Glossary

A numerical score (0-100) that indicates how vulnerable a job is to automation by AI and technology. Higher scores suggest greater automation risk, while lower scores indicate jobs that are more resistant to automation.

A set of rules or instructions designed to solve a problem or perform a computation. In AI and automation, algorithms process data and make decisions based on predefined logic or learned patterns.

Common alternative names or job titles used in the industry for a specific occupation. Alternate titles help users find jobs using familiar terminology.

A set of protocols and tools for building software applications. APIs define how different software components should interact, enabling automation and integration between systems.

The simulation of human intelligence in machines that are programmed to think, learn, and make decisions. AI systems can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.

The use of technology to perform tasks with minimal human intervention. Automation can range from simple rule-based systems to complex AI-driven processes that adapt and learn.

The likelihood that tasks within a job can be automated using current or emerging technology. Automation risk considers factors like routine tasks, data processing requirements, and physical manipulation needs.

Systematic errors or unfairness in AI systems that can lead to discriminatory outcomes. Understanding AI bias is important when evaluating automation risks and AI applications.

Extremely large datasets that require specialized tools and techniques to process and analyze. Big data is characterized by volume, velocity, and variety, and is essential for training effective AI models.

A distributed ledger technology that maintains a continuously growing list of records secured using cryptography. While primarily associated with cryptocurrency, blockchain has automation and AI applications.

A curated list of the 20 jobs with the lowest AI automation risk scores, representing occupations that are most resistant to automation and likely to remain stable.

A discipline that combines knowledge from information technology and management sciences to optimize business processes. BPM focuses on improving corporate performance by managing and optimizing business processes.

The process of setting career goals and developing strategies to achieve them. Understanding AI risk scores helps individuals make informed career planning decisions.

The likelihood that a career or occupation will remain viable and in demand over time. Jobs with low automation risk typically offer greater career stability.

The process of moving from one career or job to another, often driven by industry changes, automation, or personal goals. Successful transitions often require reskilling and strategic planning.

A computer program designed to simulate conversation with human users, typically over the internet. Modern chatbots use AI and NLP to understand and respond to user queries in natural language.

The delivery of computing services including servers, storage, databases, networking, software, and analytics over the internet. Cloud computing enables scalable automation and AI services.

Job tasks that require thinking, reasoning, problem-solving, and decision-making. Cognitive tasks vary in their automation potential, with routine cognitive tasks being more automatable.

A field of AI that enables machines to interpret and understand visual information from the world. Computer vision is used in image recognition, autonomous vehicles, medical imaging, and quality control.

A field of AI that enables machines to interpret and understand visual information from the world. Computer vision is used in image recognition, autonomous vehicles, medical imaging, and quality control.

Job tasks that involve originality, artistic expression, or novel problem-solving. Creative tasks are typically more resistant to automation than routine tasks.

The science of analyzing raw data to make conclusions about that information. Data analytics is used to calculate AI risk scores and provide job insights.

The process of discovering patterns and knowledge from large amounts of data. Data mining techniques are used in machine learning, business intelligence, and predictive analytics.

The collection and manipulation of data to produce meaningful information. Automated data processing is essential for AI systems and business intelligence applications.

An interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Data science combines statistics, machine learning, and domain expertise.

A subset of machine learning that uses neural networks with multiple layers to learn complex patterns in data. Deep learning is particularly effective for tasks like image recognition, natural language processing, and speech recognition.

The integration of digital technology into all areas of a business, fundamentally changing how organizations operate and deliver value. Digital transformation often involves automation and AI adoption.

A distributed computing paradigm that brings computation and data storage closer to the location where it is needed. Edge computing enables real-time AI and automation at the source of data.

The ability to recognize, understand, and manage emotions in oneself and others. Jobs requiring high emotional intelligence are generally less automatable.

The practice of developing and deploying AI systems in ways that are fair, transparent, accountable, and beneficial to society. Ethical considerations are crucial as AI becomes more prevalent.

An AI system that mimics the decision-making ability of a human expert in a specific domain. Expert systems use knowledge bases and inference engines to solve complex problems.

The process of selecting, modifying, or creating input variables (features) for machine learning models. Good feature engineering can significantly improve model performance and accuracy.

The evolving landscape of employment shaped by technological advances, automation, AI, and changing economic conditions. Understanding the future of work helps individuals prepare for career changes.

AI systems that can create new content, including text, images, music, code, and videos. These systems learn patterns from training data and generate original outputs based on prompts.

A labor market characterized by short-term contracts or freelance work as opposed to permanent jobs. The gig economy has grown alongside automation and digital platforms.

The partnership between humans and AI systems to achieve better outcomes than either could alone. Many jobs will evolve to focus on collaboration rather than replacement.

A work model that combines remote and in-office work. Hybrid work arrangements are becoming more common as technology enables flexible work arrangements.

A business-driven approach to identify, vet, and automate as many business and IT processes as possible using advanced technologies like AI, machine learning, and RPA.

The combination of AI technologies with automation to create systems that can learn, adapt, and make decisions. Intelligent automation goes beyond rule-based automation to handle complex, variable processes.

The network of physical objects embedded with sensors, software, and connectivity to collect and exchange data. IoT enables automation in manufacturing, smart homes, and various industries.

The systematic process of gathering and analyzing information about the content, context, and requirements of a job. Job analysis is fundamental to understanding automation potential.

The phenomenon where technological advances, particularly automation and AI, reduce or eliminate the need for human workers in certain roles. This can occur when machines can perform tasks more efficiently or cost-effectively than humans.

Actionable information and recommendations provided for each job profile, including which tasks are most automatable, skills to develop, and strategies to stay competitive in an AI-driven economy.

Patterns and changes in employment, including job growth, decline, and transformation. Understanding job market trends helps workers prepare for automation impacts.

A comprehensive overview of a specific occupation on WillAIReplaceMe.io, including the official job title, alternate titles, AI risk score, detailed analysis, and actionable insights for workers in that field.

The process of finding employment opportunities. On WillAIReplaceMe.io, job search includes finding jobs by title, browsing by category, and understanding automation risk.

The probability that an individual will keep their job. In the context of AI and automation, job security refers to how resistant a position is to technological replacement.

The evolution of job roles as technology changes how work is performed. Rather than complete replacement, many jobs are transformed, requiring new skills while eliminating some traditional tasks.

The degree to which a job is susceptible to being replaced or significantly altered by automation and AI. Vulnerability is assessed based on the nature of tasks, required skills, and technological capabilities.

AI models trained on vast amounts of text data to understand and generate human-like text. Examples include GPT, BERT, and Claude. LLMs can perform tasks like writing, coding, translation, and answering questions.

The ongoing, voluntary pursuit of knowledge and skills throughout one's life. In an era of rapid technological change, lifelong learning is essential for career adaptability.

A subset of AI that enables systems to learn and improve from experience without being explicitly programmed. Machine learning algorithms build mathematical models based on training data to make predictions or decisions.

Physical job tasks that require dexterity, coordination, and physical manipulation. While some manual tasks can be automated through robotics, others remain challenging for automation.

A measure of how well a machine learning model performs on unseen data. Model accuracy is crucial for reliable AI predictions and automation decisions.

The process of teaching a machine learning model to recognize patterns and make predictions by exposing it to training data. During training, the model adjusts its parameters to minimize errors.

A branch of AI that helps computers understand, interpret, and generate human language. NLP enables applications like chatbots, translation services, sentiment analysis, and voice assistants.

Computing systems inspired by biological neural networks. They consist of interconnected nodes (neurons) that process information and can learn to recognize patterns, making them fundamental to deep learning and AI.

Tasks that require creativity, complex problem-solving, social intelligence, or adaptability. Jobs with high non-routine task content are typically less vulnerable to automation.

The Occupational Information Network, a comprehensive database of worker attributes and job characteristics maintained by the U.S. Department of Labor. O*NET provides detailed information on over 900 occupations, including skills, knowledge, abilities, and work activities.

Characteristics and requirements of a job, including skills, knowledge, abilities, work activities, and work context. These attributes are analyzed to determine automation potential.

A system for categorizing and organizing jobs and occupations based on their characteristics, skills, and activities. Systems like O*NET and SOC provide standardized occupational classifications.

A structured collection of information about jobs and occupations. O*NET is the primary occupational database used for job analysis and automation risk assessment.

Detailed analysis and information about specific occupations, including automation risk, required skills, and career outlook. Occupational insights help workers understand their field.

The standardized, formal name for an occupation as defined by O*NET. Official titles are used for classification and analysis purposes.

A unique identifier assigned to each occupation in the O*NET database. ONET codes follow the format XX-XXXX.XX and are used to classify and reference specific job types for analysis and research.

A modeling error in machine learning where a model learns the training data too well, including noise and outliers, resulting in poor performance on new data. Overfitting is a common challenge in AI development.

The automated identification of patterns and regularities in data. Pattern recognition is fundamental to machine learning and is used in image analysis, speech recognition, and data mining.

The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes. In job analysis, predictive analytics helps forecast automation potential.

The use of technology to automate repetitive business processes, reducing manual effort and human error. This includes robotic process automation (RPA), workflow automation, and business process management.

Activities and learning opportunities that enhance professional skills and knowledge. Professional development is essential for adapting to automation and AI-driven changes.

A computing paradigm that uses quantum mechanical phenomena to perform calculations. Quantum computing has potential applications in AI, optimization, and solving complex problems.

A machine learning approach where an agent learns to make decisions by interacting with an environment and receiving rewards or penalties. The agent improves its strategy over time through trial and error.

A work arrangement where employees perform their job duties from locations outside the traditional office, often enabled by technology. Remote work has implications for automation and job structure.

The process of learning new skills to transition to a different job or career. As automation changes the job market, reskilling helps workers adapt to new roles and industries.

The process of identifying and evaluating potential risks. In the context of jobs, risk assessment involves analyzing automation potential and job vulnerability.

A categorical classification of automation risk: Low Risk (0-30%), Mid Risk (31-69%), or High Risk (70-100%). Risk levels provide a quick understanding of a job's vulnerability to automation.

Software technology that mimics human actions to automate repetitive, rule-based tasks. RPA bots can interact with applications, process data, and perform routine tasks without human intervention.

The intersection of engineering and computer science that involves designing, constructing, and operating robots. Modern robotics often incorporates AI to enable autonomous decision-making and learning.

Repetitive, predictable tasks that follow a set pattern or rules. Jobs with high routine task content are generally more susceptible to automation than those requiring creativity, problem-solving, or human interaction.

The difference between the skills that employers need and the skills that workers possess. As automation changes job requirements, skill gaps emerge, requiring reskilling and upskilling initiatives.

The knowledge, abilities, and competencies needed to perform a job successfully. Understanding skill requirements helps assess automation potential and identify reskilling needs.

Standard Occupational Classification code, a system used by federal statistical agencies to classify workers into occupational categories. SOC codes help standardize job classifications across different data sources.

The ability to understand and interact effectively with others. Jobs requiring high social intelligence, such as negotiation, persuasion, and caregiving, are generally less vulnerable to automation.

The technology that enables computers to identify and process spoken words. Speech recognition converts audio signals into text and is used in voice assistants, transcription services, and accessibility tools.

The process of collecting, analyzing, and interpreting data to identify patterns and relationships. Statistical analysis is fundamental to calculating automation risk scores.

A type of machine learning where algorithms learn from labeled training data to make predictions or classifications. The model is trained on input-output pairs and learns to map inputs to correct outputs.

The detailed examination of the tasks that make up a job. Task analysis helps identify which specific activities are most susceptible to automation.

A curated list of the 20 jobs with the highest AI automation risk scores, helping users understand which occupations are most vulnerable to technological disruption.

The dataset used to teach machine learning models how to make predictions or decisions. The quality and quantity of training data significantly impact the performance and accuracy of AI systems.

A machine learning technique where knowledge gained from solving one problem is applied to a different but related problem. This allows models to leverage pre-trained knowledge, reducing training time and data requirements.

A type of machine learning where algorithms find patterns in data without labeled examples. The system identifies hidden structures, clusters, or relationships in the data.

The process of learning additional skills to enhance one's current role or advance within the same field. Upskilling helps workers stay relevant as technology evolves.

The automation of business processes and workflows to reduce manual effort and improve efficiency. Workflow automation connects different systems and automates task sequences.

The strategic process of ensuring an organization has the right people with the right skills at the right time. Workforce planning must account for automation and AI impacts.