Agentic AI is rapidly transforming e‑commerce – from proactive recommendation engines to autonomous commerce agents that complete transactions. As industry experts note: “Agents can automatically adjust pricing, personalise offers, build product bundles, answer order inquiries, and more”—ushering in a new era of real‑time, intelligent commerce (Salesforce).

A consumer’s query is no longer constrained to a single store or brand. For example, if someone says, “I need a full outfit for a wedding this autumn – dress, shoes, accessories,” an AI agent will surface a combination of products that best fit the brief—potentially across multiple brands or merchants. This is already evident in ChatGPT’s shopping demo, where product carousels for queries such as “best smartphones under $500” include results from various retailers (e.g. Samsung.com, BestBuy), shown side by side.

Breaking Down Multi‑Brand Silos

For multi-brand retailers, AI assistants can become powerful integrators—capable of searching the entire brand portfolio and presenting customers with relevant options, regardless of the brand site they initially landed on.

From a technical architecture standpoint, enabling this requires unified access to product and customer data, and potentially, unified commerce services. Many large retail groups today still operate each brand on separate websites, with independent databases and checkout flows.

To an AI agent, these silos are friction points. A truly user-centric agent doesn't recognise internal divisions—it’s optimised to fulfil the user's request. Retailers should therefore consider building a shared layer that aggregates product catalogues and inventory across all brands, so that agents (whether proprietary or third-party, such as ChatGPT) can query a single unified source.

For example, if a company owns both Brand A and Brand B, and a customer asks an agent on Brand A’s site for a product only available at Brand B, the agent should still surface Brand B’s item—if it’s a better fit. Architecturally, this could mean exposing cross-brand APIs or implementing a headless commerce platform that sits across multiple backends.

The resulting experience enables the AI to present cross-brand results in one interface or chat, increasing the chances of conversion within the retailer’s ecosystem—rather than losing the customer to an external competitor.

Multi‑Brand Transactions: Faster and Unified Checkout

If an AI agent assists a customer in selecting products from both Brand A and Brand B in a single session, can it facilitate a single, unified checkout?

The ideal experience would say: “I’ve added both items to your cart – would you like to check out once for all of them?” But this is non-trivial where brands operate independent checkout systems. Solutions may include shared payment wallets or back-end integrations that enable cart and order merging.

We are already seeing early signals of innovation. The PayPal and Perplexity partnership is one such example—PayPal’s account linking could theoretically allow users to pay multiple vendors in a single flow, with all the heavy lifting handled by PayPal’s backend (Reuters). Similarly, Microsoft’s Copilot for Shopping introduces a concept where merchants “set up shop” within the assistant, enabling a unified commerce experience.

Aggregated Data for Hyper‑Personalisation

Another key opportunity for multi-brand groups lies in personalising experiences using data aggregated across all customer touchpoints.

AI agents perform best when they have access to rich data—preferences, past behaviours, transaction history, and so on. A retail group with multiple brands can build a much richer customer profile by unifying insights. For instance, if a customer buys children's furniture from Brand X and women's clothing from Brand Y, those preferences can be applied when they're browsing on Brand Z—so long as proper data consent is in place.

This group-level personalisation turns a multi-brand retailer into a mini-Amazon, leveraging depth and breadth of customer intelligence. Emerging tech players, such as Mason Retail Group, are already enabling AI-driven hyper-personalised storefronts across brand portfolios.

A Note on Trust and Privacy

There are, of course, limitations and risks that require active management.

Privacy must be prioritised—users need to trust how their data is used and retain control over their preferences. Additionally, recommendation diversity should be maintained. If a company owns multiple brands, the agent shouldn’t unfairly prioritise one over another unless it best serves the customer’s request.

There is also the issue of algorithmic influence. As one LinkedIn commenter rightly observed, if AI agents become dominant gatekeepers, brands may begin trying to ‘game’ the system, just as they do today with SEO or app store rankings. This could create pressure on AI platforms to allow paid placement in future agent suggestions—despite OpenAI’s current stance of no ads. Such trends may complicate the integrity of personalisation models.

Strategic Technical Roadmap

At The Commerce Team Global, we work with enterprise retailers to assess, plan and implement AI and agentic commerce strategies that integrate seamlessly into existing ecosystems.

To prepare your multi-brand business for AI-driven commerce, we recommend the following:

✅ Unify Product & Customer Data

Implement a centralised PIM (Product Information Management) and CDP (Customer Data Platform) with standardised taxonomy across brands.

✅ Deploy Headless, Composable Architecture

Use API-first front-ends and orchestration layers that allow agents to query and present cross-brand results in real time.

✅ Build or Integrate Agent-Ready Checkouts

Explore shared wallet systems or third-party tools (e.g. PayPal-powered conversational checkout flows) to support multi-brand, single-checkout experiences.

✅ Pilot a Group‑Wide AI Assistant

Create a chatbot at the umbrella brand level to help customers find products across all your brands. This provides a better customer experience and surfaces valuable data for improving cross-brand personalisation.

✅ Prioritise Governance & Audibility

Embed governance, transparency and A/B testing into your agentic flows. Monitor agent behaviour to prevent algorithmic bias and ensure decision integrity.

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To learn how your brand can leverage AI across its commerce portfolio, contact us at:
📧 info@thecommerceteam.com

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