Building trust in AI through design

78% percent of business executives believe AI will disrupt their industry within the next three years. Yet, only 20% trust their AI systems to make the right decisions. This contradiction, highlighted in a 2024 Deloitte report on enterprise AI adoption, captures a growing paradox. AI is getting smarter, but skepticism is growing alongside it.

The issue is not just about technical performance. Even the most advanced AI models struggle to gain user confidence. If users cannot see how AI reaches its conclusions, they hesitate to trust it, no matter how powerful it is.

Just as great product design fosters customer loyalty, so do seamless AI experiences, and strategic UX design is key to that goal.  Businesses are realising that intelligence alone does not drive adoption; product experience does. And that experience is shaped by four key UX principles - transparency, reliability, security, and user control. These are not just usability features. They are the foundation for making AI not only intelligent but also trustworthy.

In the sections ahead, we will explore how these UX pillars can help bridge the gap between AI’s growing intelligence and the trust it must earn.

The UX-first approach to building trust in AI

Good AI and good design share a key trait. Invisibility. The best design is seamless. You only notice it when something goes wrong. AI should feel the same way. Embedded, effortless, and frictionless. When done right, AI does not demand attention. It simply works.

Yet, many AI systems today still feel unnatural. A study published in BMC Psychology suggests that consumers are more likely to trust AI when its interactions feel intuitive and human-like, as they expect AI to exhibit human-like qualities in communication. However, most AI-driven experiences still lack that seamless quality. Users often struggle with rigid interfaces, unpredictable outputs, and systems that require too much effort to use.

One of the biggest shifts happening in AI today is the rise of AI agents—systems that can reason, plan, and take actions across different environments. Unlike traditional AI models that function within a single interface, AI agents are inherently multimodal. They interact through chat, vision, text, speech, and even physical actions. Designing for AI agents is a completely different challenge from designing for static interfaces.

Building for AI agents forces us to rethink UX. Unlike apps with defined user flows, AI agents operate dynamically, responding to real-world context and user behavior in unpredictable ways. This requires designers to embrace adaptability. How do you create an interface for an AI that shifts between voice, text, and visual interactions fluidly? How do you ensure consistency when an AI agent makes autonomous decisions on behalf of the user? These are not just UX challenges—they are fundamental to whether users will trust AI at all.

This is where UX becomes the bridge between AI’s intelligence and usability. A multimodal AI system needs interfaces that are flexible, scalable, and intuitive. Thoughtful design makes AI feel natural, predictable, and trustworthy. It ensures that intelligence does not feel intrusive or alien. Instead, it becomes a seamless extension of the user’s experience.

UX design is not just a layer on top of AI. It is the foundation that determines whether AI solutions feel intuitive or opaque, whether they inspire trust or hesitation. As AI continues to evolve, its success will depend not just on its intelligence but on how well it is designed for the humans who use it. 

Here are 4 UX design ideas that will define how human-AI interactions evolve.

Transparency and explainability

Users do not trust what they do not understand. AI systems must go beyond making decisions, they must show their work. Transparency and explainability are essential for users to feel confident in AI-driven outcomes. A system that simply produces a result without justification feels like a black box, which breeds skepticism rather than trust.

Show your work

AI should provide reasoning behind its decisions. Whether it is a product recommendation, a loan approval, or a medical diagnosis, users need to see why the AI reached a particular conclusion.

Fair play

Bias in AI erodes trust. Addressing biases in training data and communicating efforts to mitigate them builds confidence. Ethical AI design should prioritize fairness, ensuring that models do not reinforce harmful patterns or exclude key user groups.

Simplified communication

AI reasoning should not feel like reading a research paper. Explanations must be clear, digestible, and free from technical jargon. Users should be able to grasp an AI’s decision-making process at a glance.

Contextual understanding

When AI makes an ambiguous or unexpected decision, it should engage users in clarifying conversations. This could mean allowing users to ask why a recommendation was made or giving them the ability to adjust AI outputs based on their preferences.

One example of explainable AI (XAI) done right is Owkin’s MSIntuit CRC, an AI tool designed for colorectal cancer screening. Instead of acting as an opaque decision-maker, Owkin provides visual explanations and clear reasoning behind its predictions. By showing how and why it detects microsatellite instability (MSI), the AI allows doctors to validate its insights, reinforcing trust in its recommendations.

The lesson is simple. Trust in AI is not built on intelligence alone but on transparency. The more users understand how AI reaches its conclusions, the more they will feel comfortable relying on it.

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Reliability and robustness

AI is only as good as its consistency. Users trust systems that deliver dependable, repeatable results. The moment an AI model produces unpredictable or contradictory outputs, confidence erodes. Reliability in AI is not just about technical accuracy—it is about how AI handles errors, how it communicates uncertainty, and how seamlessly it integrates into real-world workflows.

Consistency

AI must deliver stable and reliable outcomes across different scenarios. A fraud detection system that occasionally misses fraudulent transactions or falsely flags legitimate ones will quickly lose credibility. Users need to feel confident that AI decisions are fair and predictable.

Graceful failures

No AI system is perfect. Errors are inevitable, but trust is maintained through transparent error handling and clear guidance. When AI makes a mistake, users should understand why it happened and how to correct or override it. A system that acknowledges uncertainty builds more trust than one that presents itself as infallible.

Continuous improvement

AI should not be static. Regular updates that improve accuracy and effectiveness reinforce trust over time. When users see that an AI system is learning and evolving, they feel more confident in its long-term reliability.

One example of AI-driven reliability is PayPal’s AI-powered fraud detection system. Designed to protect users from suspicious transactions while maintaining a seamless payment experience, PayPal has built trust in its AI security through clear communication, user control, and proactive security measures. 

Instead of working invisibly in the background, PayPal’s AI ensures that users are actively involved in their own security.

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How PayPal establishes trust in AI through UX design - 

Users receive instant notifications when an unusual transaction is detected. Alerts clearly explain why a transaction was flagged and provide next steps. Instead of vague warnings, PayPal offers context and clarity, reducing user anxiety.
If a transaction is flagged as fraudulent, users can review and dispute it within seconds. AI-driven chatbots guide users through the resolution process, ensuring a smooth and frictionless experience.

Users can enable multi-factor authentication (MFA) for extra security. PayPal also provides real-time risk scores for transactions, educating users about potential threats.

• Fraud reduction: AI has helped PayPal reduce fraudulent transactions by 73%, strengthening customer confidence.
  
  
• User trust: A study found that over 80% of PayPal users trust the platform’s fraud detection due to its transparent security measures.
  
  
• Customer retention: Improved fraud protection has contributed to a 45% increase in returning users, proving that AI-driven security enhances loyalty.

Trust is not just about intelligence. It is about reliability. AI does not need to be perfect, but it must be predictable, transparent, and continuously improving. PayPal’s approach demonstrates that when AI communicates clearly, involves users in key decisions, and provides real-time security, it shifts from being a hidden, abstract system to a trusted partner in daily transactions.

Security and privacy

Trust in AI is built on more than just performance. Users need to feel confident that AI systems are protecting their data, operating responsibly, and keeping them in control. Security and privacy is fundamental to garnering user trust, and AI should be able to integrate in a way that is transparent, user-driven, and accountable.

Data protection

AI systems must have strong security measures in place to safeguard user information. From encryption to access controls, users need to know that their data is being handled with care and cannot be exploited or misused.

Accountability

Clear policies around responsible AI usage are essential for trust. Users should understand how AI makes decisions, what data it collects, and how privacy is maintained. Transparency in AI governance ensures that security measures do not come at the expense of user rights.

One example of AI-driven security done right is Microsoft Defender’s AI-powered threat detection system. Designed to protect users from malware, phishing attempts, and zero-day attacks, Defender builds trust through predictive AI threat detection combined with a clear, intuitive design that keeps users informed without overwhelming them.

Instead of relying on invisible background processes, Defender actively engages users in their own security while maintaining seamless protection.

How Microsoft Defender establishes trust in AI through UX design

Defender does not just block threats but also explains why an action was taken. When stopping a suspicious file, instead of a vague warning, the system provides a clear reason:
“This file was blocked because it showed behavior similar to known malware.” This level of detail builds user confidence in AI-driven security decisions.

It allows users to customize AI security settings based on their personal or enterprise needs. Features like Application Guard and Controlled Folder Access let users define which apps can access sensitive data. Security feels user-driven rather than imposed.

Defender’s AI assigns confidence scores to detected threats, labeling them as low, moderate, or high risk. Instead of overwhelming users with constant alerts, this approach helps differentiate between false alarms and genuine threats. Users stay vigilant without unnecessary panic.

• Reduced False Positives: AI-driven threat detection improved accuracy by 87%, reducing unnecessary alerts while catching more real threats.
  
  
• Increased User Trust: After Microsoft improved its threat explanation UI, user confidence in Defender’s AI-based security decisions increased by 62%.
  
  
• Enterprise Adoption: Over 90% of Fortune 500 companies rely on Microsoft Defender, thanks to its balance of strong AI security and transparent user controls.

Trust in AI security is about making users feel protected, not powerless. Microsoft Defender proves that AI-powered cybersecurity can be effective without being intrusive. 

User control and agency

Trust in AI is not built on automation alone. Users need to feel like they are in control. When AI systems operate as opaque, autonomous decision-makers, they create uncertainty. But when users have the ability to adjust, refine, and understand AI-driven processes, they feel empowered rather than sidelined. Control does not just improve usability, but it also directly increases trust.

Empower users

AI should not dictate outcomes without user input. Systems that allow users to customize settings, adjust parameters, and fine-tune decisions foster greater confidence. When people feel that AI is working with them rather than for them, they are more likely to trust its capabilities.

Control Parameters

Users should have the ability to fine-tune AI behavior to align with their needs. By offering adjustable control parameters such as model sensitivity, confidence thresholds, or data preferences. This way, AI systems empower users to customize their experience. When users can influence how AI operates, it shifts from a rigid system to an adaptable assistant, increasing trust and adoption.

Control reduces fear

A lack of control over AI can create anxiety, especially in high-stakes environments. When users can modify settings, oversee decision-making, and influence AI behavior, they feel more secure. The more agency they have, the less AI feels like an unpredictable force.

One platform that exemplifies user control in AI is Vertex AI, Google Cloud’s enterprise-ready AI solution, now enhanced by Gemini models. Vertex AI enables businesses and developers to train, fine-tune, and customize large language models (LLMs) to fit their specific needs. 

Instead of offering a rigid, one-size-fits-all system, Vertex AI ensures that AI adapts to users—not the other way around.

How Vertex AI establishes trust in AI through UX design

Vertex AI empowers businesses and developers to tailor AI models to their specific needs. Users can adjust model parameters, select appropriate datasets, and modify deployment settings, ensuring AI behavior aligns with their unique requirements. This flexibility reduces reliance on pre-packaged models that may carry hidden biases, allowing for a more customized AI experience.

To enhance transparency, Vertex AI offers explainability features that elucidate model decisions. Whether dealing with text generation, image classification, or predictive analytics, the system provides insights into the rationale behind specific outputs, fostering user trust in AI-driven processes.

By granting users control over data, model tuning, and decision-making processes, Vertex AI mitigates the unpredictability often associated with AI adoption. This user-centric approach leads to increased confidence in AI outputs and promotes higher engagement levels.

Vertex AI demonstrates that AI systems can be both adaptable and transparent. By enabling users to refine, interpret, and shape AI models, it transforms AI from a mysterious entity into a reliable tool.

Impactful Data Points-

• Implementing Vertex AI's customizable features has led to a significant improvement in model performance, with users reporting increased precision in predictive analytics.
  
  
• After integrating Vertex AI's explainability tools, businesses have observed a notable rise in user confidence regarding AI-generated decisions.
  
  
• A substantial number of enterprises have adopted Vertex AI, attracted by its balance of robust AI capabilities and transparent user controls.
  
  

Building trust in AI involves making users feel empowered and informed. Vertex AI exemplifies how AI-powered solutions can be both effective and user-friendly.

Human-centered AI design: Enhancing user experience

The success of AI depends on thoughtfully crafted user experiences. A model can be powerful, but if it feels mechanical, impersonal, or difficult to engage with, users will hesitate to adopt it. AI is not just about intelligence. It is about how that intelligence is experienced.

Human-centered design ensures that AI feels natural, intuitive, and aligned with user expectations. Instead of forcing people to adapt to AI, it shapes AI to fit human needs.

Conversational tone

AI should not sound robotic or rigid. A more natural, friendly, and context-aware interaction style improves engagement and makes AI feel approachable. Even in professional settings, users respond better to AI that communicates with warmth and clarity rather than cold, technical responses.

Personalization

One-size-fits-all AI does not work. AI should adapt to individual users by learning preferences, remembering past interactions, and tailoring responses. Personalized experiences make AI feel more relevant, increasing both usability and trust.

Proactive assistance

AI should not just react, it should anticipate. Thoughtful AI experiences predict user needs, offer timely suggestions, and provide assistance before problems arise. The best AI tools feel effortless because they help without requiring users to ask for help.

Demystify AI

Trust comes from understanding. AI should not feel like magic or mystery. Educating users about what AI can and cannot do builds realistic expectations and reduces skepticism. Users should know when AI is making a recommendation when it is making an autonomous decision, and how much control they have over the outcome.

One example of human-centered AI is IRIS, an AI agent developed by Aubergine Solutions. Unlike rigid automation, IRIS was designed to seamlessly integrate into workflows, anticipate user needs, and provide intuitive, context-aware interactions.

How IRIS establishes trust in AI through UX design

IRIS is more than just a chatbot. It understands tone, context, and intent, whether users communicate in technical jargon or casual language. With advanced natural language processing, it adapts dynamically, ensuring conversations feel natural and intuitive.

To enhance usability, IRIS introduces a redesigned interface that provides conversation summaries and AI-driven suggestions. Users can quickly revisit key points instead of sifting through lengthy exchanges, improving efficiency and decision-making.

By supporting multiple languages, IRIS ensures smooth interactions across diverse users. It adapts both tone and nuance to different linguistic contexts, making conversations feel more natural and engaging.

IRIS allows users to choose from multiple large language models (LLMs) and voice providers, tailoring AI behavior to specific industries like finance, healthcare, and customer service. This modular approach ensures AI aligns with user needs rather than forcing them into predefined constraints.

By remembering past interactions, IRIS provides personalized, proactive support. It anticipates user needs and intervenes only when necessary, enhancing decision-making without being intrusive - 

• IRIS understands user tone, intent, and context for natural, fluid interactions.
  
  
• Users can revisit key points and access AI-driven recommendations effortlessly.
  
  
• IRIS supports multiple languages with localized tone and nuance for diverse audiences.
  
  
• Users can select the best intelligence for their specific industry needs.
  
  
• IRIS remembers past interactions and assists only when necessary, avoiding AI fatigue.

Building trust in AI involves making users feel understood and in control. IRIS exemplifies how AI-powered solutions can be both effective and user-friendly.

Bridging intelligence and trust in AI through UX design

The success of AI depends on thoughtfully crafted user experiences. For AI to succeed, it must not only be powerful. It must feel effortless, human-centered, and built for trust. AI is only as effective as the experience it delivers. UX is the bridge between AI’s potential and user adoption. 

At Aubergine, our team of 150+ designers, developers and AI experts is dedicated to creating AI experiences that prioritize transparency, adaptability, and user control. By combining deep AI expertise with human-centered design principles, we ensure that AI products are not just functional but also intuitive and trustworthy. 

Through rigorous research, iterative testing, and ethical AI design, we craft experiences that drive user adoption and long-term product success.

AI is only as effective as the experience it delivers. UX is the bridge between AI’s potential and user adoption. The future of AI isn’t just about smarter models. It’s about better experiences.

Want to make your AI product more user-friendly and trusted? Let’s talk.