Building trust in the era of agentic commerce: The importance of ethical AI

The impact of ethical AI in agentic commerce

As AI shifts from recommendation engines to autonomous agents that can decide, act and even purchase on behalf of customers, agentic commerce is rising as a massive opportunity for brands, retailers and even B2B enterprises. For instance, 90% of businesses stated they would increase spending on AI infrastructure in 2026. 

The possibilities for enterprises are real: More visibility, more conversions, more revenue. It’s the opportunity to launch and scale channels that consumers have been increasingly adopting with gusto. 

While consumers are indeed leveraging AI for product discovery, comparison and evaluation, the large majority of them remain cautious:

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• 74% say AI makes it harder to trust what they see online, and over half of consumers are unwilling to share sensitive data. 
• The same research found that 64% worry that tech companies will expose or misuse their data. 
• 8% of companies reported at least one AI incident that led to harm, disruption or a violation of law and negatively impacted their brand’s perception. 

At the same time, 88% of consumers will return to buy from a brand they highly trust, and brands perceived as responsible for customers’ data see 25% higher spending. 

While infrastructure investments continue to flourish in agentic commerce, consumer trust is imperative for continued growth. And that can only be achieved by applying ethical AI principles across every single layer across controls, governance and infrastructure. 

What is ethical AI? 

Ethical AI is often described as a set of principles, such as fairness, transparency, privacy and accountability. But in practice, especially in retail, it is less about abstract values and more about something far more concrete: Who has control when AI makes a decision and how that control is enforced.

In traditional digital commerce, ethical concerns were mostly human-led. A marketer decided which promotion to show, a merchandiser decided how products were grouped, a developer decided how recommendations were ranked. Even when algorithms were involved, humans stayed close to the decision loop.

That distance is now shrinking with agentic commerce as AI systems are no longer just suggesting outcomes but also executing them: They choose products, apply logic to pricing, interpret user intent and, in some cases, complete transactions autonomously. This means ethical considerations move from the interface layer into the infrastructure itself.

Ethical AI, in this context, is not a feature you turn on, but a system of constraints that shapes behavior before a customer ever sees an outcome. At its core, ethical AI in retail rests on four practical dimensions:

1. Transparency of decision-making: Can the system explain why a recommendation, price or action occurred in a way that’s understandable and auditable? If decisions can’t be traced back to clear inputs and logic, trust becomes fragile by default.

2. Fairness in outcomes: Does the system systematically disadvantage certain customers, brands or behaviors without justification? Fairness in AI is more than bias in data; it’s about how optimization goals can unintentionally distort outcomes at scale.

3. Control over data and actions: Who decides how data is used, shared and applied? And more importantly, can customers and retailers meaningfully set boundaries on what an AI agent is allowed to do on their behalf?

4. Accountability for impact: When an AI-driven decision causes harm (financial, reputational or experiential), there must be a clear chain of responsibility. Ethical AI requires ownership, not diffusion of blame across systems, vendors or models.

In retail, these principles converge into a single requirement: AI systems must remain governable even when they are autonomous.

That means ethics cannot sit outside the system as policy documentation or compliance checklists. It must be embedded directly into how AI is built, deployed and monitored through governance structures, product data integrity and enforceable policy layers.

How to create a foundation of ethical, trustworthy AI controls

If agentic commerce is the new frontier, then control systems are the foundation beneath it. Retailers that succeed will design the rules that AI must follow before it ever reaches the customer.

This foundation rests on three interconnected layers: Governance, brand mechanisms and consumer-defined rules. 

1. Governance: Building accountability before automation

Most companies still treat governance as something that happens after deployment. In the agentic era, that approach breaks down immediately.

Once AI agents can act independently — selecting products, applying discounts, or completing transactions — every decision becomes a potential compliance, reputational or financial risk. That’s why governance must shift into the design phase.

Forward-looking retailers are already building internal AI governance structures that bring together legal, product, security and data teams before any system goes live. Their job isn’t to slow innovation, but to define its boundaries.

They are asking new kinds of questions:

• Can we explain why this recommendation was made?
• Can we trace how this decision was influenced?
• Can we prove customer preferences were respected?
• Can we detect when incentives may have biased outcomes?

Alongside internal structures, the technical layer matters just as much. Audit logs, interaction tracing and compliance-ready data trails are becoming essential infrastructure, not optional documentation.

2. Brand mechanisms: Controlling the signals AI learns from

If governance defines the rules, brand mechanisms define the reality AI systems interpret.

In agentic commerce, AI agents don’t “see” branding the way humans do. They interpret structured signals: Inventory accuracy, pricing consistency, product attributes, fulfillment reliability and trust indicators. This quietly shifts control of brand perception from marketing teams to data architecture.

Retailers that fail to maintain clean, consistent, machine-readable product data risk something subtle but serious: Invisibility. If AI agents cannot confidently interpret a product, they will not recommend it. In this environment, brand strength is increasingly defined by:

• Data integrity over storytelling.
• Fulfillment reliability over promotional creativity.
• Transparent pricing over discount tactics.
• Structured attributes over visual merchandising.

As AI systems begin to reinforce these signals, reliable brands are rewarded with higher visibility, and those with inconsistent data become deprioritized.

3. Consumer-defined rules: Shifting from personalization to permission

The third layer represents the most profound shift in retail history: Customers are no longer just reacting to experiences but defining their boundaries.

As AI agents take on purchasing authority, consumers will increasingly encode intent directly into systems:

• Budget limits per purchase or category.
• Privacy constraints on data usage.
• Ethical preferences for sourcing or sustainability.
• Rules for substitutions or brand exclusions.

What was once implicit (“I prefer sustainable products”) becomes explicit, programmable logic that guides every decision an AI agent makes. This means retailers are optimizing for eligibility within customer-defined constraints.

Ethical AI as a strategic pillar for agentic growth

Ethics isn’t just a checklist item at the end of an AI implementation, especially when it comes to AI algorithms and customer data. 

Demonstrating ethical principles will help eCommerce retailers build and maintain customer trust as AI becomes increasingly integrated into the fabric of our lives. This bolsters, rather than hinders, your AI maturity. A McKinsey study found that companies that invest more in responsible AI (RAI) report higher levels of AI use and AI-related revenue.

The earlier companies invest in AI governance and controls, the better positioned they’ll be for AI implementation, including agentic commerce.

commercetools is committed to helping companies be responsible, trustworthy custodians of data. In addition to our many trust center solutions for data security and privacy, we are dedicated to an ethical approach to AI in everything we do, so you can be confident that our solutions reinforce your ethical stance as an AI user.

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Ethical AI in Retail

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FAQs

1. What is an ethical AI system?

An ethical AI system is one where fairness, transparency, privacy, and accountability are built into the architecture, not added after deployment. It ensures AI decisions are explainable, controlled and governable even when systems act autonomously.

2. How do we ensure the ethical use of AI?

Ethical AI is ensured by embedding controls across three layers: Governance, data and user rules. This includes AI governance structures, auditability, high-quality structured data, policy enforcement systems and customer-defined constraints like privacy and spending limits.

3. Why is ethical AI important in agentic commerce?

As AI agents move from recommendations to autonomous actions, trust becomes critical. Ethical AI builds customer confidence, reduces risk and directly impacts retention and revenue.

4. What are the key components of ethical AI in retail?

Ethical AI rests on four pillars: Transparency (explain decisions), fairness (avoid bias), control (set boundaries on data and actions) and accountability (assign responsibility for outcomes).

5. What role do retailers play in building ethical AI systems?

Retailers define the data, rules and signals AI relies on. By building strong governance and clean, structured data systems, they directly shape whether AI outcomes are trusted, visible and compliant.