Will brands succeed by building their own AI shopping agents?
Explores whether brands can compete by creating proprietary AI agents that mediate consumer purchases, or whether structural conflicts of interest and missing ecosystem conditions will undermine this strategy.
Accenture argues that once AI agents mediate purchasing, "the moment of choice moves upstream to the decision layer—the standards, data and verification logic that determine what gets chosen," and that brands face a binary: "become the agent of choice, or be chosen by the agent." The firm reports that 71% of consumers "expect generative AI to influence at least half of their spending decisions over the next 12 months," and that agents "will break brands down into comparable parts, test claims against reality and steer spending toward the best outcomes," so that "weak differentiation, inflated pricing and poor fit get exposed instantly." Specific appetite is concrete: 61% want an agent that shops across multiple grocery stores, and 71% want one that plans "a complete trip across airlines, hotels and activities." Accenture adds that weekly gen-AI users are "more than twice as likely to want retailers or brands to become the agent of choice than AI-native platforms."
The mechanism Accenture gives is structural, not just behavioral: when an agent sits between consumer and brand, it reorders "what gets bought, when, and how brand value is created," so treating agents as "just another channel" misreads what's happening. Accenture qualifies this against pure commoditization — "nearly 63% want agents to shop for their 'idealized self'" — so a brand with real differentiation is amplified by an agent's comparison, not just exposed by it.
Accenture's prescription — that brands should build their own agent to become "the agent of choice" — sits in tension with Can an AI agent serve both merchant and user interests fairly?, which argues a brand- or merchant-funded agent cannot represent the user's interest without conflict once it earns referral fees from the merchants it recommends. Accenture's own figure that weekly users prefer retailer-built agents over AI-native platforms describes a stated consumer preference; it does not address Azhar's structural objection to who an agent answers to once built. The claim that agents will "test claims against reality" also depends on exactly the ecosystem conditions in Why do capable AI agents still fail in real deployments? — verification logic requires standardization and trustworthiness that current agents are not shown here to have.
This excerpt does not establish that agents are currently performing this claims-testing and comparison function at the sophistication Accenture describes; the figures are consumer expectations and preferences reported to a survey, not measurements of agent behavior or market outcomes. Accenture also sells agentic-AI transformation strategy to the brand audience this report targets, giving it a direct interest in framing the shift as urgent and binary ("the window to decide where to play is open" but "habits forming now will be hard to break"). The reasonable inference is that consumers expect and would welcome agent-mediated comparison shopping, not that this comparison infrastructure already functions as described.
Inquiring lines that read this note 19
This note is a source for these research framings, grouped by the broader line of inquiry each explores. Scan the bold lines of inquiry; follow any specific question forward.
How should control and autonomy be allocated in human-AI collaboration? What systematic biases emerge in AI shopping agents' decision-making?- Why has marketing research neglected prompting behavior and relational dynamics with AI?
- Can a merchant-funded AI agent remain loyal to the consumer's interests?
- Do AI agents currently verify brand claims against reality at scale?
- Why do weekly AI users prefer brand-built agents over AI platforms?
- What conflicts exist between vendor incentives and objective consumer research findings?
- Will merchants deliberately exploit stable position biases to influence agent purchasing?
- Do AI shopping agents favor items from preferred sources over objectively better alternatives?
- Can brands deliberately climb from lower tiers through optimization efforts?
- Could unequal access to capable AI agents create new consumer economic disparities?
- How do high-autonomy versus low-autonomy AI systems differ operationally in retail?
- Why do consumer protection frameworks designed for humans break down with algorithmic agents?
- Why do safeguards matter more than AI capability for consumer adoption?
- What legal standardization and trust infrastructure do AI purchasing agents require?
- Do consumers who trust AI agents understand their actual limitations?
- What safeguards beyond sign-off do consumers want from autonomous shopping agents?
- Does stated trust in brands predict actual delegation of agentic commerce?
- How does trust transfer between AI systems and established payment brands?
- Why do consumers resist letting AI control payment authority directly?
Related concepts in this collection 4
This note in its neighbourhood — explore the map, then jump to a related concept in the list below.
Click a node to walk · click center to open · click Open in graph to see this note in the full knowledge graph
-
Can an AI agent serve both merchant and user interests fairly?
Explores whether an agent funded by merchant referral fees can make unbiased recommendations on behalf of users, or whether the payment structure creates an unavoidable conflict of interest.
tension: Accenture urges brands to build their own agent of choice while Azhar argues fee-funded agents can't be loyal to users
-
Why do capable AI agents still fail in real deployments?
Explores whether agent failures stem from insufficient capability or from missing ecosystem conditions like user trust, value clarity, and social norms. Understanding this distinction matters for predicting which agents will succeed.
the "test claims against reality" verification logic Accenture describes depends on the standardization and trustworthiness conditions this note sets out
-
Do consumers actually want to delegate shopping decisions to AI?
Accenture's survey reports 74% of consumers trust AI agents with purchases, but the data reveals selective delegation bounded by brand loyalty and safeguard requirements. The question asks whether stated willingness reflects genuine appetite or reflects survey framing and vendor bias.
sibling note from the same survey, on the consumer-side delegation pattern driving this brand-side consequence
-
Do shopping agents respond more strongly to price and rating cues?
Explores whether AI agents manipulated by prices, ratings, and nudges shift their choices more dramatically than humans facing identical options. Matters because it affects how exploitable agents are to persuasion design.
evidence for A's decision-layer claim: LLM shopping agents' choices shift far more than humans' under seller price, rating, and nudge manipulations
Related papers in this collection 8
Papers most semantically related to this note, ranked by cosine similarity in the embedding space.
- Consumer Pulse Research 2026: Talk to my AI agent
- What Is Your AI Agent Buying? Evaluation, Biases, Model Dependence, & Emerging Implications for Agentic E-Commerce
- When Agents Shop for You: Role Coherence in AI-Mediated Markets
- The AI Agent in the Room: Rethinking Consumer Decision-Making in the Age of Autonomous AI
- Agentic Commerce: Why Do Shoppers Delegate Purchase Decisions to AI?
- From Product Search to Preference Articulation: The Economics of Agentic Commerce
- The Automated but Risky Game: Modeling and Benchmarking Agent-to-Agent Negotiations and Transactions in Consumer Markets
- Shopping by algorithm: How agentic AI deploys human heuristics as a surrogate consumer
Original note title
Accenture argues brands must become the agent of choice or be chosen by the agent