Glossary
H
9 entries beginning with H, the same definitions the articles use.
Hallucination
When an AI system generates information that is fluent and confident but incorrect or fabricated. For a Board, it means any AI output feeding a decision needs a verification step you can name before you rely on it, because the system gives no signal of when it is wrong.
Related: The Accountability Gap: When AI Delegation Meets Human Responsibility
Harness
The scaffolding built around a model, the tools, retrieval, memory and control flow, that turns a raw capability into a system able to do useful work. It matters because most of what determines whether an AI system is reliable or dangerous sits in the harness, not the model, and it is the part an organisation actually builds and controls.
Hub-and-spoke model
A governance structure with a central AI CoE hub setting standards and Board reporting, and spokes embedded in business units providing local implementation, adapted from AWS Cloud Centre of Excellence designs.
Related: AI Centre of Excellence: Designing Structure for Multi-Speed Governance
Human in the loop
Human oversight kept inside an AI-driven process, so a person remains involved in the decisions it makes rather than relying on the system unchecked.
Related: Harnessing AI for organisational change led from the Board
Human Residual Concept
The durable human contribution that survives in each dimension of work as AI advances: judgement in thinking, accountability in deciding, taste and originality in creating, and adaptability and trust in delivering.
First introduced in: The Great Remaking: How the Four Dimensions of Work Are Transforming
Related: The Great Remaking: AI and the Race to Transform the Very Essence of Work
See also:The Essence of Work
Hybrid architecture
A design that blends generative AI with rules-based automation and traditional machine learning, using deterministic systems for execution while generative AI provides contextual intelligence where interpretation matters more than precision.
Related: The Return of Traditional AI: Organisations Are Rethinking Their LLM-First Strategies
See also:Generative AIMachine learning
Hype-Driven Assessment Metrics Concept
Judging AI progress by short-term ROI and perceived importance rather than actual integration, an overestimation pattern that hardens where organisations fail to track AI impact at all.
First introduced in: The AI Maturity Mirage: Diagnosing the Gap Between Investment and Readiness
See also:AI Maturity MiragePilot Success Trap
Hyperscaler
A large-scale cloud and AI infrastructure operator such as Microsoft, AWS, Meta, Google or Oracle, now building gigawatt-scale generation to secure the power their compute requires.
Related: UK AI Energy Constraints: From Niche Concern to Investment Banking Focus
Hypnosis
The subtle influence AI might exert on human decision-making processes; maintaining human oversight is crucial to avoid unintended manipulation.
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