Instead of competing with OpenAI's mass-market ChatGPT, Anthropic focuses on the enterprise market. By prioritizing safety, reliability, and governance, it targets regulated industries like finance, legal, and healthcare, creating a defensible B2B niche as the "enterprise safety and reliability leader."

Traditional onboarding asks users for information. A more powerful AI pattern is to take a single piece of data, like a URL or email access, immediately derive context, and show the user what the AI understands about them. This "show, don't tell" approach builds trust and demonstrates value instantly.

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.

Perplexity's CEO, Aravind Srinivas, translated a core principle from his PhD—that every claim needs a citation—into a key product feature. By forcing AI-generated answers to reference authoritative sources, Perplexity built trust and differentiated itself from other AI models.

Companies can build authority and community by transparently sharing the specific third-party AI agents and tools they use for core operations. This "open source" approach to the operational stack serves as a high-value, practical playbook for others in the ecosystem, building trust.

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.

The most effective AI user experiences are skeuomorphic, emulating real-world human interactions. Design an AI onboarding process like you would hire a personal assistant: start with small tasks, verify their work to build trust, and then grant more autonomy and context over time.

In sectors like finance or healthcare, bypass initial regulatory hurdles by implementing AI on non-sensitive, public information, such as analyzing a company podcast. This builds momentum and demonstrates value while more complex, high-risk applications are vetted by legal and IT teams.