AI Knowledge for Decision-Makers
A semantic glossary of the most important terms in enterprise AI. Designed for CEOs, CTOs, and strategy leaders who want not just to understand AI, but to deploy it strategically.
Retrieval-Augmented Generation (RAG)
RAG is an architectural pattern where a large language model (LLM) accesses external, verified knowledge sources at runtime. Instead of relying solely on training data, the system retrieves relevant documents, databases, or corporate policies and integrates them into response generation. For Swiss SMEs, this means: more precise, fact-based AI responses grounded in company-internal knowledge—with full control over data sovereignty. RAG drastically reduces hallucinations and enables compliance-conform AI usage.
LLM Integration
LLM integration refers to the systematic embedding of large language models into existing business processes and IT infrastructures. This includes API connections, prompt engineering, fine-tuning, and orchestrating multiple models. In the Swiss context, data sovereignty is paramount: models can be operated locally or on Swiss servers to ensure nDSG compliance. A well-designed LLM integration automates repetitive tasks, accelerates decision processes, and scales expertise across departmental boundaries.
Agentic Workflows
Agentic workflows describe autonomous AI systems that independently plan, execute, and iteratively optimize multi-step tasks. Unlike simple chatbots, these agents can use tools, make decisions, and navigate complex process chains—from market analysis to automated report generation. For decision-makers, this represents a new quality of process automation: not individual steps are automated, but entire workflows are intelligently orchestrated while human control points are strategically preserved.
AI Governance
AI governance encompasses the framework of policies, processes, and controls that ensures the responsible use of artificial intelligence in the organization. For Swiss organizations, this is particularly relevant: the new Data Protection Act (nDSG) sets high requirements for transparency, explainability, and data processing. A robust governance framework defines responsibilities, audit processes, risk assessments, and ethical guardrails—thereby creating the foundation for sustainable trust among customers, regulators, and employees.
Data Sovereignty
Data sovereignty refers to the principle that organizations maintain complete control over their data—from storage through processing to deletion. In the AI context, this becomes critical: training data, prompts, and generated content must remain under the organization's authority. In Switzerland, companies enjoy a natural location advantage through strict data protection laws and local server infrastructures. AI-Outsourcing.ch ensures that all AI operations preserve this sovereignty.
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