How retailers can earn customer trust in the age of AI

From hyper-personalized customer journeys to optimized supply chains – retailers are implementing AI to support, streamline, and scale operations across the business.

But as AI’s role in retail grows, so does consumer scepticism. The pace of transformation is outstripping the public’s understanding. Some consumers may take what they see at face value, but it’s clear that, for others, trust in AI is dwindling, and concerns around the ethics of AI use in retail are increasing. More conscious consumers are asking questions about everything from data governance to AI decision-making and sustainability goals.

Transparency is often cited as the answer for these concerns, yet airing every algorithm or overwhelming consumers with technical detail rarely fosters genuine confidence. A better approach for retailers is focusing on the actual drivers of trust. How can organizations move beyond technical disclosures to prove that their AI-driven operations are competent, reliable, and acting with integrity?

The conscious consumer in the AI era

Modern consumers are more informed, discerning, and publicly vocal than ever. And while the majority may not be interested in debating the finer points of AI ethics, many are quick to speak out when they believe AI is making experiences less fair, and brands less accountable.

Recent consumer research reveals the extent of this rising scepticism. According to YouGov, only 26% of Americans say they trust AI’s use in retail, while a third say they don’t. Similarly, the RTS reports that 77% of UK shoppers want retailers to spell out governance procedures for AI use. Almost as many (73%) feel retailers must do more to build trust around the use of AI within their shopping journeys.

Crucially, these figures don’t signal an outright rejection of AI. Consumers value the convenience, personalization, and efficiency that AI enables retailers to offer. But they expect clarity around how AI is influencing their shopping journeys, and how their data is being used in the process.

Where the breakdown in trust is actually happening

What triggers the concerns of conscious consumers? There are some specific friction points to consider:

• When AI-driven decisions lack visibility: If customers don’t know why a decision has been made, they’re more likely to be suspicious of the rationale and process that led to it, even if the outcome is correct.
• When there’s no visible human-in-the-loop (HITL) for critical decisions: For decisions with significant impact, the absence of an evident human-in-the-loop is a catalyst for mistrust. If there’s no clear path to challenge AI decisions or escalate a complaint, consumers have no sense of who ultimately owns the outcome, and this can easily be interpreted as a lack of retailer accountability.
• When customer data feels under valued: Most consumers are happy to trade their data if the payoff is a better retail experience. PG Forsta’s 2025 report on the CX trust deficit found that more than two thirds of respondents across the US and the UK are willing to share personal data when there’s a clear benefit to be had from the exchange. But trust breaks down when customers believe retailers are using their data and AI to disadvantage them, instead of delivering greater personalization and better service levels.
• When bias goes unchecked: Biased AI decisions can have significant real-world consequences for consumers, such as being unfairly excluded from a promotion or having a frustrating experience with a voice assistant that works poorly for non-native speakers. If retailers show that they're actively pursuing fairness – by investing in tools to identify and mitigate bias – they can reassure consumers that AI-driven decisions are being monitored, challenged and corrected when necessary.
• When internal siloes undermine consistency: If data, AI models, and decision-making processes exist in siloes across fragmented systems and disconnected teams (e.g. sales, supply chain, and marketing), no one has complete visibility of how AI is influencing the experience of the end customer. And without that, providing any kind of accurate external transparency is very difficult.

When trust dwindles, what are the repercussions?

When consumer trust collapses, the impact can ripple far beyond individual purchasing decisions. Some customers may quietly take their business elsewhere, but there’s always a risk that scepticism will play out in public, whether through social media backlash, review sites, petitions or other forms of lasting reputational damage. Any missteps in data handling or AI implementation can escalate quickly into a public relations disaster that swiftly undermines consumer loyalty.

On top of this, the global regulatory landscape around AI is beginning to take a more substantial shape. While governance frameworks are far from set in stone, and approaches vary by region, it’s clear that, in many parts of the world, ethical AI is shifting from a matter of organizational discretion to a compliance requirement. For instance, in 2025, the State of New York passed a new law mandating that any retailer using algorithmic pricing is required to disclose that practice at checkout.

Building customer trust in AI era

Imagine a customer receives a personalized promotion from their go-to eCommerce site. They do not necessarily need to inspect the underlying algorithm or review the code to trust the offer. As Vanessa Candela, Chief Legal and Trust Officer at Celonis, commonly says, “Trust is earned through proving competence, building credibility through reliability, showing empathy and maintaining integrity.”

For a retailer, this means using AI to demonstrate competence and reliability—ensuring that the promoted item is actually in stock and the delivery promise is met because the AI agent had full visibility into the supply chain. It means maintaining integrity by ensuring the pricing is fair and consistent across channels, regardless of the user's data profile. And it means showing empathy if a disruption occurs; rather than silence, the system proactively alerts the customer and offers a solution before they even have to ask. By focusing on these operational outcomes rather than technical disclosures, retailers prove that their AI is acting in the customer's interest.

Enterprise AI as a key operational discipline

Ultimately, the greatest risk to consumer confidence isn't that shoppers don't understand the AI, but that they don't trust the outcomes it generates. Choosing to treat AI not as a branding exercise, but as a rigorous operational discipline, will help companies earn that trust.

Retailers that focus on demonstrating competence and reliability through their AI-driven operations won’t just satisfy regulators and minimize risk. They will secure a powerful point of differentiation—demonstrating that they’re committed to making their processes work for their customers.