Artificial intelligence (AI) dominates the conversation in practically every industry. And risk and insurance leaders are no exception. From underwriting to claims to compliance, leaders are told that adopting AI is no longer optional. But in high-stakes, regulated environments, rushing into adoption can create more risk than reward. That’s why it helps to have a framework to guide decisions. Origami’s TRUST framework is a set of principles designed to make AI adoption tactical, responsible, user-focused, secure, and transparent. This article introduces the TRUST framework at a high level, but we invite you to explore it more in depth in our eBook, “The AI Maturity Roadmap for Risk and Insurance Leaders.” Why Thoughtful Adoption Wins Over Urgency The conversation around AI often defaults to urgency: “Move fast or fall behind.” But for risk and insurance leaders, the real competitive advantage comes from moving deliberately and scaling sustainably. Claims, underwriting, and compliance demand precision, transparency, and resilience. The organizations that succeed with AI won’t be those that adopt first, but those that adopt well. And that means embedding technology in ways that elevate decision-making and withstand scrutiny from regulators, clients, and boards. According to Roots’ State of AI Adoption in Insurance 2025, 45% of insurers are still only exploring AI capabilities, with just 22% running production-level solutions. The gap between curiosity and scale underscores why principles like TRUST matter: they bridge ambition and execution. The TRUST Framework Origami Risk’s TRUST framework offers a practical, principled way to think about AI adoption in risk-sensitive environments. Each element translates into best practices that ensure AI strengthens operations rather than introducing new vulnerabilities. Tactical Anchor AI to business problems that matter most. Start with narrow use cases that deliver measurable efficiency gains, such as reducing manual data entry or accelerating claims reviews, rather than chasing flashy proofs of concept. Responsible Put governance in place early. Assign clear ownership, document decisions, and ensure AI outputs are explainable and aligned with both ethical standards and regulatory requirements. User-controlled Build adoption through confidence. Give teams control over when and how AI is applied, allowing for edits and overrides where possible, and design tools that support human judgement instead of replacing it. Secure Treat security and compliance as non-negotiable. Keep data in protected environments, restrict sensitive information, and align AI use from frameworks like HIPAA and GDPR. Transparent Make clarity the default. Provide visibility into how AI reaches conclusions, track performance over time, and ensure outputs can be audited and validated. Applied together, these principles operate less like a checklist and more like a playbook for building trust in AI and embedding it responsibly into the daily fabric of risk and insurance operations. Why TRUST Matters Now The implications of AI are expanding. Emerging capabilities like automated summarization and trend analysis promise speed and insight. But without TRUST guiding their adoption, these same capabilities can amplify bias, introduce compliance risks, and undermine user confidence. In an industry built on managing uncertainty, AI itself must not become another risk. Imagine a claims team using AI to auto-summarize loss reports: with TRUST, the AI outputs are editable (user-controlled), tracked against performance benchmarks (responsible), and tied back to source notes (transparent). Without these safeguards, the same tool could raise questions about accuracy, data integrity, or accountability. You’ll be eroding trust instead of building it. Every organization sits at a different point on the AI maturity curve. Some are exploring possibilities, others are piloting, and a smaller group is scaling. The path forward doesn’t require uniform speed, but it does demand consistent principles. By applying TRUST, leaders can ensure that wherever they are on the journey, they are moving with clarity and intent. To explore how the TRUST framework applies across the AI maturity curve and see practical checklists, use cases, and strategies that bring it to life, download our eBook, The AI Maturity Roadmap for Risk and Insurance Leaders.