AI Automation Glossary

A 0–100 index measuring how likely a job role is to be significantly impacted by AI automation.

The percentage of a role's tasks that can be fully or substantially performed by AI systems available today.

A 0–10 rating indicating how well a job role can withstand AI automation pressure through adaptation, upskilling, or task evolution.

The probability that a given job role will be eliminated or significantly restructured by AI automation within a defined time horizon.

The mode of AI integration where AI assists humans with a task rather than replacing them — increasing output quality and speed while keeping the human in the loop.

The subset of tasks within a job role that require human judgment, creativity, empathy, or physical presence — and are therefore resilient to AI automation.

The estimated timeframe within which AI automation will significantly restructure a job role — typically expressed as a range (e.g., "3–5 years").

The hollowing out of middle-skill jobs as automation eliminates routine work, causing employment to concentrate at the high-skill and low-skill ends of the wage spectrum.

The use of AI to perform tasks that require reasoning, language understanding, analysis, or decision-making — previously considered exclusively human intellectual work.

The use of robotics and automated machinery to perform manual, dexterous, or physically demanding work previously performed by human workers.

Forecasts of when AI systems will reach the capability threshold required to automate a specific job role at scale, expressed as a range of years.

The process of learning new, higher-level skills within your current career domain to stay relevant as AI automates lower-level tasks in your field.

Learning an entirely new set of skills to transition into a different career when your current role faces irreversible automation displacement.

A workflow design where a human reviews, validates, or corrects AI outputs before they take effect — maintaining oversight and accountability in automated systems.

A quantitative measure derived from the O*NET occupational database that estimates the automatability of a job based on its constituent task descriptors.

The share of a job's tasks that are routine — following explicit rules or procedures — relative to non-routine tasks requiring judgment, adaptability, or creativity.

The economic dynamic where AI makes certain human skills more valuable rather than less, by increasing the productivity and leverage of those skills.

The skill of designing, structuring, and refining inputs to AI language models to reliably produce high-quality, task-specific outputs.

The use of AI tools to enhance human capability, judgment, and output quality — increasing worker productivity rather than replacing workers entirely.

The analytical process of breaking a job role into its individual constituent tasks to assess automation risk at the task level rather than the role level.

An umbrella term for research, policy, and planning focused on how technology, globalization, and demographic change are transforming employment, work organization, and labor markets.

The loss of employment by workers due to automation, technological change, or structural economic shifts that reduce demand for their skills.

The emergence of new roles and occupations resulting from technological adoption — including jobs that manage, maintain, and build AI systems, and roles enabled by AI-generated productivity growth.

Unemployment caused by technological change that replaces human labor, which can be temporary (transitional) or permanent (structural) depending on the pace of economic adaptation.

Interpersonal, emotional, and social competencies — such as empathy, communication, leadership, and conflict resolution — that are difficult for AI to replicate and increasingly valuable in automated workplaces.

Technical, measurable, and teachable competencies specific to a profession — increasingly automatable as AI systems master domain-specific knowledge and procedural tasks.

Joseph Schumpeter's economic concept describing how technological innovation destroys existing industries and jobs while simultaneously creating new ones — the engine of long-run economic growth.

The human tendency to over-rely on automated systems and AI recommendations, reducing critical evaluation and increasing the risk of undetected AI errors.

The ability to understand how AI systems work, evaluate their outputs critically, use them effectively in professional contexts, and make informed decisions about when to rely on them.

The organizational process of integrating digital technology and AI into all areas of a business, fundamentally changing how it operates and delivers value — with significant implications for workforce requirements.

A worker whose primary output is information, analysis, or expertise rather than physical goods — and who therefore faces the most direct impact from cognitive AI automation.

A labor market characterized by short-term contracts and freelance work rather than permanent jobs — increasingly intersecting with AI automation as platforms both enable and threaten gig work.

Robots designed to work alongside human workers in shared physical spaces, augmenting human capability rather than operating in fully isolated automated environments.

AI systems capable of creating new content — text, images, code, audio, or video — that is novel and coherent, trained on large datasets to learn the statistical patterns of human-created outputs.

An AI model trained on massive text datasets that can understand, generate, and reason about human language — the technology underlying ChatGPT, Claude, Gemini, and most modern AI tools.

AI systems that autonomously plan and execute multi-step tasks over extended time horizons, taking real-world actions (web browsing, code execution, file management) without continuous human guidance.

The salary and productivity advantage earned by workers who effectively integrate AI tools into their workflow, commanding higher output and compensation relative to non-augmented peers.

A role's structural capacity to withstand automation pressure — determined by the mix of tasks, skills, and context requirements that make it difficult for AI to fully replicate the job's value.

An intentional, strategic shift from one career path to a different one — often triggered by automation pressure on the current role and the availability of transferable skills applicable to a more resilient field.

The additional salary percentage that professionals who effectively use AI tools command over peers who do not, within the same job role.