AI agents in online retail: the future of e-commerce

The way online retail works is at the beginning of a new era - agentic commerce. This opens up new opportunities for retailers, for example in the area of warehousing or price optimization. AI agents will also take over many steps for shoppers in the future. This ranges from product searches and price comparisons to automated ordering. We explain what impact this will have on e-commerce and what hurdles still need to be overcome.

What are AI agents in e-commerce?

AI agents are software instances that use artificial intelligence and machine learning to autonomously perform tasks for users, both on the retailer's side and on the customer's side. They can independently manage stock levels, optimize prices and product descriptions, initiate orders, compare prices and availability in real time and make purchasing decisions based on user preferences and contextual data. Thanks to proactive analyses of calendar data, emails or previous sales and ordering habits, these agents can act without manual intervention.

Effects for retailers

Sellers benefit first and foremost from efficiency and precision. Reliable demand forecasts optimize inventory management and minimize overstocking and understocking. Suppliers like SnapSoft provide software solutions that use integrated market and competitor analyses to adjust prices in real time in order to optimize margins and sales. AI agents help with the presentation of items and can independently optimize product descriptions and images. Digital shelf management helps to automatically improve visibility on various platforms.

However, there are also disadvantages to consider. AI agents for buyers usually prefer popular, highly rated items, which makes it more difficult for less popular items or niche products. There is also a risk of losing direct customer contact, which can reduce the emotional bond with customers.

Effects for consumers

The biggest advantages for shoppers are time savings and individualized product selection. AI agents carry out automated product searches and browse countless stores and marketplaces simultaneously. They deliver tailored suggestions based on the user's behavior and life situation. For example, consumer goods such as drinks, pet food or medicine can be reordered as needed. Agentic Commerce replaces reactive shopping with proactive needs recognition.

However, this automation can also have a disadvantage, as users relinquish control to the AI agents. If they are not configured correctly, user requests can be ignored or incorrect orders triggered. The handling of personal data can also pose a risk.

Changes in the market

The transition to shopping via agents also means a radical change for the platforms. As service providers, they have to adapt to the new "customers". Marketplaces are emerging where AI agents compete directly with each other instead of consumers becoming active themselves. This is also changing the way products are presented. The focus will be more strongly on machine-readable data so that it can be perceived, read and used by the agents.

Small and medium-sized companies will generally not be able to do this themselves, nor do they have to. Software providers already offer a comprehensive range of tools to support retailers. AI-supported solutions are consistently being (further) developed here in order to be prepared for the future. Retailers who offer their products on major marketplaces such as Amazon or eBay benefit from the developments of the platforms. As an example of this, eBay announced a cooperation with OpenAI at the beginning of the year - We have reported on this.

What challenges still exist?

There are still a few, sometimes complex, hurdles to overcome on the road to agentic commerce. At the top of the list is data protection, as AI agents access highly sensitive information. Without clear consent, a comprehensive data protection impact assessment and transparent decision-making protocols, their use is risky.

There are also still challenges on the technical side. Seamless integration into ERP, CRM and store systems requires open interfaces, middleware for data harmonization (connection between system and application) and a scalable cloud architecture.

Of course, there are also questions about ethics and liability. The greater the autonomy of digital assistants, the more important clear governance structures become. Fair algorithms to avoid discrimination, structured evaluation processes to ensure fairness and ethical standards, and clear liability rules must be part of any AI strategy.

Last but not least, various AI agents for purchasing, warehousing and service must work together smoothly. Open data standards and coordinated interfaces are crucial here.

Conclusion

AI agents can bring a quantum leap to e-commerce and bring about a paradigm shift. They automate routine processes, manage stock levels and product presentations, provide tailored product suggestions and personalize the shopping experience. Both retailers and consumers can benefit from this. There are still some challenges ahead, but the change will be unstoppable. Those who set the course now will secure a long-term competitive advantage in the age of agentic commerce.