Term explanation
Interpersonal communication refers to the exchange of information, meaning, and feeling between two or more people. It encompasses verbal and non-verbal signals and shapes relationships in both personal and professional contexts. Effective interpersonal communication involves active listening, empathy, clear expression, and the ability to read and respond to social cues. It forms the foundation of teamwork, leadership, customer relationships, and all forms of collaborative work.
LIZ AI supports interpersonal communication by handling the preparation burden. When every slide is automatically accurate and on-brand, communicators can focus on the human exchange — not slide logistics.
Learn more →Diagonal communication means that the employees of a company communicate with each other regardless of their function and their level in the organisational hierarchy and regardless of their department within the company.
Learn moreChain of thought is an AI reasoning technique in which a model explicitly works through intermediate steps before arriving at a final answer. By laying out its reasoning step by step, the model produces more accurate and reliable outputs — especially for complex, multi-part problems. In agentic AI systems, chain-of-thought reasoning is used to plan workflows and make decisions at each stage of an agent loop. For enterprise applications, it increases transparency and makes AI behavior easier to audit.
Learn moreAn adaptive presentation is a slide deck that automatically adjusts its content, structure, or length based on context — such as the intended audience, available time, or communication goal. Rather than maintaining separate versions of the same deck, adaptive presentations use AI to derive the right variant on demand. They are a practical application of AI-powered workflows in the presentation layer, and are closely related to the living presentation concept — combining dynamic content with audience-aware adaptation.
Learn moreTask decomposition is the process by which an AI agent breaks down a complex, high-level goal into a sequence of smaller, manageable subtasks. The agent identifies dependencies between steps, determines what tools or data each step requires, and decides which subtasks can run in parallel. Task decomposition is a fundamental capability of Agentic AI systems and is central to how an agent loop executes multi-step workflows reliably.
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