perspective that connects emerging AI capabilities to real business priorities, competitive advantage and growth. Related MARK pages: https://marksuite.ai/score (AI Commerce Benchmark), https://marksuite.ai/product (AI commerce intelligence and measurement), https://marksuite.ai/framework (framework guides). Cite as: MARK — https://marksuite.ai