Retailers are making heavy investments in AI. From interactive virtual shopping assistants to automated supply chain tools, the goal is simple: connect with buyers and drive growth. However, realizing real business value requires bridging a critical trust gap.
So why did ThoughtSpot team up with YouGov to survey 4,833 adults across the US and the UK? It all comes back to trust. Because as an agentic analytics company, we recognize a reality many technology vendors overlook: an AI model is only as reliable as the underlying data.
When retail AI fails (whether by pushing a bad product recommendation, misjudging a customer’s budget, or miscalculating delivery timelines), it is not just a technical error. It damages customer trust.
Our survey report, titled Trust, Tested, reveals a clear split between retail strategy and customer expectations. The primary finding sets the tone for the entire study: 74% of consumers say retailers already collect too much of their personal data.
When shoppers enter a digital storefront feeling protective of their data privacy, every AI feature is put on trial. Right now, most shoppers remain unconvinced.
The AI Trust Gap by the Numbers
The Trust, Tested report highlights a significant trust deficit that retail product, marketing, and data teams need to address:
The 13% Problem: Only 13% of consumers trust AI-powered product recommendations. Nearly half (47%) actively distrust them, leaving 34% sitting neutrally in the middle.
Humans Beat Algorithms: Shoppers overwhelmingly turn to real people over predictive models. Friends and family (43%) and customer reviews (27%) top the list when people name their most trusted sources. AI recommendations land at the bottom at just 1%.
The Accuracy Deficit: Consumer skepticism is grounded in experience. 66% of respondents report that AI has misunderstood their needs or suggested the wrong item.
The Cost of Inaccuracy: Unreliable AI harms customer retention. 59% of consumers say they are unlikely to keep shopping with a retailer if AI repeatedly suggests products outside their price range. On top of that, 53% will leave a brand after a significantly late delivery. If you miss the price point or mess up fulfillment, you lose the customer.
The Personalization Paradox: Where Data Collection Crosses the Line
Personalization is a delicate balance. Handled thoughtfully, it creates smooth customer experiences. Overstep, and you risk losing repeat business.
Shoppers are not against personalization, but they object to covert surveillance. People generally accept personalized experiences when using data they explicitly provided, such as account preferences. Discomfort begins when collection extends beyond those boundaries.
In fact, 91% of consumers find at least one modern retail personalization method intrusive. The most cited examples include:
Tracking activity across other websites or apps (57.6%)
Sending product recommendations or ads too frequently (56.7%)
Referencing sensitive or personal topics (48.7%)
Using real-time location tracking (48.8%)
Predicting needs before interest is shown (48.7%)
What Shoppers Want: Transparency and Practical Utility
The report offers clear steps for building confidence in AI-driven tools. Consumers are asking for straightforward design and privacy standards:
To build trust, retailers should provide:
1. Data Transparency (34.0%): State clearly which data feeds into a recommendation.
2. Opt-Out Controls (31.4%): Give shoppers direct options to turn off AI personalization.
3. Explainability (30.6%): Offer plain-language context on why an item is recommended.
How ThoughtSpot Helps Retailers Earn Consumer Trust
Winning over the 34% neutral "swing vote" requires moving past opaque AI models and unverified assumptions.
Delivering reliable, transparent customer experiences on the front end requires total clarity across backend data systems. ThoughtSpot’s search-driven analytics platform gives retail, e-commerce, and merchandising teams the tools to build that clarity:
Turn Complex Data into Searchable Insights: ThoughtSpot lets business teams query enterprise data using natural language, providing immediate access to verified customer preferences, buying trends, and inventory levels without reliance on black-box algorithms.
Connect Recommendations to Backend Supply Chains: Fulfillment failures like stockouts and late shipments damage brand equity. ThoughtSpot unifies data across operations, inventory, and marketing, making sure recommended products are actually in stock and ready to ship on time.
Maintain Clear Data Governance: ThoughtSpot provides built-in governance and data lineage tracking. Retail leaders can verify exactly how an insight was generated, ensuring personalized experiences draw on clean, consented, first-party data rather than third-party tracking.
Retail technology analyst Miya Knights highlighted this shift in her foreword to the report:
"The undecided third of consumers is the commercial prize, and it will be won by retailers that treat transparency and control as product features rather than policy pages. It is the fulfillment of a product or service where trust is ultimately won or lost."
Conclusion: The Mandate for Retail Leaders
The Trust, Tested report points to a clear conclusion: winning long-term loyalty requires accurate, value-first execution.
To close the AI trust gap, retail organizations should focus on three priorities:
Focus on Accuracy Over Assumptions: Move away from tracking behavior across third-party apps. Instead, analyze explicit search terms, purchase histories, and consent settings to deliver features that save shoppers time and money.
Align Customer-Facing AI with Operational Facts: Avoid making promises that backend systems cannot fulfill. Connect AI shopping agents to live, accurate inventory and logistics data to prevent delivery delays.
Build Control into the User Experience: Give customers clear visibility into their data footprint. Explaining why a recommendation appears and offering easy opt-out settings turns skeptical shoppers into confident buyers.
The goal of retail AI is not to gather as much personal data as possible. The goal is to return clear value and transparency to the customer.
Want to explore the regional breakdowns and complete data sets? Check out the full report, here.
Interested in exploring how ThoughtSpot can transform your retail or CPG data strategy? Visit our resource page today!
All figures, unless otherwise stated, are from YouGov Plc. The total sample size was 4,833 adults (2,617 in the US and 2,216 in the UK). Fieldwork was undertaken between 16th – 23rd June, 2026. The survey was carried out online. The figures have been weighted and are representative of all US and UK adults (aged 18+).
