Glossary
A
34 entries beginning with A, the same definitions the articles use.
Accountability Gap Concept
When an organisation delegates work to AI without building the capability to verify it, leaving people answerable for outputs no one has actually checked. For a Board, no delegation to AI should be approved without also approving who checks the output and how, because accountability without a verification step is accountability in name only.
First introduced in: The Accountability Gap: When AI Delegation Meets Human Responsibility
Related: The Accountability Gap: When AI Delegation Meets Human Responsibility
See also:Hallucination
ADAPT Concept
ADAPT is the implementation motion of the Remake framework: the stage-based way of working that takes a remaking from ambiguity to durable change through five stages, Remake: Align, Remake: Diagnose, Remake: Advise, Remake: Plan, and Remake: Transform. Progression between stages is earned, not scheduled, and every unit of work is scoped to one strategic problem, one sponsor, and one decision space, so a director always knows what an ADAPT engagement is actually authorising.
First introduced in: ADAPT
Adaptive Localisation Concept
A strategy of running different AI approaches in different markets, tuned to each market’s regulatory, cultural, or competitive conditions, deliberately trading consistency for regional advantage. For a Board, choosing this stance means accepting real complexity costs and being ready to answer why the organisation treats one market’s rules differently to another’s.
First introduced in: AI Sovereignty: A Board's Guide to Navigating Conflicting National Agendas
Related: AI Sovereignty: A Board's Guide to Navigating Conflicting National Agendas
See also:Principled StandardisationSovereign SpecialisationAI Sovereignty Trilemma
Agency versus accountability
Agency, the doing of specific work, can be delegated to AI; accountability, answerability for the outcome, cannot be transferred with it. For a Board, this means every AI-assisted output still needs human judgement applied to it, because ratifying what the machine produced is not the same as exercising the judgement the role requires.
Related: The Accountability Gap: When AI Delegation Meets Human ResponsibilityAI and the Chair: Governing the Board Through The Great Remaking
See also:Accountability GapSummary Substitution FailureTransfer of agencyCollective responsibility / accountability
Agentic AI
AI systems with genuine autonomy: rather than simply responding to a query or predicting an outcome, they understand a goal, devise a strategy, execute a plan, and adapt based on the results, coordinating across systems without a human at each step.
Signal: Agentic AI
Related: How Agentic AI Turns Your Biggest Tech Problem into Competitive AdvantageAgentic AI: an explainerThe Compound Loop: Why Agentic AI's Real Power Lies Beyond Generative AI
AI agent
An AI system that plans, draws on tools and information, and carries out multi-step tasks within a loop it drives itself, rather than answering a single prompt. Its work is only ever as good as the data and tools it can reach, so the governing question is not how capable the agent is but what you have connected it to.
Related: AI Centre of Excellence: Future-proofing Through Continuous EvolutionMCP Explained: The Agent Infrastructure Standard Boards Need to Understand
See also:Agentic AIHarnessLoopMCP server
AI Amnesty Asset
The time-boxed programme that brings shadow AI under governance: a declaration window, typically 30 to 45 days, in which employees disclose all AI tool usage without fear of punishment, followed by the post-amnesty roadmap of rapid triage, governance guardrails, pilot launch, and ongoing operations. Its declarations populate the AI/ML register, and its findings are read with the AI Amnesty Questionnaire. For a Board, the no-reprisal commitment must be real and followed by visible action, otherwise disclosed usage simply retreats back into the shadows.
Related: AI Centre of Excellence: Your First 90 Days With Well-Advised Value FocusShadow AI and the Case for an AI AmnestyAfter the AI Amnesty: Practical Steps to Operationalise Discovered Shadow AI
See also:AI Amnesty QuestionnaireShadow AIShadow AI DiscoveryRisk-based triageAI/ML registerMinimum Lovable GovernanceGuardrails
AI Amnesty Questionnaire Asset
The structured instrument for running an AI amnesty: ten sections capturing which tools employees actually use, the use cases they serve, the data they touch, the value already created, and the risks encountered, turning unknown unknowns into a governable inventory.
See also:AI AmnestyAI CoE SimulatorAI Initiative RubricShadow AI
AI answer engine
Systems such as Claude, ChatGPT, Google AI Overviews, and Perplexity that synthesise and enhance information to answer user queries directly, rather than simply linking users to source websites as search engines did in the web era.
Related: From Print to Web to AI: Creating Sustainable Value in the AI Era
AI Business Case Asset
The integrated decision framework that crystallises across an ADAPT engagement rather than at a single stage: strategic alignment established at Align, cost and readiness evidenced at Diagnose, value shaped at Advise, and execution designed at Plan. The Investment Case is its directional form; the Transformation Plan carries its validated form.
AI Capability Bifurcation Concept
The split between workers who build genuine capability to verify and apply judgement to AI outputs, who command a premium, and those who merely accumulate credentials or tool exposure, who face an earnings penalty. For a Board, the same divide runs through the boardroom itself: familiarity with AI tools is not the same as the capability to challenge what the organisation actually does with them.
First introduced in: The AI Talent Bifurcation: Are You Building Skills or Collecting Credentials?
Related: AI and the Chair: Governing the Board Through The Great RemakingThe AI Talent Bifurcation: Are You Building Skills or Collecting Credentials?
See also:Verification PremiumVerification capabilityRedeployment Dividend
AI centre of excellence
A cross-functional governance function bridging technical implementation and regulatory compliance, ideally with direct reporting lines to the Board’s risk committee and authority to enforce AI governance standards.
Related: Harnessing AI for organisational change led from the BoardWhy Boards need an AI Centre of ExcellenceAI Centre of Excellence: Mapping Your Multi-Speed AI Reality
See also:AI Stages of AdoptionFive Pillars of AI CapabilityAI championCommunities of practice
AI champion
A business leader who grasps AI’s potential and limitations, becoming a local evangelist and first-line support, one of a distributed network that scales expertise beyond what any central team could provide.
Related: Understanding the AI Stages of Adoption: A framework for business leadersAI Centre of Excellence: Scaling Beyond Pilots to Enterprise Transformation
AI co-pilot
An AI assistant embedded in workflows to help with common, time-consuming tasks, freeing people for more strategic work and helping overcome challenges such as ‘blank page syndrome’.
AI CoE Simulator Asset
An interactive assessment tool operationalising the AI Stages of Adoption, objectively placing each business function within adoption stages using specific criteria rather than subjective self-assessment, revealing an organisation’s multi-speed AI reality.
See also:RemakeAI Stages of AdoptionAI Initiative RubricMulti-Speed Adoption
AI Initiative Rubric Asset
A pilot evaluation tool that scores candidate initiatives across the five Well-Advised value priorities and Five Pillars capability building, recommending whether to prioritise, defer, pipeline, or develop them further.
See also:RemakeAI CoE SimulatorWell-AdvisedFive Pillars of AI CapabilityProcess Audit
AI Maturity Mirage Concept
Mistaking visible tool deployments and isolated pilot wins for genuine organisational capability, a systematic overestimation that derails transformation strategies. For a Board, correcting it means diagnosing actual capability against AISA and the Five Pillars rather than trusting the appearance of activity.
First introduced in: The AI Maturity Mirage: Diagnosing the Gap Between Investment and Readiness
Related: The AI Maturity Mirage: Diagnosing the Gap Between Investment and Readiness
See also:Tool-Centric IllusionPilot Success TrapAI Stages of AdoptionFive Pillars of AI Capability
AI readiness
An organisation’s preparedness to adopt AI across four dimensions: systemic readiness, leadership readiness, team readiness, and HR readiness.
Related: Europe's AI challenge: Why culture trumps capital in technology adoptionIntroducing the AI Stages of Adoption: A framework for understanding AI readiness in your business
See also:Employee engagement
AI safety
The field concerned with preventing harmful, unintended or uncontrolled behaviour from AI systems, particularly the most capable ones.
Related: AI's Interconnected Challenge: Diagnosing the Six Concerns of the Board
AI Skills Paradox Concept
AI simultaneously threatens to automate certain roles while creating acute talent shortages in others, requiring organisations to prepare workers for both displacement and new opportunities at once.
First introduced in: The future of AI expertise: Building and managing AI-capable teams
Related: Upskilling for the AI Era: Building a Future-Ready WorkforceThe future of AI expertise: Building and managing AI-capable teams
See also:Strategic augmentation
AI Sovereignty Trilemma Asset
The proposition that organisations and jurisdictions can optimise their AI posture for trust, speed or control, but not all three simultaneously, forcing deliberate strategic positioning rather than attempting to serve every market at once.
Related: When a frontier model vanishes and reality bites
See also:Frontier model
AI Stages of Adoption Asset
A framework describing five stages of the AI journey, Experimenting, Adopting, Optimising, Transforming and Scaling, plotted on an investment-value graph, recognising that different functions progress simultaneously at different paces.
Related: AI Stages of Adoption: an explainerAI Strategy briefingA Complete AI Adoption Framework: AISA, Five Pillars, and Well-Advised
See also:Five Pillars of AI CapabilityComplete AI Adoption FrameworkMulti-Speed Adoption
AI vulnerability management
Continuous monitoring for AI-specific threats such as data poisoning, adversarial examples, and model manipulation, distinct from traditional software vulnerability management.
Related: AI Centre of Excellence: Moving Beyond Shadow AI Risk to Scaled AI AdoptionAI Centre of Excellence: The Essential Functions of the Five Pillars
AI washing
Overstating a company’s AI adoption or capabilities to the market, typically actionable as securities misrepresentation under existing disclosure law rather than under any new AI-specific rule.
Related: From AI Pilots and Projects to AI Strategy: Avoiding the Business Case Trap
AI/ML charter
A public, organisation-wide document setting the criteria for deciding when to use AI and ML, focused on transparency, compliance, fairness and safety, intended to build trust and drive appropriate use rather than block it.
Related: The Board in the machine
AI/ML register
An inventory, much like an asset register, capturing every AI and machine learning application in use across a business, its purpose and stakeholders, built from a discovery exercise that extends beyond IT into shadow usage.
Related: The Board in the machine
See also:Machine learningAI Amnesty
Algorithmic bias
Systematic unfairness in AI outputs arising from skewed training data or design, which can produce discriminatory or unequal outcomes.
Related: From Shadow AI to Strategic Asset: Building Your AI Centre of Excellence
See also:Training
Alignment
The stage where a provider shapes a model’s behaviour on refusal, tone, framing and contested questions through techniques such as reinforcement learning and constitutional methods, encoding genuine ethical choices.
Related: Upskilling for the AI Era: Building a Future-Ready Workforce
See also:Reinforcement Learning
Always-on agents
Persistent AI agents that maintain context across days and weeks and run continuous inference around the clock, inverting the diurnal usage patterns that current AI infrastructure was designed to accommodate.
Related: The Inference Migration: What Consumer Agents Mean for Enterprise AI's Next Phase
Antifragile governance
Governance, drawing on Nassim Taleb’s concept, designed to improve under stress rather than merely survive it, through productive conflict, red teams, optionality over optimisation, and distributed evolution.
Related: AI Centre of Excellence: Future-proofing Through Continuous Evolution
Artificial general intelligence
A hypothetical AI able to match or exceed human performance across essentially any intellectual task, rather than one narrow domain.
Related: Harnessing AI for organisational change led from the Board
Artificial intelligence
Computer systems that perform tasks normally requiring human intelligence, including reasoning, perception, language and decision-making.
Related: Edge Markets
Automated reasoning
The application of formal logic, constraint solving and mathematical proof at computational speed to determine what is provably true within a defined system, exhaustively analysing every possible state rather than pattern-matching against historical data.
Signal: Automated Reasoning
Related: From Probable to Provable: What Automated Reasoning Means for the Board
Automation complacency
The risk that anyone relying on AI-powered outputs stops exercising independent judgement. For a Board, the mitigation is structural: presenting multiple options rather than single recommendations, and running challenge exercises where directors articulate their own reasoning.
Related: Implementing Decision Analytics: A Practical Guide for Boards
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