In the December edition of the Agentic AI Report, PYMNTS Intelligence found that many consumers had begun tasks using artificial intelligence in some capacity. Some of them were even substituting some search behavior for the new zero-click world. Now, the technology is quickly becoming a viable entry point for commerce and money management. Yet the nearer these systems get to checkout, the more consumer confidence gravitates from novelty to familiarity: regulated credentials, established payment rails and the ability to approve, reverse or override decisions.

Consumers are ready to delegate meaningful purchasing and financial tasks to agentic AI. However, broad adoption will be constrained and shaped by “payments-grade” trust. This includes transparent controls, clear data governance and credential and rail choices anchored in banks, wallets and card networks, rather than in the AI layer alone.

Across daily-life activities, most consumers express at least some interest in delegating decisions and execution to an AI assistant. Interest is “deep” in certain areas, especially health and wellness and purchasing. The question is no longer whether consumers will engage with agentic AI, but rather where, how and under what conditions.

These are just some of the findings detailed in “From Assistive to Agentic AI: Consumers Wade Into Autonomous Commerce,” a PYMNTS Intelligence exclusive report. This installment of the Agentic AI Report Series examines consumer adoption of agentic and generative AI across 54 personal-use cases in nine areas of daily life. It draws on insights from a survey of 2,299 U.S. adult consumers conducted from Nov. 10, 2025, to Dec. 10, 2025.

The PYMNTS Intelligence Consumer AI Framework Matrix

PYMNTS Intelligence assesses consumer AI adoption through a task-based framework spanning 54 activities across nine areas of daily life. These include shopping, finances, health, education and travel. Each activity is weighted to reflect the model sophistication required for reasoning and accuracy, the perceived risk to the consumer if the technology is wrong and the sensitivity of the personal information required to complete the activity.

This scoring yields four consumer personas mapped along two dimensions: task complexity and the number of distinct AI tasks performed. At one end are Holdouts, i.e., those who do not use the technology and cite concerns about personal information as a key reason for non-adoption. At the other end are Power Users, defined as consumers who perform 27 or more distinct tasks, including higher-complexity use cases. Between these poles are Light Users (limited, low-complexity use) and Mainstream Users (moderate breadth, typically low complexity, with selective, higher-stakes use). The practical benefit of this framework is that it separates AI readiness from demographics. It enables analysis of how trust and adoption change as AI shifts from “assistive” recommendations to “agentic” action.

Interest in Agentic AI Use

This report operationalizes consumers’ agentic behavior using eight purchasing and financial management scenarios. These are not single prompts or one-off recommendations. They depict an autonomous assistant that operates continuously and proactively, monitoring signals over time (purchase history, preferences, schedules and past interactions), identifying needs before the consumer asks, evaluating options and executing actions end-to-end. In short, they move beyond assistive AI to a model in which the AI “senses, decides and acts,” while aligning with user preferences and constraints.

This framing is important for payments and commerce executives because it changes the risk surface. If AI is making real-world commitments like purchases, bill payments, scheduling services and managing subscriptions, trust is no longer a brand attribute. It becomes a set of requirements.

Interest in using agentic AI is consistently high across all scenarios. Most respondents are at least somewhat interested in every category. Interest ranges from 54% for school and parenting tasks to 71% for health and wellness management.

Two scenarios stand out as the most dynamic from a value-creation perspective, requiring high coordination, carrying high stakes and offering a clear upside from proactive execution. Health and wellness management and travel planning generate the strongest interest, at 71% and 70%, respectively. These tasks benefit from timing, constraint management and coordination, creating conditions in which consumers are especially open to delegating judgment and execution to these systems.

At the same time, the data shows that commerce-adjacent automation is already within reach. Roughly two-thirds of consumers (69%) express interest in agentic subscription management, grocery shopping and meal planning. Home maintenance was cited by 68%, and bill management at 66%. The report’s interpretation is not novelty-seeking. It is friction reduction. Consumers are interested in using agentic systems for time-consuming and cognitively burdensome recurring tasks, particularly those involving renewals, replenishment and payments.

Tool Choice Signals a Trust Environment

“Agentic readiness” depends heavily on whether consumers experience AI as a primary, “front door” interface or as a feature embedded in another workflow.

Among Power Users, the use of AI agents is already normalized. Eighty percent report very or extremely high interest in using agentic AI for grocery management, and 78% report the same for health and wellness management. In parallel, reluctance is minimal. Among Power Users, only 7% are slightly or not at all interested in using an agentic AI assistant for grocery shopping. For health and wellness management, 5% say the same.

The strategic implication is that early volume will cluster where AI is already habitual, like dedicated AI platforms, in-app assistants and ambient smart-device assistants. In these segments, roughly six in ten or more consumers are very or extremely interested in using agentic AI for grocery and health and wellness tasks. This suggests that once AI is central to task execution, extending it into autonomous action is a natural next step.

Which brings us to the conversion problem. This report is explicit that the barriers to adoption are largely design solvable. Trust increases when autonomy is bounded and legible, such as when users can approve actions before they occur, undo or reverse decisions and invoke human review or override. Consumers are not rejecting autonomy; they are insisting that it be interruptible and accountable.

The second trust pillar is data handling. Consumers emphasize privacy protections broadly. Scrutiny is rising among those who rely on dedicated AI platforms and are more likely to demand transparency into how these platforms store, use and retain their data.

Agentic AI Commerce Growth

A defining signal is that consumers increasingly want agentic AI agents to complete purchases, not merely suggest them. Nearly half of interested consumers (49%) would delegate both routine purchases and large, research-driven purchases to an agentic AI assistant.

However, the path to scale differs by readiness segment. For advanced AI users, autonomous purchasing can span from replenishment to complex decision-making. For more cautious or less exposed consumers, autonomy will first gain traction in low-friction, low-risk use cases. Designing systems that flex between these modes—starting with repeat purchases and scaling to research-heavy transactions as trust builds—is critical to broad adoption.

The data ties commerce outcomes to interface norms. AI-heavy users increasingly prefer an end-to-end journey within the AI environment, from discovery through payment. Fifty-eight percent of dedicated AI platform users prefer in-platform checkout, as do 58% of those relying on AI embedded in an app or service and 53% of those using mobile phone AI assistants.

By contrast, consumers who rely on search-led or traditional workflows prefer merchant-site completion, even when AI aids discovery. Among those who primarily use search engine AI summaries, 59% prefer an AI assistant on the merchant website.

Data shows a distribution and economic story, not simply a user experience story.

If consumers expect purchases to occur within AI interfaces, gen AI platforms become gatekeepers of discovery, conversion, and potentially, payments. This forces merchants to be “AI-visible” and to integrate in ways that allow authenticated handoffs or true in-platform checkouts.

Consumers also show pragmatic openness to data sharing when the utility is clear. Yet there is a sizable “no access” minority between roughly one-quarter and nearly one-half, depending on the segment, which means product design cannot assume blanket consent. The recommended response is a tiered trust model—granular permissions, transparency and a privacy-preserving “lite mode.” Even among traditional non-AI-assisted consumers, opt-in is a bare majority at 53%, and 47% of users would block access and keep recommendations generic.

Payment Credentials Remain the Trust Anchor

If the previous finding highlights where commerce may occur, this one clarifies who consumers want “holding the bag” when agentic AI systems touch money.

Most consumers can name at least one institution they trust to offer an agentic assistant. This suggests that widespread skepticism is not preventing adoption. Across segments, digital wallets and banks are among the most trusted providers. Card networks remain consistently competitive, reflecting their role as trusted intermediaries and rule-setters in payments.

The contrast is stark among traditional, non-AI-assisted consumers. Just 3% trust a gen AI platform as the provider. For them, trust is anchored in familiar regulated financial institutions and established rails. Twenty-six percent trust their bank, and 21% trust a credit card network.

Among AI-native tool users, trust in gen AI platforms rises but does not become exclusive. One segment (“other smart device AI assistant,” such as Alexa Echo smart speakers) shows 33% selecting the gen AI platform. Dedicated AI platform users register 19% trust in gen AI platforms; even these users distribute trust across payment players.

The data synthesis is consequential. This is likely a partnership model, not a single winner-takes-all outcome. Consumers may want AI assistants to operate where they are, whether through a platform, app or device. But they often want the trust layer to be a payments-native entity, such as a wallet, bank or network, which signals the need for security, governance and recourse.

Implications and Opportunities for Agentic AI
Design agentic AI systems for bounded autonomy, not “full autonomy.”

Build the core journey around explicit checkpoints: pre-authorization flows, clear action previews, one-click undo/reversal and human-override paths. These are repeatedly identified as the most important trust-makers and are likely the fastest way to convert “somewhat interested” consumers into “very interested” users.

Separate the “AI interface” from the “trust and liability” layer, then connect them.

The data indicates that many consumers will accept AI-native shopping experiences, but trust clusters around banks, wallets and card networks. Companies should architect agentic commerce with payments-native consumers: credential tokenization, strong authentication, auditable authorization logs, and clear recourse. The winning proposition will be an AI-led journey with a regulated trust backplane.

Operationalize the value exchange on data and offer a viable “privacy-lite” path.

Personalization improves relevance and safety, but a meaningful minority will not grant purchase-history access. Build granular permissions and transparent data-use disclosures and ensure core shopping functionality works without full history. For merchants, treat “AI visibility” as table stakes, as gatekeeping power shifts to the interface where discovery and conversion occur.

Conclusion

Agentic commerce is moving from experimentation to execution, led by consumers already comfortable delegating real decisions in AI-native environments. As autonomy expands from recommendations to purchases and payments, the consistent message is that success depends on marrying AI intelligence with payments-grade trust, including controls, reversibility and familiar credentials.

Methodology

“From Assistive to Agentic AI: Consumers Wade Into Autonomous Commerce” is based on a survey of 2,299 U.S. adult consumers conducted from Nov. 10, 2025, to Dec. 10, 2025. The report examines consumer adoption of agentic and generative AI across 54 personal use cases in nine areas of daily life, with the sample balanced to match the U.S. adult population by age, gender, education and income.