MKAI is a public research archive on AI and organisations.

The committed prompt set and scoring criteria for a comparative study of how different AI setups respond to strategic decisions from senior leaders. Five scenarios run through three setups each, producing fifteen answers scored blind on whether they take a clear position, add fresh insight, and serve as material a senior leader could debate. The methodology was locked before any outputs were generated.

A public-record study of how large organisations require, incentivise, and formalise AI engagement through mandates, incentives, training, workflow integration, and board-level expectation.

A review of the public record revealing the significant corporate disclosure gap regarding exactly how senior executives at large organisations access AI capabilities.

A reading of nine public frameworks that guide boards and audit leaders on AI oversight. None require anyone to know about the organisation-authored instruction that controls what the AI produces: who wrote it, when it was last changed, or how it affects answers. The phrase "system prompt" does not appear in any document reviewed.

A documentary reading of seven enterprise AI deployments showing that public administration and compliance documentation does not recognise executive rank as a query-time basis for varying the governed layer.

A reading of the public terms for seven enterprise AI services. Across all seven vendors, the contracts make the customer responsible for AI output. The same contracts describe controls that affect that output: filters, system instructions, safety features, retrieval rules, data masking, and classifiers. The clauses that assign responsibility do not mention those controls.