What retail buyers want from brand AI tools: a buyer-side view
Brands are adding AI to wholesale, but buyers judge it by speed, data quality and trust. This analysis sets out what the available evidence suggests buyers want.
KEY TAKEAWAYS Summary by the editors
- Buyers are likely to value AI tools that save time and improve the quality of product data and order information, not tools that merely automate the brand's sales pitch.
- The Le New Black summary of State of Fashion 2026 expects retailers to want structured attributes, consistent wording, clear categorisation and high-quality visuals from brands.
- JOOR's 2026 whitepaper reports buyers moving budget towards in-season purchases and stock ready to ship, and advises brands to offer flexible delivery dates.
- McKinsey B2B Pulse research found that buyers use many channels and want a mix of in-person, remote and self-service contact, so AI should add options and not remove access to people.
- Trust depends on accuracy and transparency: buyers will tolerate fewer errors from a tool that touches orders and prices than from one that suggests ideas.
Retail buyers are most likely to welcome brand AI tools that make ordering faster and more accurate and give them better information, and to resist those that push volume, hide terms or replace access to a person. There is little published research on buyer attitudes to brand AI specifically, so this analysis draws on adjacent evidence and is explicit about its limits.
What do buyers need from brands today?
Buyers manage many brands within a limited budget and calendar. According to JOOR's 2026 whitepaper, they are shifting budget towards in-season purchases and stock that is ready to ship, and the average time from order to shipping on its platform fell from 263 days in 2019 to 102 days in 2024. Le New Black's summary of the State of Fashion 2026 adds that buyers will scrutinise price points more closely, with value for money a top factor for consumers. Any brand tool is judged against these pressures.
A buyer's day also involves decisions that are not about information at all: how much risk to take on a new brand, which items fit a particular customer, how to balance exclusive lines against reliable sellers. AI tools can support these decisions with data, but buyers are accountable for the result and will not hand the judgement to a brand's system. Tools that respect this tend to be received better than tools that claim to make the decision.
Which AI features are likely to help buyers?
| Feature | Likely buyer benefit | Buyer concern |
|---|---|---|
| Reliable order status and delivery answers | Less chasing, quicker decisions | Wrong information |
| Structured product data and search | Faster comparison and range planning | Inconsistent attributes |
| Assortment suggestions | Ideas matched to the shop | Pressure to buy more or push the brand's priorities |
| Re-order prompts | Fewer stock-outs | Spam, unclear reasoning |
| Chat assistant in the ordering portal | Quick answers outside office hours | No route to a person |
The Le New Black summary notes that retailers will expect brands to supply structured attributes, consistent wording and clear categorisation, and that clean product data can speed up buyer decisions during market appointments. For a buyer, this is the most concrete benefit AI can support: good data in a consistent format.
A further feature is faster, more accurate re-ordering. Retailers that sell fast-moving items need to replenish quickly, and a clear view of availability and delivery dates reduces risk. JOOR's whitepaper reports that evergreen styles grew from 37 percent of its gross merchandise value in 2019 to 49 percent in 2024, which suggests that repeat and in-season buying is an important part of the picture, at least on that platform.
What will buyers be wary of?
- Persuasion over information: suggestions that appear to maximise the brand's sales rather than fit the shop.
- Opaque pricing or terms: any tool that seems to personalise prices without explanation.
- Errors in transactions: mistakes in orders, prices or delivery dates cost buyers money.
- Loss of personal contact: McKinsey's research found a roughly even preference across in-person, remote and self-service channels, so buyers want choice, not a replacement.
- Data use: unclear rules on how a retailer's order and sales data are used or shared.
Transparency about automation also matters. A buyer who receives a message should know whether it was written by a person, a person assisted by AI or an automated system, and should be able to reply to someone who can act. Hidden automation, even when well meant, damages trust if discovered later.
How do buyers' channel habits affect AI design?
The B2B Pulse survey found that the number of channels buyers use across their journey grew from five in 2016 to ten in 2022, and that buyers increasingly use mobile apps, social media and texting early in the process. It also found that 35 percent rated B2B e-commerce the most effective channel. The implication for brands is to make AI features available inside the channels buyers already use, with consistent information across them, rather than adding a separate tool for each.
Consistency across channels is a practical requirement. If the portal says an item ships in two weeks and the rep says four, the buyer will believe neither. AI features that draw on a single source of data for all channels reduce that risk, which is one reason data work usually has to come before conversational features.
The same logic applies to language and format. Buyers who work with dozens of brands benefit when product names, size scales and delivery terms are presented consistently, so a brand that uses AI to normalise its own data before sharing it does buyers a real service, even though they will never see the AI.
What should brands do with this?
- Ask a sample of buyers what slows them down and test tools against those answers.
- Prioritise data quality and accuracy before conversational features.
- Be explicit about how retailer data is used and who can see it.
- Keep a clear route to a named person at every point.
- Measure buyer outcomes, such as time to order, errors and satisfaction, and not only brand-side efficiency.
It also helps to separate buyer-facing features from internal ones. Much valuable AI work, such as checking price lists, cleaning product data and preparing rep briefings, is invisible to buyers except through fewer errors and better meetings. Brands should not feel obliged to show an AI feature to buyers to benefit from it, and should consider carefully whether a visible assistant adds anything the buyer has asked for.
What are the limits of this analysis?
The evidence is incomplete. The McKinsey B2B Pulse survey covers several industries and dates from 2022, JOOR's figures come from one platform and the State of Fashion summary is a secondary account. None directly asks buyers about AI tools from brands. Brands should treat the points above as hypotheses to test with their own customers.
A useful step is to involve buyers directly. Invite a few retailers to test a prototype, watch how they use it and ask what they would change. Observation often reveals friction that surveys miss, such as a login step, an unclear label or an answer that is correct but not useful for a purchasing decision.
Frequently asked questions
What do retail buyers want from AI tools offered by brands?
The available evidence suggests accurate order and delivery information, structured product data, quick answers and a clear route to a person, rather than automated persuasion.
Do buyers prefer self-service or sales reps?
McKinsey B2B Pulse research found buyers want a roughly even mix of in-person, remote and self-service channels, so most want a choice.
How can brand AI improve product data for buyers?
By enforcing consistent attributes, wording and categories and checking data before release. Le New Black's summary of State of Fashion 2026 expects retailers to want exactly this.
What would make buyers distrust a brand's AI tool?
Errors in orders or prices, unexplained personalisation, pressure to buy more, unclear use of their data and no access to a person.
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