Key Takeaways:
- AI product discovery in wholesale and multi-brand retail favors brands with clean catalog data, consistent attributes and verified trust signals across accounts.
- Buyers use AI to narrow consideration sets before committing; winning a spot requires strong reviews, trade coverage and credible third-party endorsements.
- Retailers pulling ahead have unified product data, connected media and inventory loops, and creative operations built to scale across multiple accounts.
- Brands still running isolated AI pilots risk falling behind on ranging, shelf placement and recommendation quality — data hygiene is the immediate priority.
AI Is Changing How Products Get Found and Bought
At a leadership breakfast hosted during the Cannes Lions festival by Particular Audience, retail and brand executives made one thing clear: the companies that get recommended, ranged and discovered will be the ones with clean product data, clear reputations and creative that adapts fast. For anyone operating in wholesale, distribution or multi-brand retail, that’s not a future concern — it’s a present one.
Leaders described a split inside many organizations. Some teams use AI cautiously while others run advanced optimization in operations. A manufacturer may tune factory lines with AI models, whereas a marketing team still hand-tags product attributes using spreadsheets. Treating AI as scattered pilots rather than a connected capability stalls value. Companies that build shared systems, shared taxonomies and shared workflows see faster gains in accuracy, speed and cost control. For brands selling across many retail accounts, that inconsistency shows up in ranging decisions and shelf placement.
The retailers getting the most from AI aren’t the ones talking the loudest. They built infrastructure first: unified product catalogs, consistent attributes and feedback loops connecting inventory, media and merchandising. Kroger is often cited for practical investment in tools that enable real shopping assistance rather than one-off demos. Clean, connected data still drives recommendation quality more than any single model — a lesson that applies equally to online marketplaces and physical buying floors.
What Does AI Mean for Buyers?
For purchasing managers and buyers, AI is compressing choices while raising relevance. Fewer options surface but the fits are better. That sharpens sourcing decisions, yet it also creates a trust test. Buyers prefer recommendations tied to recognized brands and verified suppliers, and most use AI to research before committing rather than to close in one step. To win a spot in a shortened consideration set, brands need consistent signals across reviews, trade coverage and credible third-party endorsements so systems can read a clear, trustworthy story.
On the selling side, product metadata, enriched catalog attributes and consistent images now carry outsized weight — systems match buyer intent against those signals. Standardizing naming, clarifying product benefits and resolving duplicate SKUs across accounts is no longer optional hygiene. Creative operations are changing too. Tools that resize, reformat and version assets to retailer specs reduce costs and speed launches across multiple accounts. Leaders also flagged the people dimension: as production tasks are automated, teams that shift upstream to strategy, testing and merchandising collaboration keep their edge.
What Should Brands and Suppliers Do Now?
Three focus areas came up consistently. First, fix data at the source. Clean product feeds, normalize attributes, link variants and purge stale entries, so systems can rank and recommend with fewer errors. Second, strengthen reputation signals. Encourage authentic reviews, respond to issues, surface third-party coverage, and ensure consistent claims across every channel and account. Third, modernize creative operations. Move to modular assets and clear naming conventions that adapt to each account’s placements to cut cycle time and reduce rework.
Retailers and brands with unified data engines — where media, loyalty and first-party data connect into one loop — are pulling ahead. AI accelerates an existing flywheel; it doesn’t build one from scratch. The gap between organizations that treat AI as a connected capability and those still running isolated pilots is already visible. The next quarter will reward those who prioritize data hygiene, brand trust signals and creative operations that scale across accounts without sacrificing human judgment.
(Note: AI assisted in summarizing the key points for this story.)