Today, we’re bringing you the latest in AI-powered marketing and business strategies. Here’s what’s inside:
🚨 AI Top Story: AI agents are about to start shopping, and Visa’s holding the keys.
🌟 AI Use Case Of The Week: ChatGPT just became Walmart’s latest sales channel.
🎯 Killer Marketing Prompt: A way to make your ad data talk back.
🎥 AI YouTube Resource Of The Week: The MFM guys break down the ChatGPT App Store gold rush.
Visa is building the trust layer for AI
This Could Totally Rewrite Ecommerce Rules
Visa is making a move that could flip how online shopping works. They’ve launched something called the Trusted Agent Protocol, a new standard that lets AI agents make purchases for you. Not recommendations. Not reminders. Actual purchases. Think ChatGPT or your voice assistant being able to securely buy a pair of sneakers or order dinner using your Visa credentials, without you needing to lift a finger.
Here’s the problem Visa’s trying to fix. Right now, most websites can’t tell the difference between a “good” agent acting for a real person and a malicious bot scraping prices or flooding checkout systems. The Trusted Agent Protocol is designed to solve that by giving legitimate AI agents a kind of verified ID. It’s backed by Cloudflare, Shopify, Microsoft, and Adyen, so this isn’t some side project; it’s Visa trying to become the trust layer for agent-to-merchant transactions.
The interesting part isn’t the protocol itself; it’s what Visa’s positioning tells us. For years, Visa has lived downstream of the marketing funnel, quietly facilitating payments while brands fought for attention upstream. This move pulls them into the value chain much earlier. If agents handle discovery, research, and purchase intent, Visa suddenly sits in the middle of that decision loop. They’re not just clearing payments anymore, they’re shaping how digital intent becomes a transaction.
That’s where it gets strategically huge. Whoever owns the infrastructure that agents trust will also control the data layer around purchase behavior, what products are being requested, by which kinds of agents, across which categories. For brands, that’s the new battleground. Not ads, not attribution models, but access to those intent signals flowing through the protocol.
Visa’s not trying to become a marketing company, but this puts them shoulder to shoulder with the platforms that are. They’re setting themselves up as the identity layer in the agent economy, and if that catches on, every brand, marketplace, and ad platform will need to play inside their framework. It’s a bold shift, and it shows how marketing infrastructure is now being defined by companies that used to have nothing to do with marketing at all.
🗂️ DAM is the missing link in AI-powered marketing success - MarTech argues that without organised digital asset management, AI tools can’t deliver real results. It’s a wake-up call for marketers racing ahead without fixing their content foundations first.
💬 Salesforce Angles Slack as Agentic OS for the Enterprise - Salesforce is turning Slack into a home base for AI agents that manage workflows, data, and customer interactions. A smart look at how enterprise marketing ops might soon run themselves.
🧠 WPP and Google forge groundbreaking partnership to redefine marketing with AI - WPP is going all-in with Google to build AI into every layer of its marketing stack. A must-read if you want to see how major agencies are operationalizing AI at scale.
💼 Can AI replace a CMO for cash-strapped startups? - Startups are testing whether AI can handle strategy, copy, and campaign planning when hiring a full-time CMO isn’t an option.
Walmart Joins ChatGPT’s Shopping Experiment
Walmart has just partnered with OpenAI to bring shopping directly into ChatGPT. Users can now search for products, check availability, and make instant purchases without leaving the chat interface. The integration connects ChatGPT to Walmart’s full product catalog and checkout system, letting people move from idea to purchase inside a single conversation.
It’s part of a growing set of native partnerships OpenAI is building with major retailers like Instacart and Shopify. In Walmart’s case, it’s a smart play: rather than waiting for shoppers to come to its app or website, the brand is embedding itself inside the places where discovery already happens. When someone asks ChatGPT for dinner ideas, back to school supplies, or home office gear, Walmart is right there in the flow of intent.
This isn’t a plug and play feature that any retailer can switch on. It’s the result of a direct collaboration between two giants, giving Walmart a first mover advantage in conversational commerce. What it really represents is a shift in where commerce happens, from search boxes and apps to AI driven ecosystems that anticipate what people want and help them buy it in real time.
How You Can Replicate Something Similar
Watch who gets to build in
Walmart’s deal shows AI platforms are selective. Monitor which merchants get access to commerce APIs from OpenAI, Google, Perplexity, and be ready when they open more slots.
Clean, AI-readable product data is a must
When APIs open, your structured feeds will matter. Brands already having clean metadata, SKUs, inventory, and schema markup will be easier to onboard.
Focus on distribution, not just your site
Walmart didn’t drive traffic into ChatGPT; it integrated into where people already ask questions. Think about how your brand could appear in those discovery layers when the gates open.
Build small, compliant experiments
You may not have full access yet. Test integrations via programs like Perplexity’s Merchant API or Shopify’s catalog API. These are real entry points today.
The Ad Performance Debugger
As marketing moves deeper into the AI era, one of the most useful ways to use LLMs isn’t for writing - it’s for diagnosing. This week’s prompt helps you turn ChatGPT, Claude, or Gemini into your own ad performance analyst. Feed it structured campaign data, and it will tell you why an ad underperformed, what might have caused it, and how to fix it - from creative angles to audience targeting.
You are a senior performance marketing analyst who specialises in diagnosing campaign performance using structured data and behavioural reasoning.
I’ll paste my ad performance data below. Your job is to analyse it, identify what’s underperforming, explain why, and suggest specific optimisations.
Here’s the information you’ll need:
Campaign goal: [e.g. lead generation, conversions, awareness]
Platform: [e.g. Meta Ads, Google Ads, LinkedIn, TikTok]
Target audience: [describe the audience or segments targeted]
Ad creative type: [e.g. static image, UGC video, carousel]
Performance data: [paste your ad data table or summary with impressions, CTR, CPC, CPA, conversion rate, etc.]
Copy and creative samples (optional): [paste headlines, captions, or scripts from top and bottom performers]
Time period: [e.g. last 14 days]
Budget context: [approximate spend range for each ad or campaign]
Now please:
Summarise performance trends and identify statistically meaningful differences.
Highlight which ads or audiences are underperforming and why that might be happening (e.g. weak hook, wrong audience intent, poor creative match).
Recommend specific optimisation steps to test (creative tweaks, new targeting logic, dayparting, platform adjustments).
Generate three alternative creative concepts or copy directions that could outperform based on the data.
Suggest one experiment plan for the next campaign cycle (include what to test, expected outcome, and measurement method).Example use:
Paste in your Meta Ads performance export for the last two weeks. Within seconds, the model breaks down your CTR, highlights why your remarketing ads are failing, and proposes three better creative angles—no dashboard toggling required.
Inside the ChatGPT App Store with the MFM Podcast
Get a tactical tour of the new ChatGPT app store, why discovery happens in-chat instead of a store page, and how to spot the early-mover opportunities.
Pick your path…
