How agentic AI might redefine the customer journey through autonomous shopping

Imagine a household running low on laundry detergent: instead of the family noticing, searching online, and placing an order, their agentic AI detects the need, compares available options, looks for deals, applies loyalty discounts, and pays for the detergent – which is then delivered to the doorstep without requiring any human intervention.

Following digitization and the use of AI, agentic AI will be the next step in transforming the shopping experience. In this context, agentic AI refers to autonomous AI systems that don’t just assist customers with product recommendations or comparisons but actually take action on their behalf: this means handling the entire shopping process from identifying needs and making purchase decisions to completing purchases, including payments. Unlike traditional shopping, where customers research and transact themselves, or even AI-assisted shopping with generative AI (e.g., asking a chatbot for product ideas), agentic shopping is proactive and self-directed.

Consumers today show both curiosity and hesitation when it comes to trusting agentic AI with their shopping journey. And agentic AI is still a completely different ball game than generative AI. While AI familiarity is already high – 72% of consumers say they’ve used AI tools – only 10% have gone as far as making purchases with them, typically small-ticket groceries or household goods, according to a survey on agentic AI of 2.000 U.S. consumers from Bain & Company.

The sticking point is trust: just 24% feel comfortable letting AI complete purchases. The top concerns when autonomous AI takes over shopping include

• security of payment information,
• privacy of personal data,
• the risk of “AI misunderstanding needs”, and
• the failure of AI to recognize or prevent fraudulent activity.

To build trust in AI agents, consumers need companies to guarantee strong security, privacy and fraud protection as well as to ensure accuracy in meeting their needs. Still, many are open to the idea: 64% have used or would consider using AI for transactions.

Based on the Bain & Company survey, consumer trust is highly brand-dependent. 39% feel more comfortable if the AI is tied to a familiar payment provider like Apple Pay, PayPal, or Google Pay, while only 16% say the same about their bank or credit card company. Even brands related to digital shopping (such as Amazon Alexa or the Google Assistant) and AI-centered brands (such as ChatGPT or Perplexity AI) are trusted more than financial institutions.

What would tip the scales further are tangible consumer benefits such as

• convenience,
• cost savings and access to exclusive deals, or
• better product matching and recommendations.

Once AI agents become established, consumers can look forward to a shopping experience that is faster, smarter, and more personalized than ever before.

AI tools have long supported parts of the shopping journey, but AI agents mark a new era, delivering true automation by taking over the entire process end to end. Consumers can simply set their goals, preferences, and limits once. The agent then independently searches, evaluates, and buys the best-matching products.

With an AI agent taking over, shopping stops being a time sink. Instead of clicking through the forms in the online shop and manually entering their address and payment details, the AI agent handles everything autonomously and in a fraction of the time.

Because the AI learns from past purchases and preferences, it curates shopping lists and recommendations tailored to the individual. Consumers could provide their AI agents with highly personal information such as calendars, chat histories, social media activity, dietary habits, or travel plans so that the agents can shop for them. This enables hyper-personalization without having to share detailed, sensitive information with retailers or brands.

Beyond convenience, the agent also works as a tireless bargain hunter, 24/7. It scans across platforms to spot discounts, apply vouchers, and secure the best possible prices.

No forgotten items, no last-minute rushes, no tedious comparisons. The agent ensures accuracy, efficiency, and peace of mind with every purchase. Stock management becomes seamlessly organized, making the frustration of missing everyday essentials a thing of the past. Looking ahead, connected refrigerators or smart shelves could communicate directly with AI agents, ensuring homes are effortlessly replenished.

While the vision of agentic AI transforming shopping into a seamless, automated process is compelling, there are significant limitations and unanswered questions that challenge its broader adoption – particularly for emotionally charged or high-consideration purchases.

Agentic AI holds significant promise in specific use cases in the shopping journey, namely where convenience, efficiency, and low emotional investment align. However, in other parts of the journey – particularly where trust, taste, and emotional value matter – human judgment will probably remain indispensable.

Agentic AI is especially well suited for bottom-of-the-funnel scenarios, where shoppers are acting with high intent, but with low emotional involvement, and a clear goal. For repetitive or low-stakes purchases, automation is a welcome time-saver rather than a risky trade-off.

Routine reordering and consumables

• Replenishing groceries, cleaning supplies, personal care items, and pet food.
• Automatically purchasing items like coffee pods or dishwasher tablets when supplies run low.
• Managing subscriptions for vitamins, razors, or baby products based on usage patterns.

Problem-solving and utility purchases

• High-intent product searches like “replacement printer toner XYZ same day delivery” or “cheapest pack of twenty AAA batteries”.
• Agent completes purchases based on clear functional criteria: availability, price, delivery time, and reliability.

Price monitoring and deal optimization

• Tracking price drops, comparing offers, applying promo codes automatically.
• Timing purchases to align with discounts or seasonal sales.

When purchases involve personal style, higher cost, or emotional value, the idea of giving up control becomes more problematic. Trust, taste, and context are harder for AI to interpret fully.

Fashion and style

• Choosing clothes, shoes, or accessories: Style is deeply personal. A single “wrong” recommendation can break trust. Shoppers often want to browse, compare fits, colors, and trends.

Luxury goods and big-ticket items

• Furniture, electronics, jewelry, or high-end skincare: These are high-stakes purchases. Consumers want to read reviews, evaluate quality, compare specs, and emotionally connect with the product before committing.

Experiential purchases

• Booking vacations, events, or tickets to concerts: Such choices are not purely logical, they involve mood, timing, aspirations, and social context. A “bad” vacation can’t be easily returned.

Agentic AI promises to reshape the way consumers shop. The idea of IoT fridges ordering the ingredients for our meals is not new but it certainly hasn’t found its way into every household either, although technologically possible. Many shopping use cases aren’t just transactional, they involve very human and illogical factors. This makes it unlikely that fully autonomous agents will replace consumer decision-making wholesale, especially for high-stakes or personal purchases. Instead, agentic AI will find its strongest role in routine, repetitive, and low-emotion buying where convenience outweighs the risk of getting it wrong.

However, visions can be imagined for these applications where devices and households are networked with AI agents. The smart home knows when there is a need for certain products, orders them independently, and opens the garage where the delivery robot parks the delivery.