The AI Operating Model integrates AI as a team member within organizations combining people knowledge and processes using interchangeable LLMs like OpenAI Claude and Google Gemini via LiteLLM in four layers Context Execution Orchestration and Evaluation.
LLM-agnostic · DBRS-based · For any Organization Artificial intelligence is not a tool, but rather part of a system comprising context, processes, policies, and responsibility. Vision Artificial Intelligence (AI) as a team member – not a standalone solution AI creates value not in isolation, but in combination with people, knowledge, and processes. This Operating Model describes how to make that work – regardless of which models or tools are in use. Principle Multiple LLMs match to the task. No model is permanently the best. Each finds its fitting situation. Reference Implementation (DBRS) LLM-agnostic (interchangeable). OpenAI, Claude, Mistral, Google Gemini, local Models. Routed via LiteLLM as the central Gateway. Deloitte Report: Human Skills Drive High-Performing Teams in the AI Era AI works with teams that can function just fine without it—it makes them faster, stronger, and more effective. Anyone expecting artificial intelligence to compensate for organizational weaknesses is in for a disappointment. Deloitte research involving over 1,394 professionals shows that high-performing teams use AI significantly more often—and achieve measurably better results in terms of efficiency, problem-solving, and collaboration. Not because the technology saves them, but because effective teams know how to work together to implement solutions. ( https://www.deloitte.com/us/en/about/press-room/high-performing-teams.html ) An AI operating model without a cultural dimension is like an operating manual without an operation. For businesses, Experience Innovation provides a practical foundation for successful innovation and the use of AI - clarity, processes, trust, and collaboration. Architecture Four-layer AI Operating Model - each layer is interchangeable The operating model is structured in layers. The role of each layer is clearly defined. The specific implementation can be chosen freely - Microsoft Copilot and Google Gemini are also compatible. Overview Diagramme Context Layer DBRS Structured knowledge as the foundation of every AI interaction. Execution Layer AI Coding Code, prompts, and versioning. AI-capable development environment. Orchestration Layer Workflows Workflows connect the layers and make processes reproducible. Model Layer Large Lange Models (LLM) LLMs via a central gateway - no model permanently locked in. AI Agents / Agentic AI Roles of AI agents with Policies, Rules and Attitudes AI without values is merely a tool. Every agent embodies a tradition of thought with rules that go beyond its technical function. SAMY Uses Context of Tolksdorf.digital and whitelisted World Knowledge Innovation Mentor S ystem A ssistant for M entoring Y ou, Asking questions before giving answers Alan Uses detailled Knowledge around Programming and IT Alan Touring British computer pioneer who cracked the Enigma code during World War II What cannot be formalized cannot be solved Simone Independent Review Body for Governance and Policies Simone Veil Moral integrity, a passionate European An uncomfortable truth over a comfortable consensus William Systems Thinking and Quality Management W. Edwards Deming Founder of the PDCA Cycle, Systems Thinking, and Quality Look for errors in the system, not in people Working Methods Augmented Thinking & Augmented Engineering LLMs are not equalizers. They statistically identify what fits the context—and in doing so, they reinforce the unique nature of the human-LLM system. The result depends on the contributions and characteristics of both. Vibe Mode Augmented Thinking Humans and LLMs think together—fluidly, openly, and exploratively. The model isn’t controlled; it’s invited. The prerequisite: openness. The goals of the Vibe Mode are to understand what is being sought and to define the task required to achieve it. CAISE Mode (Collaborative AI Supported Engineering) Augmented Engineering Humans and LLMs work in a structured manner - precisely, in a way that can be formalized and verified. Results are reproducible and can be versioned. Prerequisite: a clear task definition and structure. Implementation of the task using engineering methods by a team consisting of humans and AI. LLM-agnostic Design LLM Usage - Model Switching as a Method There is no single best model. Consciously switching between models is not a stopgap measure - but rather a cognitive method with three proven patterns. Model switching as a cognitive method – for overcoming mental blocks, broadening perspectives, and ensuring quality automatically. Reformulation Stuck in a deadlock? Switch the model and describe the situation fresh. The re-description is already a thinking step. Perspective shift Each model weights the solution space differently – not because it is better, but because it is different. Cross-Model Review Develop a solution with model A – have model B explain it. Quality emerges from the perspective shift, not from extra effort. Data flow & learning cycle The Human & AI system learns from context - not from a model New knowledge does not flow back into the LLM, but rather into the context system via Context Engineering . The back arrow is the crucial step. Start Questions in Dialogue Wissensquelle Context of User Context System DBRS Orchestrierung n8n LLM Selection LLM Gateway Result Output Cycle: Learn → Curate → Amplify Context System Expansion of the existing ← Feedback & Reflektion Supplementing with traditional course ware and testing Positioning An AI Operating Model suitable for any organization - regardless of the tech stack This operating model applies regardless of whether you use Microsoft Copilot, ChatGPT Enterprise, Google Gemini, or a custom solution. The principles are universal. Implementation depends on your goals and environment. What connects it Knowledge · Processes · AI · Responsibility – for sustainable, actionable results. DBRS Reference Implementation DBRS · LiteLLM · n8n · Claude Code - proven in production at Tolksdorf.digital. CCR-ID: ai_operating_model VPR-ID: vpr_tolksdorf_digital Fachliche Leitung / Subject Matter Lead: Rainer Tolksdorf | Herausgeber / Publisher: Tolksdorf.digital Verified for Human & AI Interpretation | Human-in-the-Loop Content Governance