PodcastsTechnologieRetailgentic: Agentic Commerce meets Retail and Brands

Retailgentic: Agentic Commerce meets Retail and Brands

Scot Wingo
Retailgentic: Agentic Commerce meets Retail and Brands
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41 afleveringen

  • Retailgentic: Agentic Commerce meets Retail and Brands

    Exploring the Intersection of OpenClaw and Agentic Commerce:lobster: - the Retailgentic Podcast OpenClaw Deep Dive

    05-03-2026 | 44 Min.
    In this episode of the top Agentic Commerce Podcast we explore the powerful shopping powers of the OpenClaw open-source agent. Can the Claw shop on Amazon, shop locally and use the VisionClaw system with META Ray-Ban’s to buy a very hard to search for dog treat? Tune in to find out!
    Highlights:
    What OpenClaw is (and why “persistent” changes everything)
    The real difference between OpenClaw vs. ChatGPT/Claude as tools
    How OpenClaw runs (Mac Mini vs. cloud VPS like DigitalOcean)
    Why “no guardrails” is both the innovation and the risk
    Risk mitigation: limiting blast radius with single-use virtual cards
    Why browser-driven commerce is riskier than server-to-server protocols (ACP/UCP)
    The “skills” layer: how OpenClaw becomes extensible like Lego bricks
    Why SERP-style APIs beat slow webpage crawling for shopping tasks
    Live demos: product search, local inventory, and frictionless checkout
    Using Slack as the agent interface (channels for shopping, health, etc.)
    The sci-fi moment: Meta Ray-Ban glasses + VisionClaw + Gemini for visual shopping

    OpenClaw isn’t the final answer for agentic commerce, security and architecture matter, but it is an incredible preview of where the UX is heading. If you want a glimpse of the “always-on shopping agent” future (and how people are hacking it together right now), this one’s for you. Happy agentic commerce.🦞
    Timestamps:
    01:38 — OpenClaw takeover begins: why Scot called Ryan as a lifeline
    05:26 — What OpenClaw is: persistent agent, open source, “unhinged,” and why that matters
    08:21 — The “gigantic loop”: always-on agent behavior (old-school “cron jobs,” new-school autonomy)
    09:26 — The killer feature: OpenClaw keeps working after you close your laptop
    12:33 — Read-only Gmail as an assistant: catching missed emails + reducing cognitive load
    14:35 — The trap: spending time building automation to “save time” (catch-22)
    15:33 — Why OpenClaw-style commerce is riskier than ACP/UCP: browser manipulation
    18:02 — Gateway Dashboard tour: where OpenClaw is configured (channels, skills, cron jobs)
    20:30 — Skill spotlight: Rye for agentic Amazon-friendly purchasing
    22:39 — Skill spotlight: SERP API for faster product search + ratings without crawling
    24:30 — Skill spotlight: Buy Anything for context-filled checkout (address, identity, card)
    28:19 — Demo: “Find me the 5 best-rated 65-inch TVs” (online + local)
    35:20 — Live purchase demo: finding USB-C cables, choosing Anker, and the agent checks out
    37:05 — “Most frictionless checkout”: no login, agent already knows everything
    37:31 — What this means for Alexa: why “available 24/7” changes habits
    39:07 — Meta Glasses demo setup: first-person vision commerce 
    40:24 — Vision commerce in action: identifies product + searches best price automatically
    41:34 — Multi-store results: Amazon + Chewy + Walmart links dropped into Slack

    👉 Connect with Ryan: https://www.linkedin.com/in/ryaneade/

    🧠 Want to stay ahead in AI commerce? Subscribe and follow along:
    📰 Subscribe to the free Substack: retailgentic.ai
    📺 Watch episodes on YouTube & Subscribe for updates: https://www.youtube.com/@Retailgentic
  • Retailgentic: Agentic Commerce meets Retail and Brands

    Kiri Master's Round 2: The Ad Wars Come to Agentic Commerce

    26-02-2026 | 50 Min.
    Retail media has been a profit engine for retailers, especially onsite sponsored product ads with massive margins. But what happens when shopping shifts from browsing and searching to AI agents that “decide the SKU” before a shopper ever hits a retailer site? In this episode, Scot and Kiri map the collision course between retail media networks and agentic commerce, walk through emerging ad formats from Google, Amazon (Rufus), and LLMs, and dig into the hardest problem of all: monetizing AI attention without breaking trust.
    What’s Covered:
    Why agentic shopping compresses the journey, and shrinks onsite ad inventory
    The “two threats” to onsite retail media
    How offsite retail media depends on audience signals that may dry up as browsing declines
    Why in-store media may be the most resilient channel 
    Google’s “Direct Offers,” the rise of an agentic storefront, and what it unlocks
    The emerging idea of “Agentic PLAs” and retailers bidding at the SKU level (Buy Box vibes)
    Kiri’s wishlist: multimodal ads (video/try-ons), offsite audience extension, and contextual targeting over “creepy” behavioral retargeting
    Agentic commerce won’t kill advertising, but it will force it to evolve fast. The winners will be the platforms that can monetize attention without sacrificing trust, and the brands/retailers that learn to show up in these new surfaces early.
    Timestamps
    00:05:04 — Meet Kiri Masters + Retail Media Breakfast Club
    00:08:01 — The three buckets: onsite, offsite, in-store retail media
    00:10:01 — Why onsite is the money machine (and most vulnerable)
    00:11:11 — Offsite retail media + closed-loop attribution
    00:14:23 — In-store media: small today, resilient tomorrow
    00:16:11 — Are retailers already seeing traffic shifts? Category matters
    00:19:27 — ChatGPT ads: early signals and why it’s still rudimentary
    00:21:22 — Scott’s framework: ad formats + Instant Checkout incentives
    00:23:06 — Google’s “Direct Offers” and the agentic storefront idea
    00:25:10 — Collaborative bidding: retailer + brand split the spend
    00:28:11 — Google testing new units: “Agentic PLA” / paid retailer placement
    00:33:14 — Trust vs monetization: don’t break the golden goose
    00:34:04 — Amazon Rufus: sponsored prompts as a new surface
    00:39:08 — Instacart’s ad playbook + trade dollars as inspiration
    00:40:38 — Why it’s not a race to the bottom: service layers + loyalty
    00:43:24 — The big hope: context-based targeting over creepy retargeting
    k00:46:52 — If Kiri ran ChatGPT ads: offsite + multimodal + new formats
    00:48:04 — Virtual try-ons + “throw away the sponsored product textbook”
    00:50:00 — Closing + follow Retailgentic
    👉 Connect with Kiri: https://www.linkedin.com/in/kiri-masters/
    👉 Learn more about Retail Media Breakfast Club: https://www.retailmediabreakfastclub.com

    🧠 Want to stay ahead in AI commerce? Subscribe and follow along:
    📰 Subscribe to the free Substack: retailgentic.ai
    📺 Watch episodes on YouTube & Subscribe for updates: https://www.youtube.com/@Retailgentic
  • Retailgentic: Agentic Commerce meets Retail and Brands

    True Fit Announces Agentic Commerce Agent To Solve the Fit/Sizing-Driven $850B Returns Crisis Facing Fashion Online Retailers

    17-02-2026 | 23 Min.
    True Fit is evolving from a static “what size should I buy?” widget into a conversational agent that can handle the nuanced questions shoppers actually ask: comfort, flattering fit, fabric behavior, and edge cases like petite proportions. We also dig into how vertical agents (like True Fit) can accelerate horizontal super-agents via Model Context Protocol (MCP), and why the future of agentic shopping will be built on clean, structured, prioritized proprietary data, not scraped internet sentiment.

    Highlights
    True Fit’s specialized size + fit agent (and why it matters now)
    Why 70% of fashion agent questions are about size/fit
    The “Fit Passport”: from form-filling to natural conversation profiling
    What generic agents miss: real-time sales + returns behavior, not just PDPs/reviews
    Vertical vs. horizontal agents, and how True Fit uses MCP as an accelerant
    The unglamorous moat: data cleaning, normalization, canonicalization
    True Fit scale: 80M active users, hundreds of millions of profiles, ~100K brands, ~500 retailers
    If agentic commerce is collapsing the funnel, fit is one of the biggest friction points left, and True Fit is making it a first-class agent powered by the kind of data most models will never see.
    Timestamps
    01:44 — Breaking news episode + guest intro (Jessica Murphy, True Fit)
    03:12 — True Fit announces a specialized size + fit agent
    04:20 — From static widget to agentic shopping assistant
    05:47 — Why fit-related returns are so brutal in apparel
    07:02 — What the agent experience looks like
    08:05 — “Fit Passport” and conversational profiling
    09:07 — Where checkout happens (and what’s coming later)
    10:17 — Why generic agents break: stale info + limited context
    11:35 — The moat: structuring + cleaning sizing data
    12:30 — Vertical vs. horizontal agents
    13:29 — MCP as an accelerant for super-agents
    15:18 — Top-of-funnel value: narrowing choices to “most likely kept”
    16:10 — Who might use TrueFit’s MCP (LLMs + agent builders)
    16:37 — Availability: March partners, April target GA
    18:01 — Founder story: why Jessica started True Fit
    19:41 — Fundraising reality + “it’s a data problem”
    21:16 — True Fit scale + global complexity of sizing

    👉 Connect with Jessica: https://www.linkedin.com/in/jessica-murphy-68a8b8/
    👉 Learn more about True Fit: https://www.truefit.com/

    🧠 Want to stay ahead in AI commerce? Subscribe and follow along:
    📰 Subscribe to the free Substack: retailgentic.ai
    📺 Watch episodes on YouTube & Subscribe for updates: https://www.youtube.com/@Retailgentic
  • Retailgentic: Agentic Commerce meets Retail and Brands

    Saurabh Vijayvergia & Brian McCarthy from Deloitte on Trust, Catalogs, and the Real Agentic Commerce Stack

    12-02-2026 | 51 Min.
    There’s a fundamental difference between adding AI to today’s ecommerce workflows and re-architecting commerce for an agent-driven world.
    In this episode, we start with Deloitte’s Agentic Commerce paper and quickly fan out into what’s changed since its release. If you’re trying to separate signal from noise in the rush toward AI-powered shopping, this conversation grounds Agentic Commerce in real systems, real economics, and real decisions retailers need to make now.
    Highlights 
    Why Agentic Commerce is a paradigm shift, not a bundle of AI features
    The evolution from SEO → GEO → ACO, and why GEO is just a waypoint
    The two biggest misconceptions Deloitte is hearing from retailers and brands
    Why structured product + data catalogs are the no-regret investment
    The real question execs keep asking: Where do we start? (and what’s measurable)
    The “Acommerce ecosystem”: orchestrators + specialized “nano agents”
    Trust, security, returns, customer service, and why this can’t be a side project
    Forecasts for how big agentic commerce gets, and why the point is: it moves the needle
    Agentic commerce is becoming a new channel with new unit economics, and the brands and retailers that get their product truth, governance, and trust foundations right now will be the ones that win when agents become the default interface.

    Timestamps:
    01:54 — Meet Deloitte
    02:10 — The paper: Agentic Commerce: Redefining Retail Economics
    03:55 — ACO + the SEO/GEO conversation
    04:11 — VJ’s background: SAP → Deloitte, retail AI intersection
    05:50 — Brian’s background: supply chain → strategy → consulting
    07:49 — “Traditional AI” vs GenAI in retail
    09:38 — Why Deloitte wrote the paper (and why GEO isn’t the endpoint)
    11:49 — Deloitte’s definition: why it’s a journey, not a switch
    13:33 — NRF vibe check + what’s changed since December
    14:27 — Common misconceptions (and what leaders miss end-to-end)
    16:14 — The questions execs are asking: where to start + ROI
    18:03 — Why this could be a golden age of storytelling + loyalty
    21:06 — The no-regret starting point: catalogs + structured data
    21:34 — Org design + governance + democratizing AI usage
    23:15 — CFO-ready thinking: measuring value by channel
    26:12 — Zero-click pressure + “what are you doing about it?”
    28:31 — Balanced portfolio: fast wins vs complex upside
    30:32 — The core distinction, again: AI add-ons vs agentic redesign
    31:18 — Do we still need websites? (channels vs replacement)
    33:17 — Deloitte’s role: advise, build, operate
    37:29 — “Acommerce” + ecosystems of nano agents
    41:31 — Protocols + UCP + what changes next
    42:50 — Meta, OpenAI, Google: where this is headed
    46:58 — 2030 forecasts: conservative vs aggressive cases
    50:02 — Where to follow Saurabh's podcast: The Retail Tales

    👉 Connect with Saurabh: https://www.linkedin.com/in/svijayvergia/
    👉 Connect with Brian: https://www.linkedin.com/in/briancmccarthy/
    👉 Learn more about Deloitte: https://www.deloitte.com/us/en.html
    Check out the materials we discuss:
    🔗 Agentic Commerce: Redefining Retail Economic

    🧠 Want to stay ahead in AI commerce? Subscribe and follow along:
    📰 Subscribe to the free Substack: retailgentic.ai
    📺 Watch episodes on YouTube & Subscribe for updates: https://www.youtube.com/@Retailgentic
  • Retailgentic: Agentic Commerce meets Retail and Brands

    Sensor Tower SVP Ian Simpson breaks down Holiday ’25 funnel data, conversion lift, and what brands should do next.

    05-02-2026 | 1 u. 8 Min.
    In this episode, Scot digs into a question he keeps getting from readers and listeners: What’s actually going on inside Amazon’s Rufus, and what should brands do about it?
    Ian brings fresh analysis from Sensor Tower’s panel-based methodology (privacy-compliant, double opt-in), walking through Holiday ’25 shopping sessions to show where Rufus shows up in the funnel, how usage spikes during peak moments, and why it appears to correlate with dramatically higher conversion. Along the way, they zoom out to the bigger shift: as more people learn to “talk to AI” (thanks to ChatGPT-style habits), conversational shopping becomes increasingly normal, and increasingly hard to “optimize” using old keyword-era tactics.

    Highlights:
    “The consumer is trained.” A year of daily conversational AI use has taught people how to prompt, so when they see Amazon Rufus, they already know how to use it.
    Rufus sessions show a major conversion lift. In Ian’s read of the report: ~3.5× lift vs. non-Rufus sessions, with Rufus-touch sessions far more likely to end in purchase.
    Rufus had outsized influence during Holiday ’25. A large share of purchases included Rufus interaction, even if not every session did.
    Their team mapped ten distinct Rufus-assisted shopping paths, including the standout “Research Conversationalist” profile.
    Correlation vs causation is real. Ian flags measurement caveats: not every “non-Rufus session” is a shopping mission; intent bias exists; so the takeaway is directional, but meaningful.
    Brands should shift from “keyword jail” to “product truth.” Better attributes, clearer specs, stronger review signals, and real storytelling matter more in conversational shopping.
    AEO anxiety + the brand-level rebound. Ian argues the future isn’t just “optimize for prompts”, it may reward brands with stronger reputation and social proof signals (think forums and communities).
    AI can talk consumers out of premium. They’ve seen examples where AI steers price-sensitive users away from expensive brands, an early warning system for brand teams.
    Retail media won’t vanish, but it will mutate. In a lower-click world, retailers will experiment heavily to preserve value, without turning the experience into ad soup.
    If you’ve been treating Rufus like a curiosity, this data makes it hard to ignore: conversational commerce isn’t “coming”, it’s already shaping how high-intent shoppers decide.
    Timestamps:
    00:00 – The Consumer Is Now Trained
    02:09 – Why This Episode Focuses on Rufus
    05:49 – Ian Simpson’s Background
    06:47 – Founding a Bottled Tea Startup
    09:14 – Pathmatics → Sensor Tower Acquisition
    10:11 – How Retail Media Intelligence Was Born
    13:50 – How Sensor Tower Delivers Its Data
    15:36 – Why Sensor Tower Started Tracking Agentic Commerce
    21:08 – How the Rufus Analysis Was Done
    22:48 – The 3.5× Conversion Lift Explained
    24:18 – Amazon’s $10B Rufus Claim vs. Independent Data
    28:16 – Correlation vs. Causation in Rufus Usage
    31:23 – Rufus as a Research Companion
    32:20 – The Cup Holder Story (Problem-Based Shopping)
    39:55 – Why Rufus Usage Sticks After the Holidays
    40:19 – The 10 Rufus Shopping Profiles
    41:35 – The “Research Conversationalist” Funnel
    45:00 – Why “AEO” Makes Ian Nervous
    46:47 – Brand Matters Again in Agentic Commerce
    48:47 – When AI Talks Consumers Out of Premium Products
    58:21 – Retail Media’s Future in a Low-Click World
    01:01:31 – Avoiding the Minority Report Ad Nightmare
    01:06:21 – Instacart: The Wild West of Retail Media
    01:07:15 – Where to Follow Ian
    👉 Connect with Ian: https://www.linkedin.com/in/iansimpson/
    👉 Learn more about Sensor Tower: https://sensortower.com
    Check out the materials we discuss:
    🔗 Sensor Tower Black Friday AI Trend Update 
    🔗 How Amazon’s Rufus Shaped Holiday Shopping

    🧠 Want to stay ahead in AI commerce? Subscribe and follow along:
    📰 Subscribe to the free Substack: retailgentic.ai
    📺 Watch episodes on YouTube & Subscribe for updates: https://www.youtube.com/@Retailgentic

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Retailgentic is the podcast where Agentic Commerce meets retail innovation. We help retailers and brands prepare for the future of agent-driven shopping with actionable news, expert insights, deep analysis, and forward-looking predictions.
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