Agentic AI is reshaping online retail by moving commerce from AI-assisted shopping to AI-delegated decision-making. Instead of simply recommending products, AI agents can research, compare, select and even purchase goods on behalf of consumers according to predefined preferences such as budget, preferred brands, dietary requirements and delivery options. For example, an AI agent linked to a family’s grocery account can automatically reorder common household items like laundry detergent or breakfast cereal when supplies run low, ensuring products that fit the family’s brand, price and delivery preferences are selected. This emerging model, known as agentic commerce, is expected to have a significant impact on fast-moving consumer goods (FMCG), where purchasing decisions are often routine, repetitive and price-sensitive.
Technology companies have already begun implementing agentic commerce. Google is developing features such as Universal Cart, agentic checkout, and payment protocols that enable AI agents to shop across multiple retailers without consumers visiting individual websites. Similarly, Amazon’s enhanced Alexa for Shopping can research products, recommend alternatives, replenish household items automatically and complete purchases within user-defined limits. These developments position AI as an intermediary between retailers and consumers, fundamentally changing how purchasing decisions are made.
For FMCG businesses, this shift requires a rethink of traditional marketing and sales strategies. AI shopping agents are less influenced by brand recognition or emotional marketing than human consumers. Instead, they rely on structured, accurate and machine-readable information such as pricing, product specifications, ingredients, availability and promotions. Businesses with incomplete or poorly organised product data risk becoming invisible to AI purchasing systems. To improve data readiness, businesses should prioritise standardising product attributes across their ranges and ensuring each item is described consistently. Adopting industry data schemas, such as GS1 standards for product identification and specifications, can help ensure that product information is comprehensive and accessible to AI shopping agents. Regular data audits and updates, as well as collaboration with supply chain partners to improve data quality, are practical steps businesses can take to strengthen their visibility in the AI-driven marketplace.
The rise of agentic commerce also creates a range of legal and regulatory challenges. Privacy obligations remain governed by the Privacy Act, meaning businesses must manage customer data, shopping histories and AI-generated information carefully. Consumer protection laws under the Australian Consumer Law (ACL) continue to apply, requiring businesses to ensure product information, pricing, rankings and substitutions are accurate and not misleading, even where transactions occur primarily between AI systems.
Intellectual property presents additional uncertainty. Existing trade mark laws protect brands against misleading or deceptive use. Still, courts may need to reconsider how concepts such as consumer confusion apply when purchasing decisions are made by AI rather than humans. Businesses must also guard against counterfeit products that manipulate metadata or other machine-readable information to deceive AI shopping agents.
Agentic commerce further raises novel contractual issues. While Australian law recognises contracts formed through automated systems, disputes may arise where AI agents misunderstand instructions, select unsuitable substitutes or accept contractual terms without meaningful human review. Questions concerning agency, consent, mistake and liability are likely to become increasingly important as autonomous purchasing becomes more widespread.
- To prepare for agentic commerce, businesses should prioritise actions based on impact and urgency. Improving the quality, accuracy and structure of product data is the most critical first step, as reliable data underpins how AI systems select and present products. This foundational work enables all subsequent efforts and maximises visibility to AI agents. Other important initiatives include: assessing privacy and data governance risks associated with AI systems;
- reviewing supplier, customer and platform contracts to allocate responsibility for AI-driven transactions; and
- implementing audit trails that record AI decisions, the information relied upon, user consent and any human oversight.
Agentic commerce represents a fundamental evolution in digital retail. Organisations that adapt their data, governance and contractual frameworks now will be better positioned to capitalise on AI-driven commerce while managing the associated legal and commercial risks. As a practical next step, businesses should consider conducting an immediate readiness audit or launching a pilot project to assess how well their current processes, data quality and technology stack align with the requirements of agentic commerce. Taking early, concrete action will help identify gaps, build internal capability and ensure organisations remain competitive as this new model of commerce takes hold.
Source: dcc.com
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