Glossary
I
12 entries beginning with I, the same definitions the articles use.
in-flow learning
Training embedded directly within existing workflows rather than delivered as separate modules, so skills are practised in real work contexts; particularly effective for domain experts and end users.
Related: Upskilling for the AI Era: Building a Future-Ready Workforce
See also:Training
Inference
The continuous running of trained AI models to produce outputs; as deployment scales, inference comes to dominate AI computing demand, making its economics central to deployment strategy.
Related: The Return of Traditional AI: Organisations Are Rethinking Their LLM-First StrategiesThe Inference Migration: What Consumer Agents Mean for Enterprise AI's Next Phase
See also:TrainingHyperscaler
Inference channel
The problem where an agent synthesises accessed data into outputs that carry no access controls, potentially surfacing sensitive intelligence to recipients who lack rights to the underlying source data.
Related: MCP Explained: The Agent Infrastructure Standard Boards Need to Understand
See also:Inference
Information asymmetry
The gap between what management knows and what a director can independently assess. AI makes the gap qualitatively different, and without deliberate development it becomes unbridgeable.
Related: The AI Talent Bifurcation: Are You Building Skills or Collecting Credentials?
See also:Directorial AI LiteracyDuty of care
Innovation Ratchets Concept
Structural mechanisms, such as escalating success metrics and mandatory talent rotation, that make backward movement difficult and keep an AI CoE focused on innovation rather than drifting into administration.
First introduced in: AI Centre of Excellence: Future-proofing Through Continuous Evolution
See also:AI centre of excellence
insecure by dumbness
A phrase from OX Security for the failure mode where non-technical users deploy AI-built applications at high velocity without security expertise, so vulnerable systems reach production faster than they can be properly evaluated.
Related: The Verification Premium: What Classical Training Reveals About AI Coding Costs
Intangible assets
Non-physical assets such as trained models, curated datasets, and prompt libraries, which appreciate through use, unlike physical assets that wear out and depreciate.
Related: The Invisible Asset: Why Boards Should Govern Data Like It's on the Balance Sheet
Intellectual capital
The judgement, creativity and relational intelligence of capable people, often trapped in undifferentiated work where those qualities add limited value while differentiating work goes undone.
Related: The Redeployment Dividend: Why AI Will Unleash Your People, Not Replace Them
See also:Undifferentiated work
Invariant
In formal specification, a mathematical statement about what must remain true throughout an operation; automated reasoning specifies behaviour using preconditions, postconditions and invariants to prove which outcomes are reachable.
See also:Automated reasoning
Investment-Value Realisation Graph Concept
The visualisation plotting AISA stages with investment (financial, people, data, process and time) on the x-axis and tangible and non-tangible value on the y-axis, reflecting varied returns.
First introduced in: Understanding the AI Stages of Adoption: A framework for business leaders
See also:AI Stages of Adoption
Irony of automation
Bainbridge’s 1983 observation that automating routine tasks strips away the practice that builds competence, turning operators into passive monitors whose skills fade until exceptions demand intervention.
Related: The Redeployment Dividend: Why AI Will Unleash Your People, Not Replace Them
See also:Cognitive atrophySelective Atrophy
ISO 42001
An AI management-system standard treated as the governance skeleton defining what must be governed, which the Complete AI Framework turns from a compliance checklist into competitive advantage.
Related: AI's Interconnected Challenge: Diagnosing the Six Concerns of the Board
See also:Complete AI Adoption Framework
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