Studies

A longitudinal documentary study of 155 verified public statements from nine major organisations between 2022 and 2026, tracking how corporate leaders publicly describe internal AI adoption across five core communication registers.

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 examining how major enterprises move AI beyond optional tool availability through leadership mandates, performance reviews, incentives, workflow integration, and board governance.

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.

A documentary study of fifteen large publicly listed organisations examining where enterprise AI sits within public governance architectures, showing that AI is universally governed as software, security, or product risk rather than as an informational input to professional judgement.

A controlled empirical test evaluating whether the governance wrapping that makes a model deployable inside an enterprise systematically removes specific operational capabilities while preserving the superficial appearance of intelligence.

A systematic coding study of 714 AI-related statements across twenty-two major firms examining how corporate management publicly describes AI programmes, showing that disclosures focus heavily on deployment scale, efficiency, and product marketing while direct measured contributions to business reasoning remain almost entirely unreported.

An examination of Article 14 and Article 26 of the EU AI Act against empirical psychological research on automation bias, demonstrating that statutory oversight mandates demand cognitive capabilities humans cannot sustain during routine operation.

A longitudinal study of 150 SEC 10-K filings across 50 major US public companies comparing FY2019, FY2022, and FY2024, demonstrating a 24.5 per cent increase in the annual rate of prose drift and a shift toward diffuse hedging and framing drift following the introduction of enterprise LLM tools.