# Roger Dunn

Roger Dunn writes The AI Commerce Brief on LinkedIn and analyzes how AI is changing retail, agentic shopping and retail media. Dunn also says Thrad is building ways for brands to appear in AI shopping answers and for retailers to earn revenue from conversational commerce.

- Profile: https://b2binfluencer.marketing/creators/roger-dunn/
- LinkedIn: https://www.linkedin.com/in/rogerdunn
- LinkedIn followers: 29,510
- Topics: artificial-intelligence, agentic-commerce, product-discovery, retail-media, retail-technology
- Based on 14 dated posts, latest 2026-10-08

## Summary

Roger Dunn is most useful for practical analysis of AI shopping agents, retail media and commerce infrastructure.

- Best for: A measurement, attribution or commerce-infrastructure vendor targeting retail media and ecommerce teams for agentic commerce content.
- Not for: A consumer packaged goods brand wanting broad shopper awareness measured on impressions and reach with demographic targeting guarantees.
- Check first: Confirm recent results, LinkedIn analytics and audience breakdown by job function, seniority and region.

### Takeaways

- Agent checkout is a decision most retailers made by accident.
- Judge a retail media network by whether ad and purchase sit in same place.
- Product data is becoming media inventory.

## Main ideas

- **Agent checkout is a decision most retailers made by accident**: Dunn frames retail's response to AI shopping agents as three positions taken within a fortnight: block the agent, partner with one, or open checkout to any browser agent. The third option matters most, they argue, because it is the one most retailers took without choosing: a platform-level change quietly enrolled every store trading on it. They also argue that blocking is harder to sustain than it appears, because the legal question of who is 'accessing' a site when an agent shops may not fall the way a retailer assumes.
- **Judge a retail media network by whether the ad and the purchase sit in the same place**: Dunn's test for new retail media inventory is structural: does the advertising and the buying happen in the same place, closing the loop as sponsored product listings do? If the sale happens somewhere the ad is not, the network is closer to advertising on a screen in a petrol station — reach without a direct link to the transaction. They apply this to McDonald's without dismissing it, conceding that a large loyalty base could make such a network effective anyway. Buyers should demand more than impressions as evidence that the placement worked.
- **Product data is becoming media inventory**: This is Dunn's most explicit commercial thesis: retail technology spent a decade optimising the shelf, and the next decade is about the shortlist. Once an assistant answers 'what should I buy', the product data a retailer or brand holds stops being purely an operations concern and starts behaving like advertising inventory, because it is what the answering system reads. The argument is as much about where influence moves as about who sells it. Dunn says plainly that this is the logic Thrad builds around, making the thesis company positioning as well as observation.
- **Read the plumbing of a launch, not the headline**: Dunn's news posts move from the announcement to the connective detail: which systems are involved, which integrations exist, which numbers have a disclosed base, and what a brand could actually buy today. They keep the vendor's own wording and figures attached to the item they came from, then distinguish confirmed capability from direction of travel. Where they speculate, the post labels it as anticipation rather than fact.
- **Attribution and the customer relationship are the open questions in conversational selling**: Dunn repeatedly examines what a conversation-shaped purchase means for measurement and customer ownership. If a sale can begin in a chat and finish without a click, click-based attribution does not describe what happened, and the platform hosting the conversation may sit between the retailer and its customer. They raise these as unresolved questions the announcements leave unaddressed, not as settled outcomes.
- **Treat agent traffic as customers, not bots**: Dunn's practical position for retailers is that filtering out agent traffic is not a neutral technical default — it is a decision about which of their own customers gets served. If a personal agent acts for someone who has an account and a saved card, a blanket bot-block turns that person away at the door. The advice is concrete and checkable: make product data legible to agents, decide whether an agent would get through checkout, and treat the block-or-permit question as a policy choice with a customer consequence.

## Work to read

- [Retail picked three positions on AI shopping agents in a fortnight](https://www.linkedin.com/posts/rogerdunn_in-the-space-of-a-fortnight-retail-picked-activity-7512972585395974144-25ix)
- [TikTok's agentic commerce announcements, read through the plumbing](https://www.linkedin.com/posts/rogerdunn_agenticcommerce-aicommerce-retailmedia-activity-7513123816881946624-DEmW)
- [OpenAI's always-on agent: can it read your product data, and would it get through your checkout?](https://www.linkedin.com/posts/rogerdunn_agenticcommerce-aicommerce-aiagents-activity-7510820584356773888-bpzZ)
- [McDonald's media network: what is live, what is ambition, and why the loop may not close](https://www.linkedin.com/posts/rogerdunn_retailmedia-commercemedia-aicommerce-activity-7508853280601485312-qyLb)
- [The shortlist thesis, plus recognition and a weekly publishing rhythm](https://www.linkedin.com/posts/rogerdunn_retailtech-retailmedia-aicommerce-activity-7505570287904804864-_i4E)
- [Friday ≈ Memeday: the lighter format that carries the same subject](https://www.linkedin.com/posts/rogerdunn_retailmedia-retail-media-activity-7511583477352468480-UAdJ)

## Brand relationships

- **Thrad** (employment)
- **TikTok** (mention)
- **OpenAI** (mention)
- **McDonald's** (mention)
- **Meta** (mention)
- **Retail Technology Innovation Hub** (mention)

## Partnerships

Dunn's partnership surface is their own publishing, not a published sponsorship programme. They write The AI Commerce Brief, a weekly post series, and say Thrad builds ways for brands to appear inside AI shopping answers and for retailers to monetise the conversational surfaces they stand up themselves. The posts analyse agentic commerce, retail media and buying agents. They also offer a comparison table of seven shopping agents as a PDF through the comments and take newsletter sign-ups through a link in the comments. The posts include no sponsor policy, media kit, rate card, audience specification, editorial-terms document or partnership inquiry route. Any arrangement would need to start with a direct LinkedIn message and be negotiated privately. Ask whether they accept sponsored placements in the brief or offer paid advisory work, and what terms apply.

## Alternatives

- Jason Zollan: Choose Zollan when the buyer prioritises vendor-side proof that a retail media network can be built and made to perform, and platform-scale numbers will carry the argument. The posts give an operator's account of building retail media on a commerce search and product discovery platform, the Gartner quadrant recognition for that platform, and specific figures — more than $120bn in ecommerce GMV flowing through it, 122% year-on-year growth in active advertisers, nearly 3,000 brands advertising, and 96% customer retention — plus the claim that the network beats legacy retail media on relevance, fill rate and ROAS. Zollan also speaks on retail media at Constructor's own retail summit, addressing retailers as a practitioner rather than a commentator. For a vendor selling the infrastructure beneath retail media, the framing — a network is only as good as its discovery engine — makes the same argument more directly than Dunn does.
- Max Sinclair: Choose Sinclair when the buyer wants tactical, brand-side execution on AI-driven product discovery rather than market-level analysis. Both posts offer practical instruction. One ties the arrival of a high-profile open-source agent developer at OpenAI to what it means for consumer brands, arguing that when agents handle routine purchases, visibility moves from shelf placement to algorithmic preference, and brands must optimise how their products appear in AI recommendations and connect their commerce systems to agentic protocols. The other reads Walmart's tighter product-title rules and the Sparky shopping agent as a signal that clean, structured product data is the raw material agents need, and tells brands to get their highest-volume features into the title formula first. This is closer to campaign instruction than commentary, making it a fit for a vendor selling catalogue, feed or product-data tooling.
- Vincenzo Toscano: Choose Toscano when the programme is anchored in Amazon advertising and European marketplace expansion rather than agentic commerce. Both posts come from that world: a recap of a London Amazon EU Accelerate meetup and a keynote at the Amazon Ads EU Business Accelerator. The advice is operational — treat Q4 advertising as audience-building rather than extra spend, use Amazon Marketing Cloud and DSP to see who actually buys, protect margin before cost-per-click spikes, and retarget into January — alongside cross-border expansion, AI in advertising and operations, and fulfilment changes such as FBA Select and AWD. A vendor selling to Amazon sellers and brands, or into the EU seller community, gets a speaker with agency leadership, a client base and an event platform where its brand could appear.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/