Retailgentic: Agentic Commerce meets Retail and Brands

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

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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/

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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

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📺 Watch episodes on YouTube & Subscribe for updates: https://www.youtube.com/@Retailgentic

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

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In this episode of Retailgentic, Scot and Ken map out how Mastercard saw agentic commerce coming early, and what they built in 2025 to prepare the ecosystem, especially around trust, identity, consent, and tokenization.
We dig into: 
How Mastercard Foundry takes ideas from experiments → production-grade products → global scale
“Know Your Agent”: registering legitimate agents (agentic equivalent of KYC/KYB) to reduce malicious bot risk
“Order Intent”: adding richer context so that with consent, agents/merchants can fulfill the right order (not just process a payment)
Why consumer consent is non-negotiable (passkeys/biometrics + tokenized credentials)
Mastercard Insight Tokens: a secure and governed way for agents to access and apply permissioned insights from Mastercard, which will enable consumers to receive, with their consent, more personalized and useful experiences
Agentic Toolkit and why Mastercard leaned into services to support customers move from strategy → execution fast
Mastercard’s Agent Pay Acceptance Framework: helps merchants to
transact with trusted agents with a minimal lift
Standards: why Mastercard will support ACP + AP2 and expects interoperability (or convergence) over time
Agentic commerce won’t be won by whoever moves fastest, it’ll be won by whoever earns trust at scale.

Key timestamps
05:14 — Ken’s background: technologist, global banking work, startups, Accenture, Citi → Mastercard Foundry
07:44 — How Foundry works: experiment, build, then “graduate” products into core teams
10:32 — The pace-of-change thesis: why organizations must be built to adapt continuously
13:07 — How Mastercard saw agentic commerce early (signal-spotting in 2024)
14:23 — The 3 converging forces: AI reasoning, compute, and foundations (tokens/passkeys/consent)
19:01 — AgentPay launch + what it includes (Know Your Agent, Order Intent)
21:05 — Consumer consent, passkeys/biometrics, and tokenizing payment credentials
22:25 — First agentic transaction (partner example)
23:19 — Agentic Toolkit + Insight Tokens + consulting expansion
26:00 — Merchant Acceptance Framework + scaling agentic readiness for the “long tail” of merchants
27:13 — Tokenization explained simply (cocktail-party version)
29:37 — Passkeys explained (biometric identity replacing passwords)
33:37 — Returns problem + how agentic workflows could reduce “buy 3, return 2”
40:19 — Standards wars: ACP vs AP2 + why Mastercard stays agnostic
44:03 — Crystal ball: adoption, trust, and why B2B may go autonomous faster
47:43 — The travel agent analogy: why some flows stay assisted by choice

👉 Connect with Ken: https://www.linkedin.com/in/ken-moore-cio/
👉 Learn more about Mastercard: https://www.mastercard.com/us/en.html
🔗 Agentic Toolkit: https://www.mastercard.com/us/en/news-and-trends/press/2025/september/mastercard-unveils-new-tools-and-collaborations-to-power-smarter,-safer-agentic-commerce.html
🔗Agentic Commerce Rules, Mastercard Joins Google: https://www.mastercard.com/us/en/news-and-trends/stories/2026/agentic-commerce-rules-of-the-road.html
🔗Agent Suite: https://www.mastercard.com/us/en/news-and-trends/press/2026/january/mastercard-launches-agent-suite-to-ready-enterprises-for-a-new-e.html
🧠 Want to stay ahead in AI commerce? Subscribe and follow along:
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🎧 Listen wherever you get your podcasts: Spotify, Apple, etc: https://retailgentic.transistor.fm