Artificial intelligence is quickly moving from behind-the-scenes analytics to front-line decision-making. In e-commerce, this shift is taking the form of agentic commerce: AI agents that can interact with customers and even carry out purchases on their behalf.
For shoppers, these tools can feel like having a digital assistant who anticipates needs and manages routine transactions. For merchants, they hold the promise of higher sales and smoother customer interactions. Yet the rise of agentic commerce also brings hidden risks.
Transactions made by autonomous systems can lead to confusion, unmet expectations, and costly chargebacks. Let’s examine how AI agents are reshaping e-commerce, the ways they might contribute to chargebacks, and what merchants can do to prepare for a future where more transactions are carried out not by people, but by algorithms acting on their behalf.
Unlike traditional digital assistants that simply provide information, AI agents can take actions independently, including placing orders. Their autonomy is what makes them both powerful and risky.
Common functions include personalized product discovery, where the system recommends items based on past behavior; automated reordering of essentials; cross-platform price comparisons; and shopping assistance that goes beyond customer service.
Major players in the e-commerce world are experimenting with these capabilities. Amazon’s AI shopping assistant is currently in beta, while PayPal is developing an agentic commerce solution for merchants. This momentum suggests that AI-driven transactions are not a fringe experiment but an emerging standard.
The customer journey in e-commerce has traditionally been thought of as having three steps between its beginning and the point of purchase: awareness, consideration, and conversion. With AI agents, those steps are compressed. A machine can move from recognition of need to final purchase in seconds, sometimes without requiring active input from the customer.
For example, an agent linked to a household account may automatically reorder laundry detergent when supplies run low, based on past usage patterns. The customer may not even check the transaction, trusting the AI to get it right.
This shift means the line between impulse and intentional purchases becomes blurry. What once required deliberate human review now happens automatically, guided by preferences, algorithms, and past behaviors. While this can be efficient, it also leaves room for mistakes, mismatched expectations, and disputes if the customer later claims they did not agree to the purchase.
Chargebacks have been a persistent challenge for online merchants since long before agentic commerce entered the picture. A chargeback occurs when a customer disputes a charge with their bank, often claiming it was unauthorized or that the product wasn’t received. While chargebacks are meant to protect consumers, they carry significant costs for merchants, including lost revenue, fees, and potential penalties for high dispute ratios.
Agentic commerce introduces new dynamics that can increase these disputes:
These situations create a fertile ground for “friendly fraud,” where the customer technically authorized the transaction but disputes it anyway, often making false claims in the process. With AI in the middle, merchants face greater difficulty proving that the purchase was legitimate.
Merchants can’t control every aspect of AI agents, but they can prepare for the risks by adapting policies and processes. One important measure is transparency. For merchants using AI agents themselves, customers should be clearly informed when a transaction has been initiated by an AI system. Notifications, receipts, and emails can help reduce disputes rooted in surprise or confusion.
Another safeguard is giving customers meaningful controls. Opt-in and opt-out options, along with adjustable preferences, can help users feel they have agency over the agent’s behavior. Order confirmation emails can include cancellation links, so customers can reverse unintended purchases without resorting to a chargeback.
Guardrails for certain types of purchases are also worth considering. Merchants might require secondary confirmation for high-value orders or for products that historically carry high chargeback rates. While this adds friction to the process, it provides protection against disputes that could otherwise be costly.
AI agents are not a passing trend. As technology advances, they will likely become standard tools in digital commerce. Consumers are unlikely to turn away from systems that save them time and reduce routine decision-making, even if disputes occasionally arise.
Banks, payment processors, and merchants will need to collaborate on new standards for what constitutes valid authorization when an AI initiates a transaction. Traditional proof of intent may not suffice when disputes involve algorithmic decision-making.
The long-term hope is that AI agents themselves will become more sophisticated, learning to better align purchases with true customer intent. Over time, they may help reduce disputes by filtering out transactions that are likely to cause dissatisfaction. Until that point, however, merchants should anticipate higher chargeback risks and prepare accordingly.
The ability to analyze chargeback data will become more important than ever in the era of agentic commerce. By treating chargebacks not just as isolated disputes but as signals of where agent-driven transactions go wrong, merchants can turn a costly problem into a learning opportunity.
The best chargeback management companies can help merchants go beyond surface-level dispute resolution by providing in-depth analytics that uncover patterns and root causes. With their specialized tools and expertise, merchants gain clearer visibility into how AI-driven transactions contribute to chargebacks and how to reduce them.