--- title: "AI Isn't Just Becoming Another Channel. It's Becoming Part of the Selection Process" description: "Lessons from the ARC AI Summit on AI product selection, retail data, shopping-assistant measurement and organisational accountability." date: 2026-09-17 dateModified: 2026-09-17 author: "Malin Gaertig" canonical: https://marksuite.ai/insights/ai-selection-process-retail-arc-summit categories: ["Agentic commerce","Governance","Market data"] publisher: MARK (https://marksuite.ai) --- # AI Isn't Just Becoming Another Channel. It's Becoming Part of the Selection Process *2026-09-17* The ARC AI Summit showed that the retail AI question has moved beyond adoption: brands now need to know whether AI can see their products, why it selects them and who is accountable for the choice. That was the main idea I kept coming back to after the Australian Retail Council (ARC) AI Summit. Across the sessions, the conversation wasn't really about whether retailers should 'use AI' because they already are. The more relevant question is what happens when AI starts sitting between the customer and the choice. A customer asks a question. AI researches the options. It decides what is relevant. It recommends a shortlist. And increasingly, the customer acts on that recommendation. ## Can AI see your products — and does it select them? This is the first question retailers need to start answering. Being technically available to an AI system is not the same as being selected. The commercial issue begins when a model can find a product but recommends a competitor instead. JB Hi-Fi is working backwards from the customer decision to improve the product data AI relies on. That is a practical recognition that product information is no longer just merchandising content; it is evidence a machine uses to decide what belongs in the shortlist. ## Does AI have the right evidence to make that decision? Once an AI system can see a product, the next question is whether it has enough accurate, structured and persuasive evidence to choose correctly. The quality of the recommendation depends on the quality of the information the model can retrieve and interpret. That changes the job for retailers. Product data has to do more than populate a page or a feed. It has to help an AI system compare suitability, value, availability and risk with enough confidence to recommend one option over another. ## If AI makes the wrong decision, does anyone inside the business know why? Bunnings is already measuring what happens when customers use an AI shopping assistant, including whether the advice is right, useful and safe. That moves measurement beyond adoption or engagement and towards the quality of the decision itself. The organisational implication is just as important. If AI recommends the wrong product, omits the brand or gives unsafe advice, somebody needs to see the failure, understand its cause and be able to act on it. Without that ownership, the selection process remains commercially important but operationally invisible. ## What does leadership behaviour have to do with AI adoption? A workforce study shared by Deloitte's Ben Fish produced a finding that challenged its own hypothesis: leadership's visible use of AI was a stronger predictor of adoption than the growth mindset the researchers had expected to matter most. That finding connects technology adoption to organisational behaviour. Teams pay attention not only to what leaders say about AI, but to whether those leaders visibly use it, question it and take responsibility for its outcomes. ## What are the five retail AI questions to answer next? Different problems are emerging from the same underlying shift. Technology is changing how customers make decisions. It is changing the evidence businesses need to provide, creating new ways to measure whether AI made the right choice and raising a new organisational question: who inside the business is paying attention to what AI chooses — and why? Over the next five posts, I'll peel those questions apart: Can AI see you? Will it select you? Does it have the evidence to choose correctly? Can you tell whether it made the right decision? Who inside the business is paying attention? ## Key takeaways - AI is becoming part of the retail selection process, not simply another customer channel. - Retailers need to distinguish between products being visible to AI and products being selected by it. - JB Hi-Fi is working backwards from customer decisions to improve the product data AI uses as evidence. - Bunnings is measuring whether AI shopping-assistant advice is right, useful and safe. - Visible leadership use of AI can matter more to workforce adoption than the growth mindset leaders expect to drive it. Source: https://marksuite.ai/insights/ai-selection-process-retail-arc-summit ## See also (related MARK analysis) - [93% of AI Investment Is Going Into Technology. The Harder Part Is the People Around It.](https://marksuite.ai/insights/ai-adoption-93-percent-technology-people-process) - [Just in: What Australia's AI Trust Gap Means for Brands Selling Through AI Agents](https://marksuite.ai/insights/australia-ai-trust-gap-brands-selling-through-ai-agents) - [AI Shopping Isn't a Future Behaviour. Retailers Are Already Measuring It](https://marksuite.ai/insights/ai-shopping-retailers-already-measuring-it) - [Just in: Why the Agentic Commerce Governance Gap Is the Real Risk for Brands Right Now](https://marksuite.ai/insights/agentic-commerce-governance-gap-real-risk-brands) - [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