AI Systems and Human–AI Collaboration

Agentic Systems AI-enabled systems that can plan, use tools, execute multi-step tasks, and adapt actions toward defined objectives within governed boundaries. → AI Governance Policies, roles, controls, evaluation practices, and decision rights used to manage AI systems throughout their lifecycle. → AI Information Quality The accuracy, relevance, structure, provenance, and fitness of information used by or produced through AI-enabled systems. → AI Readiness The organizational, information, technology, governance, and operating conditions required to adopt AI responsibly and create useful outcomes. → Generative AI AI systems that generate or transform content, code, images, data, or other outputs from learned patterns and user or system instructions. → Human–AI Teaming The intentional design of roles, interfaces, handoffs, oversight, and feedback so people and AI systems can collaborate effectively. → Metacognitive Intelligence The ability of a person or system to evaluate the quality, limits, uncertainty, and reasoning process behind its own understanding or decisions. → Retrieval-Augmented Generation An AI architecture that grounds generated responses in retrieved external information to improve relevance, currency, traceability, or factual support. → Trustworthy AI AI systems designed and operated to be reliable, transparent enough for their context, secure, governable, and aligned with intended human outcomes. → Workflow Automation The use of software, rules, integrations, and AI to reduce manual coordination and reliably execute repeatable business processes. →