Data vs. Commerce

A customer asks ChatGPT how to fix his style, gets sent to three stores, and nobody on the retail side can explain why those three and not the other thirty. That gap is where this episode sits. Matt Johnson and Floyd Blaikie talk with Dan Ornstein, Retail Industry Leader at Pivotree, about how large language models decide which retailers to recommend and what that means for the people who own product data.ㅤDan took the data side, and the friction surfaced fast. The marketers want brand, lifestyle photography, and emotional copy to carry the search. The machine starts with UPC codes, GTINs, inventory, and shipping policy before it cares about any of that. The conversation covers the robots.txt settings that quietly block AI crawlers, the attribution problem when a shopper leaves ChatGPT and goes straight to a store, and why content marketing still earns its place once the data foundation is solid.ㅤ👤 Guest BioDan Ornstein is Pivotree's Retail Industry Leader, focused on helping retailers grow revenue through unified commerce, customer experience, product data, and practical AI. Before Pivotree he was a Partner at KPMG Canada and a Director at Publicis Sapient, working across e-commerce, omnichannel, and loyalty. On this episode he took the data side, arguing that product data completeness, not brand copy, is what gets a retailer surfaced by AI shopping agents in the first place.ㅤ📌 What We CoverWhy retailers suddenly see traffic from ChatGPT, Perplexity, and Gemini without doing anything to earn itThe order an LLM works in: product data first, then price and availability, then third-party trust signals from sites like Vogue, GQ, or RedditThe robots.txt problem, where fraud and denial-of-service settings block the AI crawlers before they ever reach your catalogHow subjective attributes like "soft" or "puffy and warm" have to become data the model can read, like down fill rate and temperature ratingThe attribution gap when a shopper exits ChatGPT and goes straight to the store, and why LLM referrals still convert at a higher rateWhich categories suit agentic shopping now (grocery, hardware) versus where brand still drives the decision (fashion, home furnishings)What an e-commerce or merchandising leader should check tomorrow to confirm they show up at allㅤ🔗 Resources MentionedChatGPT, Perplexity, Gemini (AI assistants surfacing retailer recommendations)Shopify (embedded LLM referral analytics)Vogue, GQ, Reddit (third-party reference sites the models check)Amazon (marketplace-seller comparison)TikTok, YouTube, Instagram (social channels referenced)

Every analyst call this quarter runs on the same promise about AI: fewer humans, same output. Matt Johnson and Floyd Blaikie don't buy it, and neither do this episode's guests. Floyd sits down with Pivotree CEO Bill Di Nardo and chief architect Joel Farquhar to make the case for what they call real intelligence plus artificial intelligence, with real intelligence first. This isn't a data-versus-commerce split. It's the harder argument underneath the AI hype: augmentation versus replacement. The companies winning with AI didn't fire their experts. They handed those experts something sharper. Bill and Joel walk through what that looks like inside a lean services company, why customers spend the savings on more roadmap instead of pocketing them, and the one problem they admit they haven't solved yet.ㅤ👤 Guest BiosBill Di Nardo is the Chief Executive Officer of Pivotree. He founded Grocery Gateway, one of Canada's first major pure-play ecommerce brands, and was named EY Young Entrepreneur of the Year in 2000 before leading the merger that became Pivotree.ㅤJoel Farquhar is Pivotree's Chief Architect, with more than 25 years in enterprise commerce architecture and a seat on the MACH Alliance Technology Council. He came up through software development and now owns Pivotree's commerce architecture and platform strategy.ㅤ📌 What We CoverWhy the "fewer humans, same output" pitch fails the math, and why winning teams handed experts a sharper tool instead of cutting them. ㅤThe reason customers don't pocket the savings from AI-assisted systems integration, and what they ask for instead. ㅤHow a lean company scales without hiring 100 more people, and why that doesn't mean shrinking the team. ㅤJoel's matrix of domain knowledge against technical capability, and the multipliers, visionaries, and gatekeepers it produces. ㅤWhy Q1 was an experimentation quarter, and how some experiments quietly cost more time than they saved. ㅤThe move from billable hours to outcomes, and why CFOs spend when they have confidence the work will land. ㅤThe problem Bill says they haven't solved: where the next generation builds judgment when the entry-level work disappears.ㅤ🔗 Resources MentionedAnthropic's Claude (the AI code assistant and tool referenced throughout)Adapt Relentlessly (Pivotree's core value, cited by Bill)

The storefront said overnight shipping. The part showed up three days later, and the dishwasher was still broken.ㅤMatt Johnson and Floyd Blaikie open Episode 2 of Data vs. Commerce by tracing that delay past the product data they covered last time, into the place most teams forget to look: the integrations between systems. Matt walks through how an e-commerce system can confidently promise overnight delivery while the ERP and OMS behind it know the item is sitting in a warehouse across the country. The storefront wasn't lying. It just never got the truth from the back end.ㅤFrom there the two get into why national retailers and distributors end up with a twisted ball of yarn: 30 acquisitions, 17 Salesforce instances, crusty middleware nobody wants to touch, and load-bearing systems you can't switch off without the business crawling to a halt. This is a solo episode, hosted by Matt Johnson and Floyd Blaikie of Pivotree. The friction this week isn't data against commerce. It's the invisible plumbing between them, and who actually owns it.ㅤ📌 What We CoverㅤWhy an e-commerce system can promise overnight shipping when the item is actually in stock across the country, in a different warehouseHow distribution center location and on-shelf inventory live in back-end systems that often never connect to the front-end customer experienceWhat growth by acquisition does to your stack: inherited ERPs, inventory systems, and disparate data that all have to talk to one storefrontThe 17 Salesforce instances problem, and why companies insist on keeping every system they acquireBrittle, hard-coded integrations and crusty middleware held together by one person in ITWho needs the macro view of the system architecture, and why nobody clearly owns itWhere AI helps: reading APIs and connecting data faster than any human developer, plus agents that flag when an integration is about to breakWhere AI backfires: order management systems that hallucinate a part because the underlying data was wrongㅤ🔗 Resources MentionedPivotree, named on the episode for its work on AI integration frameworks and processesSalesforce, referenced via the company running 17 separate instancesThe system categories at the center of the conversation: ERP, OMS, PIM, and the front-end e-commerce platform, plus the integration layer and middleware that connect them

Episode 1 of Data vs. Commerce starts with a broken dishwasher and a part that didn't fit. Floyd Blaikie ordered an overnight pump from a distributor's website. Three days later it showed up with three pins instead of four. Same model number, wrong part. Matt Johnson walks through what actually broke, and it wasn't the warehouse.ㅤThe hosts kick off the show by naming the friction the whole podcast is built around. Data and commerce want the same outcome. They've stopped talking to each other in most organizations. Matt and Floyd, both of Pivotree, frame it less as a fight and more as couples therapy: a relationship that needs the communication rebuilt.ㅤThis episode is the premise. Future episodes pressure-test it with guests who take a side.ㅤ📌 What We CoverThe dishwasher pump story: a manufacturer changed three pins to four mid-year, kept the same model number, and the distributor's product page never caught up.Why customer service catches the blame for an error that started in a supplier feed.The reason distributors and manufacturers run physical fulfillment at 99.9% accuracy and product data nowhere near that.What "operating on putting out fires" looks like inside a product data team.The contractor who orders the wrong part, takes the heat from their customer, and never trusts the website again.Why B2B e-commerce penetration at distributors still sits in the 10 to 15% range and how product data quality drives that ceiling.Data governance as a continuous lifecycle rather than a one-time integration project.The personal fitness analogy: product data discipline is a habit, not a launch.ㅤ🔗 Resources MentionedPivotreeMatt Johnson on LinkedInFloyd Blaikie on LinkedIn

Every product moves twice. Once through the data layer, where it gets named, classified, attributed, priced, and made findable. Once through the physical layer, where it gets bought, picked, packed, shipped, and delivered. Most companies invest heavily in the second journey and treat the first as a pile of disconnected projects.ㅤThe gap between the two is where modern commerce actually fails. Stockouts that aren't really stockouts. The 0.8% integration error nobody caught. The AI agent that ordered the wrong size. The B2B replatform that blew up on data nobody had cleaned in six years.ㅤThis show lives in that gap. Hosts Matt Johnson and Floyd Blaikie bring on the people who own the data, run the platforms, and pay the price when those two stop playing nice. New episodes drop weekly. If you're responsible for any part of how products get from a database to a doorstep, this is your show. Subscribe now.