Glossary
E
13 entries beginning with E, the same definitions the articles use.
Ecosystem scaling
Extending AI capabilities through partners and value chains to create network effects, building Stakeholder Confidence and yielding faster innovation cycles than internal scaling alone.
Related: Rethinking Business Cases in the Age of AI: Creating the Foundation
See also:Network effects
Edge AI
Running AI on local devices near where data is generated, for lower latency, resilience and privacy versus cloud processing.
Related: AI Centre of Excellence: Designing Structure for Multi-Speed Governance
Eight Disciplines of AI Asset
The taxonomy of eight AI disciplines: Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Reinforcement Learning, Robotic Process Automation, Cognitive Computing, and Generative AI. The model names which discipline a piece of work actually calls for, so strategy stops treating AI as a single undifferentiated capability.
See also:AI Stages of AdoptionCognitive computingComputer visionDeep LearningGenerative AIMachine learningNatural Language ProcessingReinforcement LearningRobotic Process Automation
Embodied AI
AI operating in the physical world through robotics, autonomous systems, and computer vision; the mechanism restructuring ‘delivering’ work, driven by falling robotics unit costs rather than the language models remaking cognitive work.
Signal: Embodied AI
Related: AI Centre of Excellence: Future-proofing Through Continuous Evolution
See also:Computer vision
Emergent Threat Paradox Concept
AI risks evolve through learning, adaptation and interaction in ways traditional risk frameworks cannot anticipate, so established controls fail against systems that continuously learn and change.
First introduced in: AI's Interconnected Challenge: Diagnosing the Six Concerns of the Board
Employee engagement
The degree of employees’ emotional commitment and involvement at work; weak engagement correlates with weaker AI adoption.
Related: Europe's AI challenge: Why culture trumps capital in technology adoption
See also:Leading indicatorLagging indicator
Energy sovereignty
A nation’s or organisation’s ability to secure sufficient, affordable, and reliable power for AI, increasingly a determining competitive factor as data centre demand grows.
Related: AI Sovereignty: A Board's Guide to Navigating Conflicting National Agendas
See also:Sovereignty ParadoxGrid actor
Enterprise Risk Management
An organisation’s established framework for identifying, assessing, and governing risk; AI-specific risks belong inside it rather than in parallel structures.
Related: From Shadow AI to Strategic Asset: Building Your AI Centre of Excellence
Enterprise SSO
Single sign-on authentication through corporate systems, mandated as a non-negotiable guardrail so employees access AI tools via governed enterprise accounts rather than personal logins or credit cards.
Related: After the AI Amnesty: Practical Steps to Operationalise Discovered Shadow AI
Entrepreneurs' relief
A UK tax concession announced under business pressure that kept capital gains tax unchanged on the first £1m of an entrepreneur’s lifetime gains, described here as an inadequate, belated response.
EU AI Act
The world’s first comprehensive legal framework governing AI systems, in force from August 2024, applying a risk-based approach with penalties scaled to global turnover.
Related: Navigating the AI Regulatory Maze: A Boardroom Survival Guide
See also:General-purpose AIGPAI Code of PracticeSystemic riskModel Documentation Form
Executive sponsorship
Visible leadership backing for a change programme, where an engaged sponsor shares the vision at all levels, seeks diverse perspectives to drive collaborative decision-making, and creates cross-functional, empowered teams to unblock organisational change.
Related: Why now is not the time to take your foot off the gas
See also:AI champion
expertise pipeline
The junior-to-senior progression where juniors develop pattern recognition by writing code that seniors review; using AI to bypass junior work erodes the future supply of experts able to verify AI outputs.
Related: The Accountability Gap: When AI Delegation Meets Human Responsibility
See also:Verification capability
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