In both physical and digital commerce, trust is foundational. As retailers explore new ways to serve customers through conversational AI, expectations are shifting. AI systems are no longer just answering questions—they’re helping users discover, compare, and move toward decisions in more guided, collaborative ways.
This evolution toward agentic AI—systems that take initiative and help users progress—holds promise. But it also raises critical questions about transparency, safety, and user agency. As these systems play a more active role in shaping the customer journey, trust can’t be treated as a feature. It has to be an integral part of how we design, test, and improve.
Why Agentic AI Needs to Prioritize Trust
Retailers are asking AI to do more: surface relevant products, manage ambiguity, respond naturally, and handle edge cases—all while protecting user data and brand tone. These expectations are growing because the user experience is becoming more dynamic and personal.
According to a 2023 PwC survey, over 75% of consumers say that trust in how companies use AI is essential to their willingness to engage digitally (PwC, 2023). And Gartner projects that companies prioritizing transparency and trust in AI will see significantly higher returns on their investments (Gartner, 2023).
In short: trust isn’t optional. It’s directly tied to value, adoption, and outcomes.
Key Design Considerations in Practice
In developing agentic conversational systems, we’ve found the most meaningful advances come from features that improve both experience quality and user confidence. A few practical themes have emerged:
- Clarifying Unclear Requests: Shoppers often ask questions that are broad or underspecified. Instead of returning irrelevant results—or guessing—the AI should be able to identify gaps in understanding and ask useful, focused follow-ups. This keeps the conversation productive without overwhelming the user.
- Understanding Context Over Time: User intent evolves across multiple turns. By tracking context and drawing from prior messages, the AI can offer more coherent and personalized responses—whether it’s refining product filters or recognizing a shift from discovery to comparison.
- Graceful Recovery: Even the most capable AI won’t always have the perfect answer. What matters is how it responds when it reaches the edge of its knowledge. Fallback mechanisms that provide on-brand, safe, and non-repetitive alternatives can help preserve trust, even during uncertain moments.
- Language Accessibility: Supporting multiple languages broadens reach—but it also adds complexity. We see multilingual readiness not just as a feature, but as part of ethical inclusivity, helping ensure users everywhere have equal access to value.
Learning and Improving Through Feedback
Responsible AI is never static—it evolves through continuous interaction with users and real-world data. At Rezolve Ai, we take a user-first, data-informed approach to development, focusing not just on how the system is designed to perform, but on how it’s actually experienced across diverse and dynamic retail scenarios.
We pay close attention to signals like hesitation, rephrasing, or shifts in user direction—not as errors, but as opportunities to better understand intent and improve the system’s ability to guide, clarify, and support. Partner feedback, internal reviews, and behavioral patterns help us identify areas where the experience can be made more intuitive, responsive, or helpful.
Best practices like A/B testing, user research, and in-session analysis play a critical role in making AI systems more accountable and aligned with user needs. By grounding product decisions in real-world insights, we ensure that every improvement brings us closer to delivering an AI that is not only capable—but trustworthy, human-aware, and genuinely useful.
Looking Ahead
There’s no single milestone or launch that completes the journey toward trustworthy AI. It’s an ongoing process of aligning product performance with user expectations—and doing so with humility, clarity, and care.
We continue to refine how our AI communicates, when it takes initiative, and how it adapts to different needs. Our goal is to help users feel understood and supported—not just with the right product results, but with an experience that reflects their intent and earns their confidence.
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