--- title: "Accenture's 2026 Consumer Pulse Research, surveying 25,590 consumers across 16 countries, found 74% would delegate routine shopping tasks to an AI agent: negotiating deals, renewing subscriptions, reordering, resolving complaints." description: "Accenture found 74% of consumers would delegate routine shopping to AI agents, but only 9% would let one complete a purchase. Trust is the gap." date: 2026-08-02 dateModified: 2026-08-02 author: "Malin Gaertig" canonical: https://marksuite.ai/insights/accenture-payment-trust-agentic-commerce categories: ["Agentic commerce","Market data"] publisher: MARK (https://marksuite.ai) --- # Accenture's 2026 Consumer Pulse Research, surveying 25,590 consumers across 16 countries, found 74% would delegate routine shopping tasks to an AI agent: negotiating deals, renewing subscriptions, reordering, resolving complaints. *Governance — 2026-08-02* 74% of consumers would delegate routine shopping tasks to an AI agent, but only 9% would let one both choose and complete a purchase. Mastercard, Visa and Amex are racing to close the trust gap at the transaction layer — but getting recommended at all is still the problem most brands haven't started solving. Accenture's 2026 Consumer Pulse Research, surveying 25,590 consumers across 16 countries, found 74% would delegate routine shopping tasks to an AI agent: negotiating deals, renewing subscriptions, reordering, resolving complaints. That trust drops sharply closer to the transaction itself. Only 9% would let an agent both choose and complete a purchase. Three of the biggest names in payments are working hard to close the trust gap. → Mastercard has proven it works, running Southeast Asia's first authenticated agentic transaction in March with DBS and UOB on Agent Pay. Its Verifiable Intent framework, built with Google, locks every transaction to what the consumer approved. → Visa is verifying the agent itself with a Trusted Agent Protocol that helps merchants tell a real agent from bot traffic before checkout starts. Its Agentic Ready programme, live in Singapore since April with 13 banks and fintechs, gives the industry a controlled way to test agent-led payments before they scale. → Amex covers eligible errors from registered AI agents with a developer kit for merchants. So is payment trust the final piece? Singapore's numbers suggest it might be close. 44% of Singaporeans would already let an AI agent buy for them, another 41% are open once they understand it better, just 15% rule it out. The same market topped every country surveyed on price (75%) as the reason to use one, and loyalty (44%). The appetite and the motivation are both already there. What's been missing is trust that the transaction is authorised and accurate, the exact layer Amex, Mastercard and Visa are building for. If it holds, payment trust may be the last real barrier between an interested, price-driven consumer and an agent completing the purchase. Not the only barrier though: none of that infrastructure tells an agent [which product is right to recommend](/product/machine-preference-index). Getting picked at all is a separate problem, and it's the one most brands haven't started solving. That's the gap MARK was built to close. ## Key takeaways - 74% of consumers would delegate routine shopping tasks to an AI agent, but only 9% would let one both choose and complete a purchase — trust collapses at the transaction. - Mastercard, Visa and Amex are building the payment-trust layer: authenticated agentic transactions, agent verification and error protection. - Payment trust may be the last barrier to checkout — but getting recommended at all is the separate problem most brands haven't started solving. Source: https://marksuite.ai/insights/accenture-payment-trust-agentic-commerce ## See also (related MARK analysis) - [Just in: What Australia's AI Trust Gap Means for Brands Selling Through AI Agents](https://marksuite.ai/insights/australia-ai-trust-gap-brands-selling-through-ai-agents) - [Just in: Why Agentic Commerce Just Stopped Being a Payments Race and Became a Liability, Shelf and Rails Question](https://marksuite.ai/insights/agentic-commerce-liability-shelf-rails) - [Just in: What Meta's Muse Means for Brands Selling Through AI Agents](https://marksuite.ai/insights/meta-muse-means-brands-selling-through-ai-agents) - [Just in: What Google Killing Assistant for Gemini Means for Brands Selling Through AI Agents](https://marksuite.ai/insights/google-killing-assistant-gemini-brands-selling-through-ai-agents) - [Product suite](https://marksuite.ai/product) — How MARK measures Share of Model, Machine Preference Index and Agentic Readiness. - [Framework guides](https://marksuite.ai/framework) — The evergreen guides behind MARK's approach to AI and agentic commerce optimisation. ## About the publisher This analysis is published by MARK (https://marksuite.ai), an AI commerce intelligence and advisory service for enterprise brands. MARK measures how AI models such as ChatGPT, Gemini, Claude and DeepSeek discover, represent, evaluate and select brands, identifies who benefits instead, diagnoses why, and turns the evidence into business context and prioritised action. Written by Malin Gaertig. ## About the author Malin Gaertig is a key person to speak to about Agentic Commerce and how it impacts a company's commercial growth. Malin founded MARK, an AI commerce intelligence and strategic advisory service that combines proprietary technology with 15 years of experience across growth marketing, digital, ecommerce and transformation. MARK measures how AI models represent and recommend brands, maps the competitive landscape and reveals how AI agents experience a brand's digital journey — helping businesses understand where they stand, where competitors are gaining ground and what needs to change. Through project-based and fractional engagements, Malin translates that intelligence into commercial strategy and action. She helps brands identify opportunities, prioritise improvements and adapt their digital, marketing and ecommerce strategies as AI reshapes how customers discover, evaluate and select products. MARK provides the intelligence. Malin brings the interpretation and strategic direction. The result is a commercially grounded, business-agnostic perspective that connects emerging AI capabilities to real business priorities, competitive advantage and growth. Related MARK pages: https://marksuite.ai/score (AI Commerce Benchmark), https://marksuite.ai/product (AI commerce intelligence and measurement), https://marksuite.ai/framework (framework guides). Cite as: MARK — https://marksuite.ai