AI agents in B2B order entry: what will retailers' buying bots expect from brands?

KEY TAKEAWAYS Summary by the editors

1. Gartner predicted in October 2025 that by 2028, 90 percent of B2B buying will be AI agent intermediated, pushing over 15 trillion US dollars of B2B spend through AI agent exchanges.
2. For fashion wholesale, buying agents will first handle repetitive work such as re-orders, availability checks and order entry, while range selection and negotiation stay largely human.
3. Agents need machine-readable product data, live availability, customer-specific prices and terms, and an interface such as an API or EDI through which orders can be placed and confirmed.
4. Consumer-side standards such as Google's Universal Commerce Protocol, announced in January 2026, show how agent-to-merchant interfaces are being standardised, but B2B wholesale still largely runs on EDI and portals.
5. Brands should prepare by cleaning product and price data, exposing reliable availability and order confirmation, and defining which decisions an external agent may trigger.

Retailers' AI buying agents will expect brands to provide what any automated system needs: structured product data, live availability, correct customer-specific prices and terms, and a dependable way to place, confirm and change orders without a human in the loop. Brands that can only take orders by email, phone or a sales rep's tablet will be harder for these agents to buy from. The shift is starting with routine re-orders, not with the creative work of building a seasonal range.

What are AI agents in B2B order entry?

An AI agent is software that can pursue a goal over several steps, such as checking stock, comparing options and submitting an order, rather than just answering a question. In B2B buying, agents can act for the buyer (finding and ordering products within rules set by the retailer) or for the seller (reading incoming orders, answering availability questions and preparing confirmations).

Analysts expect this to grow quickly. In its predictions for 2026 and beyond, published in October 2025, Gartner forecast that by 2028, 90 percent of B2B buying will be AI agent intermediated, pushing over 15 trillion US dollars of B2B spend through AI agent exchanges, and said that verifiable operational data will become a prerequisite for participation. Such forecasts are directional and cover all B2B sectors, not fashion specifically.

Which buying tasks will agents take over first in fashion?

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What can conversational AI assistants do for B2B fashion buyers?

What will buying agents expect from brands?

• Machine-readable product data: consistent identifiers (such as GTINs), attributes, size scales, images and sustainability information, not PDFs and line sheets.
• Live availability: available-to-promise stock and incoming quantities with realistic dates.
• Customer-specific commercial data: the retailer's own prices, discounts, minimums and payment terms.
• A transaction interface: an API, EDI connection or agent-ready protocol through which orders can be placed and changed.
• Reliable confirmations: fast, line-level order responses that state what will be delivered and when.
• Clear rules: published limits, for example which order sizes or changes need human approval on the brand's side.

Much of this exists in EDI. The GS1 EANCOM order response (ORDRSP) already lets a supplier accept all, part or none of an order and propose amendments line by line. Agents do not make EDI obsolete; they raise the expectation that every brand, not only those supplying large retailers, can answer in a structured, timely way.

How are agent commerce standards developing?

The most visible standards so far are consumer-facing. In January 2026 Google announced the Universal Commerce Protocol (UCP), an open standard for AI agents to connect with business back ends for discovery, cart, checkout and post-purchase order management. According to InfoQ, it was developed with Shopify, Etsy, Wayfair, Target and Walmart and endorsed by more than 20 companies including Zalando, Macy's, Adyen, Stripe, Visa and Mastercard, with bindings for the Agent2Agent (A2A) protocol and the Model Context Protocol (MCP).

B2B wholesale has different needs: account-specific prices, credit limits, delivery windows and pre-order logic. It is not yet clear whether consumer protocols will be extended to these cases or whether B2B will rely on EDI, supplier APIs and portals with agent access. Brands should watch the standards, but not wait for them before fixing their data.

On the brand's side, agents are already practical for order intake. Many wholesale orders still arrive as emails, spreadsheets or photographed order forms. An AI agent can read these, match items to the product master, check availability and create a draft order for a person to approve. This reduces manual typing and errors, and it prepares the brand for a future in which more incoming orders are generated by retailers' own agents.

The same discipline applies in both directions: every automated order needs a traceable source, a validation step against prices and stock, and a clear confirmation back to the customer. Brands that build these controls for their own agents will find it easier to accept orders from external ones.

What are the risks for brands?

1. Invisible exclusion: if an agent cannot read a brand's catalogue or availability, the brand is simply not considered for a re-order.
2. Price transparency: agents compare systematically, which puts pressure on inconsistent price lists and terms.
3. Erroneous orders: an agent acting on wrong data can place large or duplicate orders; brands need limits and confirmation rules.
4. Authentication and liability: brands must verify that an agent is authorised by the retailer, and contracts should state who is liable for agent errors.
5. Loss of relationship: if all routine contact is automated, sales teams have fewer natural touchpoints with accounts.

Read also

What a good B2B ordering portal needs: a practical checklist

How should brands prepare now?

The preparation is largely the same work that improves human self-service. Clean and enrich product master data, expose reliable availability and customer prices through portals and APIs, make order confirmations fast and accurate, and define approval rules for orders that arrive automatically. McKinsey's 2024 B2B Pulse survey found that B2B customers already use an average of ten interaction channels; an agent is likely to become one more channel, and it will favour the suppliers whose data it can trust.

Frequently asked questions

What is an AI buying agent in B2B?

It is software that acts on behalf of a buyer to search for products, check availability and prices, and place or change orders within rules set by the buyer's company. It can work through supplier portals, APIs or EDI connections.

Will AI agents replace wholesale buyers in fashion?

Not in the near term. Agents are best suited to routine tasks such as re-orders, availability checks and order entry. Seasonal range selection, brand strategy and negotiations with key accounts still depend on human judgement.

What is the Universal Commerce Protocol?

It is an open standard announced by Google in January 2026 that lets AI agents connect with merchants' commerce systems for product discovery, checkout and order management. It was developed with major retailers and platforms and is currently focused on consumer shopping.

How can fashion brands make their products agent-ready?

By maintaining structured product data with consistent identifiers, exposing live availability and customer-specific prices, offering an API or EDI interface for orders, and sending fast, line-level order confirmations. Clear rules for which automated orders need human approval are also essential.

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