AI’s potential is limitless, but so are the questions surrounding it. Unlike traditional software, which delivers the same result for the same input every time, AI is non-deterministic—meaning it doesn’t always give the same answer, even when asked the same question. If that sounds familiar, it’s because it behaves a lot like a toddler: often unpredictable, sometimes brilliant, occasionally nonsensical.
This unpredictability breeds uncertainty, skepticism, and even fear—not only among consumers but also within the organizations building AI-powered products. How do we ensure AI is reliable, ethical, and aligned with human values? How do we create AI systems that people actually trust?
The answer isn’t purely technical—it’s human. Trust is more than a function of accuracy or performance in an LLM; it’s built the same way it is in human relationships: through transparency, reliability, and alignment with shared values. To understand how to build trust in AI, we can apply a framework originally designed for leadership: the Trust Triangle.
Why AI Faces a Trust Deficit
It may be tempting to write off concerns around trust in AI as knee-jerk reactions to a new application of technology. To do so, however, is to miss the larger point, and to ignore that many people’s skepticism around AI is rooted in real concerns. Research from Washington State University bears out, for example, that consumers are wary of purchasing products that have AI included in the product description.
Reasons for this lack of trust in AI and AI products include the following:
- AI is unpredictable. Unlike traditional software, AI doesn’t always produce the same result under the same conditions. That variability makes people uncomfortable, especially when AI is being used in high-stakes domains like healthcare, finance, and hiring.
- We don’t know how it works. Many AI models are trained on vast amounts of data, but where that data comes from, how the models are trained, and what biases exist within them are often opaque—even to their own creators.
- We hold AI to a higher standard than humans. Research suggests that we’re far less forgiving of mistakes made by machines than by people. A self-driving car that causes one fatal accident can feel far more unsettling than the thousands caused by human drivers every year.
- Historical precedent tells us trust takes time. Consider the history of automated elevators: Despite being around since the 1800s, they weren’t fully trusted until the 1970s—long after they became safe and reliable. What finally tipped the scales? Design choices that made people feel secure, like emergency buttons, phones, and alarm systems. AI will require the same careful scaffolding.
If we want AI to be embraced, we can’t just expect people to trust it—we have to design for trust.
The Trust Triangle: A Leadership Framework Applied to AI
Leadership experts Frances Frei and Anne Morriss introduced the Trust Triangle, which identifies three pillars of trust:
- Logic: Does this make sense? Are its outputs explainable and consistent?
- Empathy: Does it consider the needs and perspectives of the user?
- Authenticity: Does it behave in alignment with its stated purpose?
Although designed for human interactions, this framework applies surprisingly well to AI. The most trusted AI systems will be those that:
- Show their logic. AI models need to provide transparency into how they reach their conclusions. Companies like DeepSeek and Perplexity have made progress here by displaying their reasoning process step by step.
- Demonstrate empathy. AI should be designed with user context in mind, anticipating real concerns and aligning with human needs. An AI system that understands user intent and offers explanations (rather than just spitting out answers) will foster greater trust.
- Operate authentically. AI should function in ways that align with its stated goals. If an AI assistant promises unbiased hiring recommendations, but consistently favors certain demographics, it creates a major trust breach.
The more AI systems reflect these principles, the more likely they are to be embraced.
How Companies Can Design for Trust
Building trust in AI won’t be achieved through a single feature or policy—it requires a multi-layered approach.
1. Transparency Is Key
Users don’t just want answers—they want to understand how and why AI reached a particular conclusion. Companies like Anthropic are prioritizing “constitutional AI,” where their models are designed with clear ethical guidelines and transparent methodologies. Open-source models like DeepSeek and LLaMa also contribute to trust by allowing external experts to review their processes.
2. Create Clear Guardrails
Organizations that develop AI products need visible, enforceable safeguards:
- Clearly documented data sources and model training processes
- Internal evaluations and monitoring to catch bias and inconsistencies
- Transparent communication about the system’s limitations and risks
Without these, users will always wonder: What’s really happening under the hood?
3. Trust as a Competitive Advantage
Customers are already skeptical of black-box AI—the companies that make their AI more visible, understandable, and accountable will have a market advantage. Just as the introduction of safety features and certifications helped people embrace automated elevators, AI systems need trust markers built into the user experience.
Internal Trust: AI and the Future of Work
Trust in AI isn’t just an external problem—it’s an internal one too. Many employees are already using AI tools, whether their organizations have formally adopted them or not. Companies that try to block AI usage altogether are fighting a losing battle.
Instead, businesses should focus on guiding AI adoption responsibly with clear internal policies:
- Accountability: Employees should know that they are still responsible for the quality and accuracy of AI-assisted work.
- Data Protection: Companies must set firm rules against entering sensitive or proprietary data into AI tools.
- Leadership Buy-In: Leaders should actively use AI and encourage thoughtful experimentation, rather than letting AI adoption happen in secret.
Startups are AI-first by default—leveraging AI at the core of their business. Larger organizations need to adapt or risk falling behind.
Conclusion: Trust Is Earned, Not Assumed
AI is here to stay, but its success depends on how well we build trust into its foundation. This isn’t just about accuracy—it’s about explainability, transparency, and alignment with human values. Organizations that embrace AI thoughtfully and transparently will gain a massive competitive edge, while those that fail to address trust concerns will struggle with adoption. The companies that act like startups—experimenting, communicating openly, and embedding AI as a strategic advantage—will be the ones that define the future.
About the Author
Jessica Hall is the Chief Growth Officer at OpsCanvas, a Knownwell AI Advisory Board member, and a leading voice on how UX will change in the age of AI. She has spent more than a decade on the cutting edge of innovation, design, and product leadership, a combination that gives her a unique perspective on how AI experiences can impact business growth. For more of Jessica’s thoughts on the importance of establishing trust in AI, listen to this week’s episode of AI Knowhow on Using AI to Drive Proactive Leadership.
