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The Agentic Commerce Readiness Checklist

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An AI agent does not browse. It queries, and it recommends only the products it can read. Is yours one of them?

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The way consumers find products has changed. They no longer scroll category pages. They ask an AI agent to find the best high-protein snack under 200 calories, or the frozen meal with the cleanest ingredient list. The agent answers with whichever products are legible to it, not the most popular or the best-funded, but the ones whose data it can parse, evaluate, and confidently recommend.

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This checklist gives you an honest read on whether your products are ready. Eighteen questions across the three layers of product intelligence that decide whether your brand is visible in AI-driven commerce. No partial credit. Score yourself, find your weakest layer, and see exactly where the gap is.

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Download the full checklist here.

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What This Checklist Covers

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Eighteen yes-or-no questions, six per layer, that map your readiness across the three layers of the agentic commerce foundation:

• Attribute Intelligence, the foundation. Can AI agents find and evaluate your products? Structured, machine-readable attribute data at the UPC level, consistent across every retailer and channel.
• Demand Intelligence, the strategic direction. Are you building toward where demand is going? Attributes clustered into commercially meaningful Demand Themes, sized by actual sales and benchmarked against your competitive set.
• Consumer Intelligence, the voice of the shopper. Do you know the exact words that make your product worth recommending? Reviews analyzed at the attribute level, surfacing the language shoppers use and the territories competitors own.

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The checklist includes a scoring page that places you in one of three readiness tiers, with guidance on which layer to prioritize first.

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How to Use It

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Answer each question yes or no. The capability is either in place or it is not. Tally your yes answers at the end of each layer and overall, then prioritize your lowest-scoring layer first, because it limits the effectiveness of every layer above it.

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Most enterprise CPG brands score between 8 and 11. That is not a comfort. It is competitive exposure. The brands scoring 14 and above are the ones AI agents surface first in their category.

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Why This Matters Now

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This is not a future-state problem. The shift to AI-mediated discovery is already underway:

• 58% of consumers have replaced traditional search engines with AI for product research, up from 25% in 2023. (Capgemini Research Institute, January 2025)
• AI-driven traffic to US retail sites grew 4,700% year over year in 2025, the fastest-growing channel in ecommerce history. (Adobe Digital Insights, August 2025)
• $20.57B in US AI commerce spending is projected for 2026 alone. (eMarketer, December 2025)
• Grocery and CPG were named the highest-probability early category for agentic adoption. (J.P. Morgan)

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How Harmonya Closes the Gap

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Harmonya is Product Intelligence for CPG and retail, delivering all three layers of the agentic commerce foundation through a single data feed that integrates with the tools your teams already use.

• Attribute Intelligence: Enrichment structures 40M+ products across 17,000+ attributes at the UPC level, machine-readable, maintained, and mapped to every retailer and channel.
• Demand Intelligence: Attributes are clustered into Demand Themes, sized by actual sales and benchmarked against the competitive set, retailer by retailer.
• Consumer Intelligence: 103M+ consumer reviews are connected at the attribute level, surfacing the exact language shoppers use and the territories competitors own.

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These are not three separate projects. They are three layers of one foundation.

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Download the Checklist

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About Harmonya

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Harmonya brings together product attributes and consumer review language to help teams understand how shoppers, and now AI agents, evaluate products in real-world conditions. By structuring and quantifying the language consumers use, Harmonya connects early-stage shopper signals to validated performance outcomes, revealing what is changing across categories and why it matters.

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