Quad/Graphics and The Harris Poll released a nationwide study showing that while consumers are eager to use agentic AI for shopping, 75 % say they would trust those agents less if recommendations were sponsored, putting a spotlight on the fragile relationship between AI‑driven commerce and brand credibility.
The survey, titled “The New Rules of Retail Trust in the Age of AI,” polled 2,180 U.S. adults in early February 2026. It found that price‑sensitive shoppers turn to generative AI assistants for convenience and price‑checking, yet they remain skeptical of any monetary sway. Only 39 % trust AI agents to make everyday purchases, and just 34 % feel comfortable delegating larger‑ticket items to a bot. By contrast, 81 % say a strong in‑store experience boosts confidence in buying the same brand online, underscoring the continued relevance of brick‑and‑mortar in an AI‑first world.
Survey Findings
The data paint a nuanced picture of consumer behavior:
- Awareness is high: 74 % of respondents know that agentic AI shopping tools exist, and 51 %—including 62 % of Gen Z and Millennials—prefer AI assistance to avoid bad purchases.
- Pricing scrutiny is intense: 73 % say algorithm‑driven pricing makes it hard to verify they’re getting the best deal, while 71 % would rather shop in a physical store to avoid “surveillance pricing.”
- Sponsored results are a deal‑breaker: 75 % would trust an AI assistant less if its recommendations were influenced by brand dollars, and the same share would trust the brand itself less if it paid for that influence.
- In‑store trust remains a differentiator: 81 % believe a great in‑store experience makes them more confident trying new products online, and 71 % feel that uniform store pricing counters the perception of unfair online deals.
These figures echo broader industry trends. Gartner predicts that by 2027, 70 % of B2B marketers will rely on AI‑generated insights to shape campaigns, yet Forrester warns that over‑personalization can erode trust if consumers suspect hidden motives. The Quad/Harris Poll study adds a concrete, consumer‑facing dimension to that warning.
Why Trust Matters for Enterprise Marketing
For enterprise marketers, the survey’s headline—*trust in AI‑mediated commerce is fragile*—is a call to action. AI agents are increasingly embedded in platforms owned by Google, Amazon, Microsoft, and Salesforce, where they surface product recommendations, price comparisons, and even purchase shortcuts. If those agents start surfacing sponsored items without clear disclosure, brands risk a backlash that could spill over to the platforms themselves.
The findings suggest three strategic imperatives:
- Transparency First: Clearly label any paid placement within AI‑driven recommendation engines. The FTC’s 2024 guidance on “AI‑generated endorsements” makes this a regulatory as well as reputational concern.
- Leverage Physical Touchpoints: Use in‑store experiences to reinforce brand credibility. Retailers that align AI‑suggested pricing with in‑store price tags can mitigate the “surveillance pricing” anxiety that 71 % of shoppers expressed.
- Data Stewardship: With 73 % of respondents uneasy about AI’s use of personal shopping data, enterprises must adopt a privacy‑by‑design framework and communicate data handling policies in plain language. (Data Stewardship)
Competitive Landscape
Quad’s partnership with The Harris Poll positions the company alongside AI‑focused rivals such as Adobe’s Sensei and Salesforce’s Einstein, which both tout “transparent AI” as a differentiator. However, the survey highlights a gap: most AI platforms have yet to integrate a robust, user‑facing disclosure mechanism for sponsored content. Adobe recently introduced “AI‑Transparency Tags” in its Experience Cloud, but adoption remains limited. Microsoft’s Azure AI services provide audit logs for internal compliance, yet they lack a consumer‑visible layer.
In contrast, Amazon’s Shopping Assistant has begun testing “Sponsored Recommendation Labels” in select markets, a move that could set a new industry baseline if consumer trust metrics improve. Quad’s data could serve as a benchmark for these experiments, offering a quantitative lens on how labeling affects conversion and brand perception.
Implications for Enterprise Marketing Teams
Marketing teams must now balance the efficiency of AI automation with the risk of alienating a trust‑sensitive audience. Practical steps include:
- Integrating AI with CRM data to deliver personalized offers that are visibly consistent across channels, reducing the perception of hidden pricing algorithms.
- Deploying AI‑driven chatbots that can hand off to human agents when a shopper signals high purchase intent, blending the speed of bots with the reassurance of human interaction.
- Running A/B tests on disclosed versus undisclosed sponsored AI recommendations to quantify the impact on click‑through and purchase rates.
The survey also reveals an emerging cohort of “AI‑first shoppers” among Gen Z and Millennials, who are comfortable using bots for real‑time assistance but still value unbiased recommendations. Brands that can demonstrate impartiality while leveraging AI’s speed will likely capture a larger share of this demographic.
Future Outlook
As AI agents evolve from simple search assistants to autonomous purchasing entities, the line between recommendation and persuasion will blur. IDC forecasts that AI‑driven commerce will account for 30 % of total retail spend by 2028, making the trust equation a strategic moat. Companies that embed transparent monetization models and align AI outputs with consistent in‑store experiences will be better positioned to ride that growth curve.
Market Landscape
The AI‑enabled commerce market is consolidating around a few cloud giants—Google Cloud’s Vertex AI, Microsoft Azure AI, and Amazon SageMaker—each offering APIs that power agentic shopping experiences. Simultaneously, specialized AI agents from startups like Anthropic and Cohere are being integrated into e‑commerce platforms to provide conversational product discovery. However, as the Quad survey shows, the competitive advantage now hinges less on raw model size and more on **trust architecture**: clear labeling of sponsored content, robust data privacy, and seamless integration with physical retail.
Industry analysts warn that without these safeguards, **brand dilution** could become a systemic risk. A 2024 McKinsey study linked a 5 % drop in **brand equity** to perceived “algorithmic bias” in recommendation engines. In contrast, firms that pair AI insights with transparent, human‑verified touchpoints have reported **up to 12 % lift in conversion** during holiday peaks.
Top Insights
- Sponsored AI results erode trust: 75 % of shoppers would trust an AI assistant less if recommendations were paid for, signaling a need for clear disclosure.
- Physical stores remain a trust anchor: 81 % say a strong in‑store experience boosts confidence in buying the same brand online.
- Gen Z and Millennials lead AI adoption: Over half of these cohorts use AI for real‑time assistance, yet 54 % are uneasy about AI accessing their shopping history.
- Pricing transparency is critical: 73 % find algorithmic pricing confusing, and 71 % prefer uniform store pricing to avoid “surveillance pricing.”
- Enterprise marketers must blend AI with human touch: Combining AI efficiency with transparent, human‑verified recommendations can protect brand equity while driving growth.
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