)
## The Moment Online Shopping Stopped Being Human-Centric
Online shopping used to be about seduction: glossy hero images, endless review scrolls, comparison tables, carefully tuned funnels. Today, it looks more like an arms race of spam, fake reviews, SEO games, and sponsored clutter.
Amazon says it blocked over 275 million suspected fake reviews in its store. Consumers are exhausted, not empowered. They’re hitting "buy again," subscribing to essentials, and delegating choices wherever possible. That behavior shift is precisely the opening AI has been waiting for.
Enter agentic commerce: AI-driven shopping agents that don’t just suggest products, but autonomously search, compare, decide, transact, and track on our behalf. As Mastercard’s chief AI & data officer Greg Ulrich explained to ZDNET, these agents transform shopping into a delegated task system — users express intent, and the software does the rest.
For big platforms, this is a feature. For small businesses, it’s an existential filter.
Because in an agent-driven economy, if the bots can’t read you — you don’t exist.
---
## From Search Boxes to Software Agents
Today’s e-commerce stack is human-facing by default:
- User searches for "ceramic pour-over coffee dripper."
- Marketplace displays thousands of options.
- User scans star ratings, parses reviews, squints at photos, checks delivery dates.
Agentic commerce inverts that model.
The workflow becomes:
- User: "Find me a durable, non-toxic, under-$40 ceramic pour-over dripper from a highly rated small business that ships within three days."
- AI agent: crawls APIs, marketplaces, feeds, schemas; scores trust; checks policies; picks; buys.
The UI is no longer your product page. The UI is the agent’s reasoning process.
Which means:
- Metadata beats marketing copy.
- Data quality beats design.
- Infrastructure owners (payments, identity, procurement, cloud, AI platforms) mediate which sellers are "safe" to surface.
This is where the power struggle becomes clear. Mastercard (Agent Pay), Visa (AI-enhanced cards), Oracle and SAP (AI in ERP and procurement), big cloud, and frontier AI providers are all building the rails on which these agents will run. Their goal is not just to help consumers — it’s to own the transactional substrate of digital commerce.
For small businesses, the question is brutally simple: how do you remain discoverable when your buyers are bots operating on someone else’s rails?
---
## Influencing the Algorithms: 7 Operational Rules for Staying in the Game
The original ZDNET piece frames it well: in the age of AI shopping, you’re effectively doing influencer marketing for robots.
Here’s what that actually means at an implementation level for technical and digitally mature SMBs.
### 1. Treat Data Formats as Law, Not Suggestions
Every platform you sell on already tells you how it wants data: fields, schemas, feeds, product attributes.
In an agentic world, those formats are no longer annoying admin work — they’re your primary distribution interface.
- Implement full structured data (Schema.org Product, Offer, Organization, FAQ) on your own site.
- Respect marketplace schemas (Amazon, Shopify, WooCommerce, Square, etc.) precisely.
- Avoid "misc" fields for critical attributes; if there’s a dedicated field, use it.
Agents will prefer suppliers whose data maps cleanly to known schemas. Messy data is friction; friction is de-ranking.
### 2. Ship Complete, Unambiguous Product Metadata
AI agents don’t skim lifestyle copy. They parse:
- GTIN/UPC/EAN
- Unique SKUs per variant
- Precise titles and canonical names
- Dimensions, materials, and technical specs
- Price, taxes, shipping options, SLAs
- Stock status and backorder rules
- Brand ownership and authenticity signals
When this data is missing or inconsistent across channels, agents either:
- Skip you as ambiguous, or
- Misclassify you (which is worse — you get surfaced to the wrong buyers or lose trust).
If you’re a technical lead at an SMB, treat product information like you’d treat API documentation: strongly typed, versioned, authoritative.
### 3. Wire in Real-Time APIs for Inventory, Pricing, and Policies
The next generation of shopping agents will expect:
- Live inventory feeds
- Real-time pricing and promotions
- Machine-readable return and warranty policies
- Shipping estimates with dynamic carriers and cutoffs
If your integrations are batch-based, laggy, or brittle, agents will learn that your data is stale. And they will remember.
Concretely:
- Use native APIs from Shopify/Amazon/etc. or build lightweight integration services that push updates instantly.
- Expose standardized feeds where partners (and later agents) can poll reliably.
This is classic DevOps and integration work — but now it directly maps to revenue exposure.
### 4. Understand That Bad Data Becomes a Permanent Scar
If an AI agent places an order it believes is "in stock" and the transaction fails, that’s not just a bad UX. It’s a training signal.
Agents will:
- Down-rank you as unreliable.
- Route future intent elsewhere.
There may be no human appeals process.
This is harsh, but technical teams know the remedy:
- Tighten source-of-truth systems for inventory.
- Implement health checks and monitoring for product feeds.
- Add automated tests validating that your storefront, feeds, and warehouse systems stay in sync.
Think of it as SRE for commercial trust.
### 5. Accept That AI Agents Will Score Your Reliability
Your "reputation" is becoming quantitative and machine-consumed:
- Delivery performance
- Refund and dispute rates
- Policy clarity and consistency
- Response time to issues
- Security posture and verified identity
Mastercard’s Ulrich describes their Agent Pay stack as a permissioned, verifiable transaction layer. That’s not just about fraud; it’s about programmable trust.
If you’re loose with policies, vague on pricing, or inconsistent across channels, agents may classify you as higher risk compared to a competitor with disciplined, machine-verifiable signals.
### 6. Enforce Ruthless Consistency Everywhere
Agents won’t just hit your main store. They’ll crawl:
- Marketplaces (Amazon, Etsy, Walmart, etc.)
- Social commerce listings
- Manufacturer catalogs
- Distributor feeds
- Affiliate databases
- PDFs, datasheets, and spec sheets
If your product is $39 on one channel, $45 on another, "in stock" here but "backordered" there, specs mismatched in a PDF — that inconsistency erodes trust scores.
Solve this like engineers, not marketers:
- Define a single canonical source of truth for product data.
- Automate propagation to all channels.
- Run scheduled diffs to detect drift across listings.
Consistency isn’t just brand hygiene anymore; it’s the feature that gets you through the AI procurement gate.
### 7. Build for “AI SEO” — Optimization for Non-Human Buyers
Search engine optimization is evolving into agent optimization:
- Clear, structured, machine-readable content
- Transparent business identity (addresses, legal entities, verified profiles)
- Strong security practices (HTTPS, DMARC, verified domains)
- Rich, high-signal documentation and FAQs
Think less about keyword stuffing and more about:
- "Can an LLM reliably answer: who are you, what do you sell, how reliable are you, how do you handle returns, and do you deliver on time?"
If the answer is yes — from your own data, not just scraped reviews — you’re building AI-native visibility.
The Trust War: Guardrails, Manipulation, and Infrastructure Power Plays
The optimistic pitch: agentic AI will shield consumers from fake reviews, counterfeit goods, dark patterns, and ad-choked interfaces, while optimizing loyalty points and return policies in the background.
The realistic view: any system that mediates money at scale becomes a target.
We’re already seeing content farms and coordinated disinformation operations tuned to influence LLM outputs. As soon as agents gain the authority to spend autonomously, a parallel industry will materialize to:
Game agent rankings
Poison training data
Spoof or hijack trust signals
So the payment giants and enterprise vendors are racing to wrap this in "verified intent" and cryptographic guarantees.
Mastercard’s Agent Pay is positioned as a trust fabric: every agentic transaction must be permissioned, traceable, revocable, bound to digital credentials. Oracle’s Hari Sankar, in his conversation with ZDNET, frames the power dynamic even more bluntly: agentic AI is only as strong as the data and processes it orchestrates — which means those who own the infrastructure where that data lives hold the leverage.
For developers and architects inside SMBs, this subtext matters:
Your e-commerce strategy is now inseparable from your data architecture.
Your choice of platforms (payments, ERP, commerce, hosting) dictates how legible and trustworthy you appear to third-party agents.
Deep infrastructure players will increasingly define the protocols of "acceptable" commercial behavior.
If that sounds like the platformization of trust, that’s exactly what it is.
When the Buyer Is a Bot, Don’t Act Like a Mall
The decline of traditional malls wasn’t sudden; it was structural. They froze their model while consumer behavior, logistics, and economics evolved around them. New formats — big-box, direct-to-consumer, same-day fulfillment — simply routed around the old stack.
Agentic commerce sets up a similar inflection point for everyone selling online.
For now, human-driven shopping isn’t disappearing. People will still obsess over cameras, bikes, servers, and weird niche gear. But routine purchasing — consumables, standardized parts, replenishments, B2B inputs — is primed for automation.
In that world, the winners share three traits:
They are machine-discoverable (structured, standardized, up-to-date).
They are machine-trustworthy (consistent, verifiable, low-friction).
They are machine-integrated (APIs, real-time feeds, interoperable with emerging trust layers).
Small businesses don’t need to outspend Amazon to compete here. But they do need to:
Treat product and policy data as production-grade infrastructure.
Invest in basic integrations rather than brittle manual workflows.
Design their presence for agents as seriously as they once did for search engines.
Because very soon, your most important customers will never see your homepage.
They’ll see your data.
And they’ll decide, in milliseconds, whether you still belong in the cart.
Source: Adapted and analyzed from ZDNET’s reporting on AI shopping agents and agentic commerce, including David Gewirtz’s “How small businesses can survive AI shopping: 7 essential steps” (Nov. 12, 2025).
