Podcast Summary
What is the single biggest macro anxiety that digital shelf and brand leaders are bringing to you right now?
That’s what Shreshta Joy asked Aaron Conant, the Co-Founder and Chief Digital Strategist at BWG Connect.
He speaks with 20 to 30 brand leaders every week, collecting their challenges, wins, and partner experiences and redistributing that knowledge across a growing network of digital commerce professionals.
Aaron Conant brings a rare bird’s-eye view to digital commerce. He lays out the anxiety stack facing digital shelf teams right now: agentic commerce is the big existential question, but LLM visibility is the prerequisite nobody can skip. Content strategy has been flipped on its head, with images now doing the conversion work and text needing to speak to machines, not just shoppers. Meanwhile, omnichannel profitability has become a survival exercise, and retail media measurement remains a problem so complex that even the largest CPG data science teams cannot solve it alone.
Through it all, Aaron keeps returning to one theme: the brands that build real networks, talk to real peers, and set aside real budgets to experiment with AI are the ones that will come out ahead.
Episode Highlights
03:06 What Is the Biggest Challenge for Brand Leaders Right Now?
04:39 How Should Brand Content Evolve for LLMs and AI Agents?
09:11 Are Amazon and Walmart Changing Listing Rules for Agentic AI?
13:09 Omnichannel Visibility vs SKU-Level Profitability
16:48 Do Brands Still Copy-Paste Amazon PDPs to Walmart?
19:09 How Should Brands Measure Retail Media Incrementality?
24:30 Common Mistakes in Digital Shelf Tech Stack Selection
29:00 How to Separate Real AI Tools from Marketing Fluff
Key Themes
Here are some of Aaron’s hot takes on agentic AI, AEO, and retail media:
Agentic Commerce Starts with LLM Visibility
Aaron identifies agentic commerce as the number one macro anxiety across the brands he talks to. The idea that personal shopping assistants will eventually make purchases on behalf of consumers is reshaping how brands think about discoverability. But Aaron frames it bluntly: if your brand is not getting recommended inside the LLMs today, no agentic AI is ever going to buy your product. AEO, SEO, and GEO are not separate from the agentic commerce conversation. They are the foundation.
The Content Flip: Images Convert, Text Talks to Machines
Great images draw shoppers in, keyword-stuffed text handles the rest. Aaron explains why that formula is dead. Shoppers today barely read the text. They scroll images. So conversion-driving claims like “lifetime warranty” need to live on the image itself. Text, meanwhile, has a new job. It needs to be structured and readable for LLMs, which skim text but do not process images at scale. Keyword stuffing actually hurts now because LLMs register a term once and move on.
Omnichannel Profitability Is No Longer Optional
Aaron traces a familiar arc: brands that existed comfortably in retail scrambled online during COVID, dumped their entire catalog onto Amazon, celebrated the revenue, and then discovered that marketplace take rates had eaten their margins. The correction happening now is a return to SKU-by-SKU profitability analysis. Smart brands are asking whether a kit, a bundle, or a co-marketing arrangement could turn a losing SKU into a winner. Beyond that, Aaron sees a more sophisticated play emerging: building marketplace-specific assortments the same way brands have always used different pack sizes to differentiate between a Walmart shelf and a Target shelf.
Retail Media Measurement Remains the Hardest Problem
Aaron sets aside the incoming disruption from AI and agentic shopping to focus on the present-tense chaos: brands are struggling to figure out where their next dollar of retail media spend actually moves the needle. The old model of seven marketing touch points before a purchase has ballooned past 40. Consumers research on one platform and buy on another, which makes last-touch attribution almost meaningless. Aaron shares a telling example: a consumer electronics brand advertising on Best Buy might feel like it is wasting money, until it turns those ads off and watches Amazon sales drop.
The AI R&D Budget and the Cost of Falling Behind
When it comes to evaluating AI tools and vendors, Aaron is refreshingly honest: 95% of what is out there right now is garbage, and 5% is genuinely transformative, but nobody can reliably tell which is which yet. R&D budgets exist precisely because most experiments fail, and the rare successes justify the entire investment. Brands need to carve out a dedicated AI R&D budget, get their CIO and CSO involved for security and governance, and give their teams permission to experiment for three to six months with new tools, platforms, and even emerging agency models like ChatGPT-focused agencies or Reddit optimization services.
Quick Takeaways for Brands
Here’s what brands should take note of to prepare for the digital shelf in 2026 and beyond:
Work on AEO and LLM-Optimized Content
If your brand is not showing up in LLM recommendations today, agentic commerce readiness is irrelevant. Start with AEO and LLM-optimized content before worrying about agents buying on your behalf.
Optimize Content to Cater to Search Intent
Move conversion-driving claims (warranty, certifications, key differentiators) into your images. Restructure your text to be clear, non-repetitive, and machine-readable. Keyword stuffing now works against you in an LLM-driven discovery environment.
Track SKU Profitability
Conduct a SKU-by-SKU profitability analysis across every marketplace you sell on. Be willing to kill unprofitable SKUs, or explore kits, bundles, and marketplace-specific assortments to turn them around.
Boost Omnichannel Strategy
Each marketplace deserves native optimization investment. Copy-pasting Amazon PDPs to Walmart.com and calling the results a failure is a self-fulfilling prophecy. Budget and optimize accordingly.
Get Started on AI R&D Budget
Carve out a dedicated AI R&D budget. Let your team test tools, agencies, and channels like Reddit for three to six months. Get your CIO involved, hire curious people, and accept that most experiments will fail. The few that work will be worth it.
Aaron Conant’s perspective is shaped by volume. He is reporting what hundreds of them are telling him every month. The through-line across every topic in this conversation is the same: the playbooks that worked even 18 months ago are breaking down.
Content, profitability, retail media, agentic AI. All of it is in flux. The brands that will come out ahead are the ones investing in real networks, real experiments, and a willingness to move before the answers are fully clear.
Disclaimer: The content shared in the Digital Shelf Insider Podcast by MetricsCart is for general informational and discussion purposes only. The insights, opinions, and perspectives expressed by hosts and guests are their own and do not constitute professional advice, recommendations, or endorsements by MetricsCart or any affiliated entity.
