Why Consumer Trust Will Decide the Winners in Agentic Commerce

Ecommerce is entering a new phase where consumers are no longer just searching for products leveraging AI, but are beginning to trust AI for advice, research, and even delegating decisions to the machine. Beyond serving up recommendations, AI has evolved to browse, compare, select, and even purchase products on a consumer’s behalf.

Agentic commerce promises maximum convenience and minimal friction. The tradeoff is more profound, though: it now requires a level of access and trust that people have historically been uneasy about offering. Consumers are increasingly experimenting with AI helping them purchase something low cost or a re-order, but are still hesitant to completely trust AI to actually shop for them, or instead of them. This distinction is the next era of agentic commerce; the level of trust for discovery and shopping versus a known purchase.

The companies that succeed with agentic commerce will be the ones that preserve a sense of consumer control while delivering speed and ease.

The Trust Problem Behind Agentic Commerce

One of the biggest concerns surrounding AI shopping agents is the depth of behavioral insight these systems can gather. They do not just track what you buy; they learn how you make decisions. 

Over time, AI systems can pick up on hesitation, price sensitivity, brand loyalty, and even subtle cues that suggest mood or intent. This creates a much more expanded personal profile that is far more revealing than a simple purchase history, and may be unknown and feel invasive to the average consumer.

The issue becomes even more complex because agentic systems rely on increasingly centralized pools of data to function effectively. For an AI agent to be useful, it often needs access to payment methods, location, calendars, emails, and preferences across many categories. This combination creates an understanding of the shopper’s need in that exact moment while being influenced by past engagements. The new ability is the conversation and questions that lead to an in the moment comprehension that is unparalleled from what was possible from clicks and past purchases. However, there is just as much opportunity for a poor outcome. The more capable the system becomes, the more data it requires, and the higher the stakes become if something goes wrong.

Another concern is the shift toward systems that are always present and always learning. These agents work best when they are ambient, continuously observing and updating information about the person in the background. That raises a familiar question about how much is being monitored and when helpful awareness starts to feel like passive surveillance. How common is it to think out loud about something and immediately the social and ad channels on our devices are responding with content and products related to the ‘thought’? This is now normal behavior to most of us.

When Personalization Starts Feeling Like Influence

There is also a more subtle risk emerging around influence. When an AI understands a consumer’s habits and is authorized to act on their behalf, it can shape purchasing outcomes in ways that are not immediately obvious. 

AI agents might prioritize higher margin products, steer toward certain brands and preferred retail partners, or take advantage of known behavioral patterns. This does not require malicious intent to feel uncomfortable. It is simply an inference based on the shopper’s past behavior. This is why the immediate understanding is so crucial and where the combination of engagements in discovery are the most effective in retrieving the best option. Re-orders are a fairly predictable purchase. However, assuming one person’s high-end butter means they want high-end coffee would be an example of a mistake. This is where a prompt or question in the experience can save the day. Asking a simple question via an agent provides an opportunity to show care, and to simply provide 2 choices. Or also asking for a quick ‘yes’ or ‘no’ on a product to add to a cart.  We can use this approach to avoid being pushy or nudgy and getting the shopper what they want immediately.

The line between helping and nudging can become difficult to see. Consumers already recognize that algorithms across social media channels serve up concentrated levels of content to feed a perceived interest. This intensity can reach a point where consumers feel like they are living in an artificial bubble. The line between helping and nudging becomes much harder to identify when recommendations evolve into automated decisions. That is why trust in agentic commerce will depend less on the sophistication of the AI itself and more on whether consumers feel they remain in control of the experience.

Consumers Want Assistance AND Control

Despite these concerns, consumers have shown they are willing to make certain tradeoffs to take advantage of the AI-powered assistance. According to a recent Algolia survey, 61% of consumers say wider AI adoption will create better shopping experiences. 

Many will share some personal data in exchange for clear, immediate benefits, such as saving time or getting better deals. They are also comfortable delegating routine, low-stakes decisions, such as reordering household goods or comparing travel options. If the outcomes consistently feel useful, people are often willing to accept some level of opacity in how decisions are made.

Instead of fully rejecting the technology, there must be boundary setting in place. Trust begins to erode when financial control feels compromised, especially if purchases happen in ways that feel unexpected or out of sync with the consumer’s intent. People also react strongly when systems begin making inferences about sensitive areas like health, relationships, or financial stress without explicit input. That is where “helpful” starts to cross the line to become “invasive.”

Transparency also becomes critical as AI systems grow more autonomous. When users cannot easily understand why a decision was made, confidence drops quickly. This becomes even more important when money is involved. 1 in 5 (20%) consumers would trust AI to purchase $50 or less without review, but only 8% said they’d trust AI with anything over $250. Mistakes are generally tolerated, but only if they are easy to reverse. Irreversible or difficult-to-correct errors create frustration and undermine trust. Similarly, consumers have little tolerance for their personal data being reused beyond the context in which it was originally shared, especially when it is sold or applied to advertising.

Ultimately, the tension is not simply between privacy and convenience. It is about control and delegation. People are willing to hand off tasks, but not their sense of agency or their paramount desire to be understood; this is the kernel of customer experience. When it feels like the system is acting with them, the experience is empowering. When it feels like it is acting instead of them, the value quickly diminishes.

The companies that succeed in this space make oversight simple and visible, offer clear and flexible permissions, and build trust through consistent, predictable behavior. When done well, people will not feel like they are giving up their privacy. They feel like they’ve gained a new super power, (or life hack, cheat code), that becomes part of how they think about your brand.