Users distrust "talk to your data" tools they don't understand. Stripe's Sigma product overcomes this by generating a natural language explanation alongside every answer. It details assumptions made, like the specific dates used for "Black Friday," allowing non-technical users to verify the logic.
Related Insights
The need for explicit user transparency is most critical for nondeterministic systems like LLMs, where even creators don't always know why an output was generated. Unlike a simple rules engine with predictable outcomes, AI's "black box" nature requires giving users more context to build trust.
To avoid AI hallucinations, Square's AI tools translate merchant queries into deterministic actions. For example, a query about sales on rainy days prompts the AI to write and execute real SQL code against a data warehouse, ensuring grounded, accurate results.
To build trust, users need Awareness (know when AI is active), Agency (have control over it), and Assurance (confidence in its outputs). This framework, from a former Google DeepMind PM, provides a clear model for designing trustworthy AI experiences by mimicking human trust signals.
AI tools that provide directives without underlying context—"AI without the Why"—are counterproductive. An intent signal telling sales to target a company without explaining the reason (e.g., what they researched) leads to generic outreach, wasted effort, and ultimately, distrust in the technology.
To trust an agentic AI, users need to see its work, just as a manager would with a new intern. Design patterns like "stream of thought" (showing the AI reasoning) or "planning mode" (presenting an action plan before executing) make the AI's logic legible and give users a chance to intervene, building crucial trust.
Moonshot AI overcomes customer skepticism in its AI recommendations by focusing on quantifiable outcomes. Instead of explaining the technology, they demonstrate value by showing clients the direct increase in revenue from the AI's optimizations. Tangible financial results become the ultimate trust-builder.
The best agentic UX isn't a generic chat overlay. Instead, identify where users struggle with complex inputs like formulas or code. Replace these friction points with a native, natural language interface that directly integrates the AI into the core product workflow, making it feel seamless and powerful.
Unlike consumer chatbots, AlphaSense's AI is designed for verification in high-stakes environments. The UI makes it easy to see the source documents for every claim in a generated summary. This focus on traceable citations is crucial for building the user confidence required for multi-billion dollar decisions.
Internal surveys highlight a critical paradox in AI adoption: while over 80% of Stack Overflow's developer community uses or plans to use AI, only 29% trust its output. This significant "trust gap" explains persistent user skepticism and creates a market opportunity for verified, human-curated data.
Unlike many AI tools that hide the model's reasoning, Spiral displays it by default. This intentional design choice frames the AI as a "writing partner," helping users understand its perspective, spot misunderstandings, and collaborate more effectively, which builds trust in the process.
