Trusted AI is AI built on the foundation of trusted data—data that is reliable, accurate, governed, and seamlessly integrated across an organization. Without trusted data, AI initiatives are unlikely to deliver meaningful return on investment (ROI).

Key elements of Trusted AI include:

• Trusted data: The cornerstone of Trusted AI, it ensures that AI models are trained and operate on high-quality, well-governed data
• Explainability and accountability: Organizations must understand how outputs are generated, especially for generative and agentic AI
• Scalability and openness: The use of trusted data platforms enables scalable AI/ML operations with open application programming interfaces (APIs) and integration capabilities
• Business and technical alignment: Tools that rely on Trusted AI help both business and technical teams make better decisions by simplifying access to insights
• Faster ROI: By harmonizing data and integrating AI into business processes, organizations can accelerate value creation and innovation

Trusted AI is built on a framework of three core pillars, or principles:

1. People: AI must be developed and deployed with human oversight, ensuring ethical use and compliance with safety and privacy standards
2. Transparency: Organizations need full visibility into how AI decisions are made, with explainable models and governed data to ensure trust and accountability
3. Value creation: AI should consistently deliver measurable business value through reliable performance, driving innovation and cost-effective growth

Organizations can use a combination of technical assessments, governance frameworks, and ethical principles to determine whether their AI is trustworthy. Teradata’s framework for Trusted AI, built on the three core principles of people, transparency, and value creation, is one place to start.