According to Accenture's Consumer Pulse research, based on a survey of 25,590 people across 16 countries, 74% of consumers say they would trust a personal AI agent more than their best friend to make a purchase on their behalf. The Accenture report, "Talk to My AI Agent," also finds 74% willing to delegate routine commerce tasks (negotiation, complaints, reorders), 32% willing to let an agent select a product provided the consumer authorizes payment, and 9% open to fully autonomous purchases that include payment, per Accenture. The report shows consumers retain controls: 56% would tell an agent which brands to consider while 37% of self-identified brand-loyal consumers would accept an agent switching brands for a better fit, according to Accenture.
What happened
According to Accenture's Consumer Pulse research, published June 3, 2026 and based on a survey of 25,590 consumers across 16 countries, 74% of respondents said they would trust a personal AI agent more than their best friend to make a purchase on their behalf. The report, "Talk to My AI Agent," finds 74% of consumers are willing to delegate routine commerce tasks such as negotiating deals, resolving complaints, reordering products, and managing subscription renewals to an AI agent. Accenture reports 32% of consumers would let an agent decide what to buy provided the consumer confirms payment, and 9% are open to fully autonomous purchases including payment. The research also reports 56% of respondents would instruct agents about preferred brands while 37% of behaviorally loyal consumers would accept an agent switching brands if it better meets needs, per Accenture.
Technical details / Editorial analysis - technical context
Editorial analysis - technical context: Survey results highlight practical trust boundaries that map to current technical constraints and product design patterns. Payment confirmation remains a clear user-held control point, which aligns with common implementations that separate recommendation/selection pipelines from authorization flows. Low-stakes, repetitive transactions are the most likely early use cases for agentic workflows, a pattern consistent with staged deployment strategies seen in commerce-oriented automation tools. Adoption at scale will depend on reliable preference encoding, explainable recommendation signals, and integration with payment and identity systems.
Context and significance
Editorial analysis: For brands and e-commerce practitioners, the data underscores an emerging two-level decision ecosystem where value accrues both to the human-facing brand and to the agent-facing signals that guide automated choices. The report frames trust as the new baseline for agent adoption; Accenture's messaging argues that brands must demonstrate verifiable value to both people and their agents. Outside reporting in Forbes and TechRadar emphasizes that AI-mediated recommendations could erode pure name-recognition loyalty and shift competition toward measurable performance in agent evaluations.
What to watch
Editorial analysis: Observers should track three indicators:
- •whether major retailers and marketplaces publish agent-facing APIs or schemas for preferences and trust signals
- •pilot deployments that move payment authorization from humans to delegated flows while preserving auditability
- •metrics from early agent-mediated purchases showing repeat usage and lift in lifetime value. Industry reporting also suggests monitoring generative-AI usage segments, since active gen-AI users were among those more likely to delegate discovery to agents
Bottom line
Accenture's large-scale survey provides a measured snapshot of consumer willingness to hand routine commerce tasks to AI agents, while also documenting clear boundaries and conditions for delegation. Editorial analysis: The pattern mirrors other technology adoption curves where predictable, low-risk tasks unlock broader automation once trust is established through repeated positive experiences.
Scoring Rationale #
The report is notable for its large sample and actionable consumer metrics that matter to product, commerce, and marketing teams, but it does not introduce new models or technical breakthroughs. It signals meaningful adoption trends rather than a technical inflection.
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