When an organization gives its employees central access to an AI model, technical access alone is not enough. What matters is the context.
The more relevant information an organization provides in a structured way, the more helpful the AI model can become in daily work. This includes, for example:
- goals and strategic guidelines,
- roles and responsibilities,
- processes and standards,
- products and services,
- terminology and abbreviations,
- approved subject-matter knowledge, and
- rules for data protection, security and the use of AI.
Without this shared context, employees have to keep explaining the same basics in their prompts over and over again. That costs time and leads to very different results. If organizational knowledge, on the other hand, is well prepared and usable for AI, each person can get to more fitting results faster.
That makes maintaining context a shared task. Administration creates the technical and organizational foundations. The departments provide up-to-date knowledge. Leadership clarifies guardrails and responsibilities. And employees give feedback on which information is actually missing in daily work.
The productivity of an AI model therefore doesn't just depend on which model is used. It also depends on how well an organization knows, structures and makes accessible its own knowledge.
To me, that's an important point in every AI rollout: it's not just the tool that matters, but also the context we give it.
❓ How do you make sure an AI model in your organization can access the right context?