KNOWLEDGE BASE
AI Insights & Thought Leadership
Practical knowledge, strategic frameworks and in-depth analyses on agentic AI, governance, adoption and enterprise AI transformation.
What is Agentic AI?
Agentic AI goes beyond traditional chatbots and automation. Discover how autonomous AI agents transform workflows, make decisions and collaborate within enterprise environments.
Read article →Core Concepts
Foundational knowledge about enterprise AI — from governance to prompt engineering.
AI Governance Frameworks
How do you build a governance structure that enables AI innovation without losing control?
Read about AI Governance Frameworks →Enterprise AI Roadmap
A step-by-step approach to building an enterprise AI roadmap that delivers value.
Read about Enterprise AI Roadmap →LLM Orchestration in Practice
How do you orchestrate multiple AI models in an enterprise environment? Patterns and best practices.
Read about LLM Orchestration in Practice →What is AI Governance?
Everything about AI governance: principles, implementation and why it is essential for responsible AI use.
Read about What is AI Governance? →What is AI Security?
Data sovereignty, prompt injection defence and secure-by-design principles for enterprise AI.
Read about What is AI Security? →What is AI Readiness?
How do you measure AI maturity and what are the dimensions of a successful AI readiness assessment?
Read about What is AI Readiness? →What is LLM Orchestration?
Integrating Large Language Models into business processes: architecture, RAG and prompt engineering.
Read about What is LLM Orchestration? →What is a knowledge layer?
The shared source of truth beneath your organisation: readable for people, readable and writable for agents. Including what our own knowledge layer surfaced in its first days.
Read about What is a knowledge layer? →What is an AI platform?
The difference between standalone AI tools and a platform: a shared knowledge layer, connections, oversight and ongoing management. With the figures on why AI stalls for almost everyone.
Read about What is an AI platform? →What is AI literacy?
Article 4 of the EU AI Act has obliged you since 2 February 2025 to ensure your people are sufficiently AI literate. No exam, but it must be demonstrable.
Read about What is AI literacy? →What is GPAI?
The chapter of the AI Act on general-purpose models. Two roles, and the line where you quietly turn from deployer into provider.
Read about What is GPAI? →Large or small language model?
Four trade-offs: cost, speed, quality and sovereignty. Data sovereignty without model sovereignty is half a story.
Read about Large or small language model? →What is MCP?
Model Context Protocol: the open standard that lets agents reach the systems you already run. Forty bespoke connectors, or thirteen connections. Plus what MCP does not solve.
Read about What is MCP? →What is context engineering?
What the model carries with it when it answers: the instruction, the knowledge layer, the tools, memory and boundaries. And what became of prompt engineering.
Read about What is context engineering? →AI Change Management
Why technology alone is not enough. A strategic approach for sustainable AI adoption in teams.
Read about AI Change Management →All insights, connected
From fundamentals to strategic comparisons — our knowledge base forms a cohesive whole.
Comparisons & Analyses
In-depth analyses, comparisons and practical decision frameworks.
In-house AI vs. Consultancy
When do you build an internal AI team and when do you engage a consultancy? Decision framework and trade-offs.
Read about In-house AI vs. Consultancy →Build vs. Buy: AI Platforms
Custom AI development or platform solution? A decision model for making the right choice.
Read about Build vs. Buy: AI Platforms →EU AI Act vs. GDPR
Two regulatory frameworks compared: differences, overlap and a combined compliance strategy.
Read about EU AI Act vs. GDPR →RAG vs. Fine-tuning
When do you choose Retrieval Augmented Generation and when fine-tuning? A technical comparison.
Read about RAG vs. Fine-tuning →Traditional AI vs. Agentic AI
How does agentic AI differ from traditional AI? Architecture, capabilities and enterprise impact.
Read about Traditional AI vs. Agentic AI →Cloud vs. On-Premise AI
Cloud AI versus on-premise deployment: data sovereignty, costs and hybrid strategies.
Read about Cloud vs. On-Premise AI →Directly applicable
Step-by-step plans, frameworks and immediately applicable knowledge for your AI journey.
AI Transformation Roadmap
A 5-phase transformation plan: from assessment to scalable AI implementation.
Read about AI Transformation Roadmap →AI Security & Data Sovereignty
Secure-by-design principles, data ownership and threat models for enterprise AI systems.
Read about AI Security & Data Sovereignty →AI Enterprise Architecture
From fragmentation to coherence: four architecture layers for enterprise AI systems.
Read about AI Enterprise Architecture →Agentic Systems in Practice
Four core competencies, enterprise applications and architecture patterns for agentic AI.
Read about Agentic Systems in Practice →LLM Integration Guide
From proof-of-concept to production: layered architecture, RAG and LLM governance.
Read about LLM Integration Guide →AI Governance Compliance
Closing the governance gap: three pillars, the AI register and scaling governance with maturity.
Read about AI Governance Compliance →Translate knowledge into action?
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