Agentic Commerce Readiness Checklist for CPG Brands

AI shopping agents are already making purchase recommendations for your product category. Gartner projects these agents will influence more than $100 billion in purchases by 2028. McKinsey estimates AI-driven commerce will represent 15 to 20% of all ecommerce by 2030. The question isn't whether agentic commerce will reshape CPG. It's whether your brand will be visible when it does.

Most CPG brands aren't ready. Their product data is incomplete, their brand entity signals are weak, and they have no way to measure whether AI agents even know their products exist. Salesforce's 2025 State of Commerce report found that 39% of consumers are already comfortable letting AI agents make purchase decisions for them. That number will only grow. This guide gives you a concrete, scored checklist to assess your readiness and a 90-day plan to close the gaps.

Why CPG Brands Need an Agentic Commerce Readiness Plan

AI shopping agents create a new purchase channel that operates on completely different signals than Amazon search, Google Shopping, or retail shelf placement. And unlike those channels, you can't buy your way to the top. There's no "Sponsored AI Recommendation" ad unit. Visibility depends entirely on your product data, brand signals, and review quality.

When a consumer asks ChatGPT "what's the best probiotic supplement for bloating," the AI doesn't show a search results page with 10 options. It gives one to three specific product recommendations based on structured data, review signals, brand authority, and fulfillment trust. According to Jungle Scout's 2025 consumer trends report, 73% of US consumers already start product searches on Amazon. As AI agents increasingly intercept those searches, the brands that show up in AI recommendations will capture disproportionate market share.

This isn't a future problem. ChatGPT Shopping, Perplexity Shopping, Amazon Rufus, and Google's AI shopping features are live today, handling millions of product queries daily across ChatGPT's 800 million+ weekly active users alone. The invisible shelf is already here.

Here's what makes this different from every previous digital shift: you can't see it happening. When Google changed its algorithm, you could check rankings. When Amazon changed search results, you could search your keywords. But when an AI agent decides not to recommend your protein powder or your laundry detergent, there's no ranking page to check. The consumer never sees your product. They get three recommendations from the AI and they buy one. If you're not in those three, you've lost the sale before you knew there was one to lose.

The 6-Point Readiness Checklist Overview

Agentic commerce readiness for CPG brands comes down to six dimensions that AI shopping agents evaluate when deciding which products to recommend. No single dimension will make or break your visibility — agents weigh multiple signals together. But getting any one dimension wrong creates a bottleneck that limits the impact of the others.

Dimension	What It Covers	Why Agents Care
1. Product Data Foundation	Schema markup, product feeds, attributes, ingredients	Agents need machine-readable data to match queries
2. Platform Presence	Where your products appear (Amazon, DTC, retailers, knowledge graphs)	Agents check multiple sources to verify recommendations
3. Brand Authority Signals	Entity mentions, citations, expert endorsements	Agents assess trustworthiness before recommending
4. Review and Social Proof	Review volume, sentiment, recency, platform distribution	Agents synthesize reviews to validate product claims
5. Pricing and Fulfillment	Competitive pricing, stock reliability, shipping speed	Agents include purchase details in recommendations
6. Measurement Setup	AI visibility monitoring, citation tracking	You can't optimize what you can't measure

Readiness Scoring Matrix

Rate your brand on each dimension using three maturity levels. Be honest about where you stand. The goal is to identify your biggest gaps, not to feel good about your score.

Dimension	Beginning (0-1 pts)	Intermediate (2-3 pts)	Advanced (4-5 pts)
Product Data	Basic product pages, no schema markup, ingredients in images only	Product schema on main pages, partial feed attributes, text-based ingredients	Full Product + NutritionInformation schema, complete feeds, all certifications in structured data
Platform Presence	Amazon listing only, no DTC product pages	Amazon + DTC product pages, partial retailer coverage	Amazon + DTC + major retailers + Google Merchant Center + knowledge graph entries
Brand Authority	No editorial coverage, no expert endorsements	Some press mentions, inconsistent brand information across platforms	Regular editorial features, expert endorsements, consistent entity data, Wikidata entry
Reviews	Reviews on Amazon only, low volume or outdated	Reviews on 2-3 platforms, moderate volume, some detailed reviews	Reviews on 4+ platforms, high volume, recent, detailed attribute mentions
Pricing/Fulfillment	Pricing not in feeds, slow shipping, limited availability	Real-time pricing on some channels, Prime eligible on Amazon	Real-time pricing everywhere, Prime + fast DTC shipping, broad retailer availability
Measurement	No AI visibility tracking, don't know how agents see products	Manual monthly checks on 1-2 AI platforms	Automated AI monitoring across all platforms, baseline metrics established

Score interpretation: 0 to 10 = Not agent-ready (major gaps to close). 11 to 20 = Partially ready (foundation exists, significant optimization needed). 21 to 30 = Agent-ready (competitive position, ongoing optimization phase). In our experience, most mid-market CPG brands score between 8 and 14 on their first assessment. The biggest score jumps come from fixing product data (Checkpoint 1) and adding DTC product pages (Checkpoint 2) — these alone can lift your score by 6-8 points in 30 days.

Checkpoint 1: Product Data Foundation

AI-ready product data is the non-negotiable foundation of agentic commerce readiness. Without structured, complete product attributes, no amount of brand authority or review volume will make your products visible to AI agents.

Schema Markup Checklist

Your DTC product pages need JSON-LD markup using Schema.org types. For CPG products specifically:

• Product schema: name, brand, GTIN/UPC, description, image, offers (price, availability, seller)
• Ingredients: Full list in structured text, not images
• NutritionInformation: Calories, protein, fat, carbohydrates, fiber, sugar per serving
• Certifications: Using hasCredential or additionalProperty for USDA Organic, Non-GMO Project, kosher, etc.
• Allergen data: Explicitly structured (Contains: milk, soy, tree nuts)
• ProductGroup: Linking variants (flavors, sizes) together

Read our full guide on schema markup for AI for implementation details.

Product Feed Completeness

Your Google Merchant Center, Amazon, and retailer syndication feeds should include every available attribute field for your category. Common gaps we find in CPG feeds:

• Missing GTIN/UPC codes
• Generic product titles without key attributes ("Acme Protein" instead of "Acme Organic Whey Protein, Vanilla, 30 Servings, 25g Protein")
• No dietary/lifestyle attributes (gluten-free, vegan, keto, paleo)
• No certification attributes
• Missing size/count variants
• Outdated pricing or inventory status
• No usage instructions or dosage information for supplements
• Missing product comparison attributes (price per serving, price per ounce)

The fix is straightforward but tedious: export your current feed, compare every field against the full attribute list for your Google product category, and fill in every gap. Priority: GTINs, dietary attributes, and certifications — these are the fields AI agents query most for CPG products.

Amazon-Specific Data

If you sell on Amazon (and 73% of US product searches start there according to Jungle Scout's 2025 report), your listing needs: complete A+ Content modules with substantive text (not just lifestyle images), a populated Q&A section covering top consumer questions, backend search terms using all available character space, and complete Browse Node categorization.

Amazon Rufus draws directly from your Amazon catalog data to answer product questions. When a consumer asks Rufus "what protein powder has the least sugar?", it searches your bullet points, A+ Content text, and product attributes. If your sugar content only appears in a nutrition facts image, Rufus can't find it. The fix: put every factual product attribute in text form in your bullet points and A+ Content modules, even if it's also in images. Think of your images as the visual layer for human shoppers and your text as the data layer for AI agents.

Checkpoint 2: Platform Presence

AI agents check multiple platforms to verify product recommendations. Brands that appear consistently across platforms get recommended more confidently than those with a single-channel presence.

Platform	Why It Matters for Agents	Required Action
Amazon	Rufus uses Amazon catalog exclusively; ChatGPT and Google also crawl Amazon data	Complete listing with A+ Content, Q&A, reviews
DTC Website	Primary source for Perplexity and ChatGPT web crawling; full brand control	Product pages with schema markup, ingredients, certifications
Google Merchant Center	Direct input for Google AI Shopping features	Complete product feed with all CPG-specific attributes
Major Retailers	Retailer presence = trust signal; some agents check retailer availability	Accurate product data on Target.com, Walmart.com, Kroger.com, etc.
Google Business Profile	Brand entity verification for Knowledge Graph	Claimed, verified, complete business listing
Wikidata	Foundation of knowledge graph entity data used by multiple AI models	Entry if brand meets notability criteria

A common gap: brands that sell only through Amazon and retailers, with no DTC product pages on their own domain. This means Perplexity and ChatGPT have no brand-owned authoritative source to reference. Even if you sell primarily through Amazon, having detailed product pages on your own website significantly improves agent visibility.

The Multi-Platform Verification Effect

When we test AI shopping agents, we consistently find that products appearing on 4+ platforms get recommended at roughly 2x the rate of products on just 1-2 platforms. The reason: AI agents cross-reference sources before making a recommendation. If your organic protein powder appears on Amazon, your DTC site, Whole Foods, iHerb, and Google Merchant Center — all with consistent product data — the agent treats that as strong verification. If it only sees your product on Amazon with sparse listing data, the recommendation confidence drops. Think of each platform as a vote for your product. More votes, more recommendations.

Checkpoint 3: Brand Authority Signals

Brand entity optimization ensures AI agents recognize your brand as a distinct, trusted entity, not just a name that appears on some product listings.

Entity Consistency Audit

Check that your brand's name, description, founding date, headquarters, and key product claims are consistent across: your website (About page with Organization schema), Amazon Brand Registry, Google Business Profile, LinkedIn Company Page, Crunchbase, industry directories, and any Wikidata or Wikipedia entries.

Inconsistencies (different founding years, different company descriptions) erode agent confidence. AI systems cross-reference data points across sources, and conflicting facts make them less likely to cite you confidently. Fix these before investing in new content. The good news: this is a one-time cleanup that takes an afternoon and pays dividends permanently.

Third-Party Mentions

AI agents cross-reference your brand against independent, third-party sources before making recommendations. The highest-value mentions for CPG brands come from:

• Editorial review sites (Wirecutter, Consumer Reports, Allure, Bon Appetit)
• Credentialed experts (registered dietitians, dermatologists, pediatricians)
• Retailer editorial programs (Whole Foods trends lists, Sephora Clean program)
• Industry certification directories
• Award programs and industry recognition

Expert Endorsements

Build relationships with credentialed professionals in your category. A dermatologist who recommends your sunscreen by name or a dietitian who features your product in meal plans creates the AI brand visibility signals that agents trust most. Publish expert-contributed content on your site with clear author credentials. According to Qwairy's 2026 AI citation study, authors with visible credentials get 40% more AI citations than anonymous content — and that signal extends to the products those experts endorse.

Checkpoint 4: Review and Social Proof

AI agents synthesize reviews across multiple platforms. Review quality, recency, and platform diversity all factor into recommendations.

Review Volume and Quality

The minimum viable review profile for agent visibility: 50+ reviews on your primary platform (typically Amazon), with at least 10 reviews in the last 90 days. But volume alone isn't enough. Agents extract specific attribute mentions from review text. Reviews that say "mixes well in cold water, no gritty texture, mild vanilla flavor" are far more useful to AI agents than "Great product! Love it!"

Platform Distribution

Diversify your reviews beyond Amazon. Target review presence on: your own website (first-party reviews carry authority), retailer sites (Target, Walmart, Sephora, Ulta depending on category), category-specific platforms (Influenster, MakeupAlley for beauty; iHerb for supplements), and Google Business Profile.

Review Recency

AI agents weight recent reviews more heavily. A product with 3,000 reviews but none in the last 6 months sends a stale signal. Implement ongoing review generation programs, not one-time review campaigns. Ahrefs' research across 17 million AI citations found that AI-cited content is 25.7% fresher than what traditional search surfaces. The same recency bias applies to the product data and reviews that agents use when making shopping recommendations.

Encouraging Attribute-Rich Reviews

Generic five-star reviews don't help agents much. What helps: reviews that mention specific product attributes. "This protein powder dissolves completely in cold water, has 25g of protein per scoop, and the chocolate flavor isn't overly sweet" gives agents concrete facts to match against queries. You can encourage detailed reviews by sending post-purchase emails with specific prompts: "How was the taste? Did it mix well? How did it compare to your previous protein powder?" This doesn't violate any platform's review policies — you're asking for honest detail, not positive ratings.

Checkpoint 5: Pricing and Fulfillment Readiness

AI agents include purchase information in their recommendations because consumers expect actionable answers. Missing or outdated pricing data leads to exclusion from recommendations.

Real-Time Pricing

Your product feeds must reflect current pricing across all channels. Google Merchant Center, Amazon, and DTC prices should update automatically when you change them. Agents that show outdated prices lose user trust, so they deprioritize products with stale pricing data.

Availability and Fulfillment

Stock status matters. Products marked "out of stock" or "currently unavailable" get excluded from recommendations. And this isn't a temporary exclusion — agents learn patterns. If your product is frequently out of stock, agents start deprioritizing it even during in-stock periods because they've learned you're unreliable. Beyond basic availability: Amazon Prime eligibility, fast DTC shipping (2 to 3 days), and broad retailer distribution all increase recommendation probability. Agents want to recommend products consumers can actually buy quickly, and they get penalized (in user trust) when they recommend something that turns out to be unavailable.

Competitive Pricing

AI agents factor in price relative to the category. If the consumer asks "best organic protein powder under $40" and your product is $45, you're excluded regardless of quality. Make sure your pricing is competitive within the category segments you target. Price-per-serving or price-per-unit calculations in your product data help agents make accurate comparisons.

Subscribe & Save and Subscription Signals

For CPG products, subscription availability is becoming a meaningful signal. When an agent recommends a consumable product, it increasingly includes replenishment options in its response. Amazon Subscribe & Save eligibility, DTC subscription options, and auto-replenish features on retailer sites all give agents more to work with when answering queries like "what's the most convenient way to buy protein powder monthly?" If your product is subscription-eligible, make sure that data shows up in your feeds and schema markup.

Checkpoint 6: Measurement Setup

You can't improve your agentic commerce readiness without measuring your current AI visibility. Set up monitoring before you start optimizing.

AI Visibility Monitoring

Track your brand's presence in AI shopping recommendations across all major platforms. Monitor AI share of voice (how often you appear vs competitors), citation accuracy (are your product attributes described correctly), and recommendation position (are you the primary or secondary recommendation).

AI Radar automates this monitoring across ChatGPT, Perplexity, and Google AI. For Amazon Rufus, manual testing is currently required: ask Rufus product questions in your category and document the results monthly. Start with 20-30 queries that represent how consumers actually ask about your category: "best [category] for [use case]", "healthiest [category]", "[category] that actually works for [problem]", and direct brand comparisons.

The measurement gap is bigger than most brands realize. Ahrefs' analysis of 75,000 brands found that brand web mentions have the strongest correlation (0.664) with AI visibility — meaning what's written about you across the web directly affects whether agents recommend you. But most brands have zero visibility into what agents say about their products today. You could be getting recommended thousands of times or zero times, and without monitoring, you have no way to know.

Conversion Attribution

Set up analytics to track AI-referred traffic separately from organic and paid search. Look for referral sources from chat.openai.com, perplexity.ai, and Google's AI features in your analytics platform. This data shows you the actual revenue impact of improving agent visibility.

Competitive Benchmarking

Monitor not just your own visibility but your competitors'. If a competitor appears in AI recommendations where you don't, analyze their product data, reviews, and entity presence to understand what signals they have that you're missing.

90-Day Implementation Timeline

This timeline assumes you're starting from an "Intermediate" readiness level (score 11-20 on the matrix above). If you scored below 10, add two weeks to each phase. If you scored above 20, you're likely past the foundation stage and can focus on the optimization phase.

Phase	Timeline	Focus Areas	Key Deliverables
Foundation	Days 1 to 30	Product data, schema markup, feed optimization	Complete Product schema on all pages, updated product feeds, baseline AI visibility audit
Authority	Days 31 to 60	Brand entity, reviews, platform presence	Entity consistency fixed, review generation programs launched, DTC product pages live
Optimization	Days 61 to 90	Content authority, measurement, competitive gaps	Category content published, AI Radar monitoring active, first optimization cycle complete

Days 1 to 30: Product Data Foundation

• Audit all product pages for structured data completeness
• Implement Product + NutritionInformation + certification schema
• Update product feeds with all CPG-specific attributes
• Fix any ingredient data locked in images or PDFs
• Run baseline AI visibility test across 20+ category queries
• Set up AI Radar for ongoing monitoring

Days 31 to 60: Brand Authority and Reviews

• Audit and fix entity inconsistencies across all platforms
• Launch structured review generation programs (attribute-specific prompts)
• Create or complete DTC product pages with full data
• Pursue 2 to 3 editorial coverage or expert endorsement opportunities
• Complete Google Merchant Center and retailer feed optimization

Days 61 to 90: Content and Optimization

• Publish ingredient education and category comparison content
• Build FAQ content covering top 30 consumer questions in your category
• Run first competitive gap analysis (where competitors appear and you don't)
• Implement fixes based on first month of monitoring data
• Establish monthly AI visibility reporting cadence
• Update your readiness scorecard and compare against initial baseline
• Identify the top 3 queries where competitors appear and you don't — these become your priority targets for the next 90 days

For a deeper look at how AI shopping agents specifically evaluate CPG products, read our guide on how AI shopping agents choose products. For the broader AI visibility strategy, see our complete guide to AI visibility.

Common Readiness Gaps We Find in CPG Audits

After running readiness assessments for dozens of CPG brands, certain patterns repeat. These are the five most common gaps, roughly in order of how frequently we find them.

Ingredient data locked in images. This is the most common gap and the most fixable. Most CPG brands invested heavily in beautiful product photography for Amazon A+ Content and their DTC site. But the ingredient lists, nutrition facts, and certifications live exclusively in those images. AI agents can't read images (yet). They need text-based structured data. Extracting your ingredient panels into structured schema markup typically takes a developer 2-3 hours per SKU.

No DTC product pages with schema. Many CPG brands that sell primarily through Amazon and retailers have minimal DTC websites — sometimes just a homepage and an "About" page. Without dedicated product pages on your own domain, you're invisible to AI platforms that crawl the open web. ChatGPT and Perplexity can't crawl Amazon's internal catalog the way Rufus can.

Inconsistent brand entity data. The brand description on Amazon says "Founded in 2018 in Austin, TX." The LinkedIn page says "Established in 2017." The press kit says 2019. These inconsistencies erode agent confidence. A 30-minute audit across your major platforms usually reveals 5-10 discrepancies that take an afternoon to fix.

Reviews concentrated on a single platform. 95% of reviews on Amazon, zero reviews anywhere else. This creates a single-platform dependency where Amazon Rufus sees your product clearly but ChatGPT and Perplexity have limited review data to draw from.

No AI visibility baseline. You can't track improvement without knowing where you start. Most brands we work with have never tested what AI agents say about their products. The first step in every engagement is running 30-50 category queries across ChatGPT, Perplexity, and Google AI to establish a baseline.

Frequently Asked Questions

How long does it take to become agent-ready?

Most CPG brands can reach a competitive readiness level within 90 days if they start with the product data foundation. Schema markup and product feed optimization can be completed in two to four weeks and produce the fastest visibility gains. Brand authority building takes three to six months for meaningful impact. The timeline depends on your starting point and the number of SKUs you need to optimize.

Do I need to be selling on Amazon to be visible to AI agents?

No, but it helps significantly. Amazon Rufus only sees products in Amazon's catalog, so you need an Amazon presence for that platform. However, ChatGPT Shopping, Perplexity, and Google AI Shopping can recommend products from any retailer or DTC site with complete product data. Brands selling only through their own website can be agent-visible, but they need strong schema markup and broad review presence to compensate.

What's the minimum investment to prepare for agentic commerce?

The product data foundation (schema markup, feed optimization) can be done by a developer in 20 to 40 hours depending on your SKU count and platform. Review generation programs cost $500 to $2,000 per month through platforms like Yotpo or Bazaarvoice. AI monitoring tools like AI Radar start at accessible price points for mid-market brands. Total initial investment for a mid-market CPG brand: $5,000 to $15,000 for setup, $1,000 to $3,000 per month ongoing.

Can my existing product feed work for AI shopping agents?

Probably, but it likely needs enhancement. Most existing feeds were built for Google Shopping or Amazon, which have different attribute requirements than AI agents. The most common gaps: missing certifications in structured fields, abbreviated ingredient lists, no allergen data, and missing dietary/lifestyle attributes (vegan, keto, gluten-free). Audit your current feed against the checklist in this guide and fill the gaps.

How do I measure progress toward agent readiness?

Run the readiness scoring matrix in this guide monthly, tracking your score across all six dimensions. For visibility metrics, monitor AI share of voice (brand mentions in AI shopping recommendations), citation accuracy (whether agents describe your products correctly), and AI referral traffic and conversions. AI Radar provides automated tracking for most of these metrics. Aim for a 5-point improvement in your overall readiness score every 30 days.

Should I focus on Amazon Rufus or ChatGPT Shopping first?

If you sell on Amazon, focus on Rufus first because it draws from Amazon's catalog, where your existing listing data lives. Improving your Amazon product data, A+ Content, and Q&A section helps with Rufus immediately. ChatGPT Shopping requires a different approach: DTC product pages with schema markup, brand authority content on your own domain, and third-party editorial mentions. Most CPG brands should work both tracks in parallel since the Rufus improvements (weeks 1-4) can happen while you build DTC pages and schema (weeks 3-8).

Does my product's category affect agent readiness requirements?

Absolutely. Health, beauty, and supplement categories need more rigorous structured data than household goods. AI agents are more cautious about recommending ingestible products, so they look for certification data (USDA Organic, NSF Certified, third-party tested), ingredient transparency, and credentialed expert endorsements. For household and cleaning products, the emphasis shifts to safety data, environmental certifications, and value comparisons. Tailor your readiness priorities to your category's specific trust signals.