Reference Guide
AI Glossary for Business Leaders
55+ AI and automation terms explained in plain language. No PhD required — just the definitions you need to make confident technology decisions.
Glossary Terms A–Z
AI Agent
A software system that can autonomously perform tasks, make decisions, and take actions on behalf of users. AI agents combine LLMs with tools, memory, and planning to handle multi-step workflows like customer support triage or data entry.
Related: AI Automation ServicesAI Copilot
An AI assistant embedded in workplace software that helps users complete tasks faster. Unlike autonomous agents, copilots work alongside humans — suggesting code, drafting emails, or summarizing documents.
Related: AI Copilots in ERP WorkflowsAPI (Application Programming Interface)
A standardized way for software systems to communicate with each other. APIs let your business tools send and receive data — for example, connecting your CRM to an AI chatbot.
Related: API Integration ServicesAutomation
Using technology to perform tasks with minimal human intervention. AI automation goes beyond simple rules-based automation by understanding context, making judgment calls, and handling exceptions.
Related: AI Automation ServicesBias (in AI)
Systematic errors in AI outputs caused by skewed training data or flawed model design. Bias can lead to unfair hiring recommendations, loan approvals, or customer service experiences.
Related: AI Governance in Regulated IndustriesBusiness Intelligence (BI)
Tools and processes that transform raw data into actionable insights. AI-enhanced BI goes further by automatically surfacing anomalies, forecasting trends, and generating natural-language summaries.
Related: ERP Data & Executive Insights with AIChatbot
A software application that simulates human conversation through text or voice. Modern AI chatbots use large language models to understand nuance, maintain context, and provide helpful responses.
Related: Chatbot Development ServicesClaude
An AI assistant developed by Anthropic, known for being helpful, harmless, and honest. Claude excels at analysis, writing, coding, and complex reasoning for business applications.
Related: ChatGPT vs Claude for BusinessCompliance (AI)
The practice of ensuring AI systems meet legal, regulatory, and industry standards. In Canada, this includes PIPEDA for privacy, sector-specific regulations, and emerging AI governance frameworks.
Related: PIPEDA-Compliant AI in CanadaCustom GPT
A tailored version of ChatGPT configured with specific instructions, knowledge, and capabilities for a particular business use case. Custom GPTs can serve as internal tools, customer-facing assistants, or workflow automators.
Related: How to Build a Custom GPTData Pipeline
An automated sequence of steps that moves data from source systems through transformation and into a destination. AI-powered pipelines can clean, categorize, and enrich data automatically.
Related: API Integration ServicesDemand Sensing
Using AI to analyze real-time signals (POS data, weather, social trends) to predict short-term demand more accurately than traditional forecasting methods.
Related: AI Demand Forecasting with Oracle SCMDocument Review (AI)
Using AI to analyze, classify, and extract information from documents. Law firms and compliance teams use AI document review to process contracts, legal briefs, and regulatory filings at scale.
Related: AI for Law Firms: Document ReviewEmbeddings
Numerical representations of text that capture semantic meaning. Embeddings let AI systems understand that "reduce headcount" and "cut staff" mean similar things, enabling smarter search and classification.
Related: AI Enterprise Data SecurityERP (Enterprise Resource Planning)
Integrated software suites (like SAP and Oracle) that manage core business processes including finance, HR, supply chain, and manufacturing. AI is transforming how teams interact with ERP systems.
Related: AI Copilots in ERP WorkflowsFine-Tuning
Training a pre-built AI model on your organization’s specific data to improve its performance for your use case. Fine-tuning helps models learn your terminology, processes, and quality standards.
Related: AI Infrastructure ServicesFoundation Model
A large AI model (like GPT-4 or Claude) trained on broad data that can be adapted for many tasks. Businesses build on foundation models rather than training models from scratch.
Related: AI Models Comparison 2025Generative AI
AI systems that create new content — text, images, code, or data — rather than just analyzing existing content. ChatGPT, Claude, and DALL-E are examples of generative AI tools.
Related: What Is ChatGPT? FAQs & Key FeaturesGovernance (AI)
The framework of policies, roles, and processes that control how AI is developed, deployed, and monitored in an organization. Good governance reduces risk and builds trust.
Related: AI Governance in Regulated IndustriesGPT (Generative Pre-trained Transformer)
The architecture behind ChatGPT and similar models. GPTs are trained to predict the next word in a sequence, which allows them to generate coherent, contextual text.
Related: What Is ChatGPT? FAQs & Key FeaturesHallucination
When an AI model generates information that sounds plausible but is factually incorrect or fabricated. Hallucinations are a key risk in enterprise AI and can be mitigated with RAG and human oversight.
Related: AI Governance in Regulated IndustriesHuman-in-the-Loop (HITL)
A design pattern where AI handles routine work but escalates edge cases to humans for review. HITL balances automation efficiency with accuracy and accountability.
Related: Training Your Workforce for AI CollaborationIndexNow
A protocol that lets website owners instantly notify search engines about content changes. It eliminates waiting for crawlers and helps new pages get indexed within hours.
Related: BlogInference
The process of running a trained AI model to generate predictions or outputs. Inference costs are a key factor in the economics of AI — faster inference means lower operational costs.
Related: AI Infrastructure ServicesLarge Language Model (LLM)
An AI model trained on massive text datasets that can understand and generate human language. GPT-4, Claude, Gemini, and Llama are all LLMs used in business applications.
Related: AI Models Comparison 2025Legacy Modernization
Updating outdated software systems to work with modern technology and AI capabilities. This often involves API layers, data migration, and phased rollouts to minimize disruption.
Related: Legacy Modernization ServicesMachine Learning (ML)
A subset of AI where systems learn patterns from data rather than following explicit rules. ML powers recommendation engines, fraud detection, predictive maintenance, and more.
Related: AI Automation ServicesMeeting Cost Calculator
A tool that reveals the true cost of meetings by multiplying attendee hourly rates by meeting duration. Helps organizations identify and eliminate unnecessary meetings.
Related: Meeting Cost Calculator ToolModel Context Window
The maximum amount of text an AI model can process in a single interaction. Larger context windows (100K+ tokens) enable processing of long documents, codebases, or conversation histories.
Related: AI Models Comparison 2025Natural Language Processing (NLP)
AI’s ability to understand, interpret, and generate human language. NLP powers chatbots, sentiment analysis, document classification, and voice assistants.
Related: Chatbot Development ServicesNeural Network
A computing system inspired by the human brain that learns to recognize patterns. Deep neural networks with many layers (deep learning) power modern AI breakthroughs.
Related: AI Models Comparison 2025Oracle Fusion Cloud
Oracle’s cloud-based ERP suite that increasingly integrates AI across finance, supply chain, HR, and customer experience modules.
Related: Oracle Fusion Cloud AI Use CasesPIPEDA
Canada’s Personal Information Protection and Electronic Documents Act, the federal privacy law governing how businesses collect, use, and disclose personal information. All AI systems handling Canadian data must comply.
Related: PIPEDA-Compliant AI in CanadaPredictive Maintenance
Using AI to analyze equipment sensor data and predict failures before they happen. This reduces unplanned downtime and extends asset lifespan in manufacturing and facilities.
Related: Predictive Maintenance with SAP PMPrompt Engineering
The skill of crafting effective instructions for AI models to get better outputs. Good prompts are specific, provide context, define the desired format, and include examples.
Related: Essential Business PromptsRAG (Retrieval-Augmented Generation)
A technique that combines AI text generation with real-time information retrieval from your documents or databases. RAG reduces hallucinations and keeps AI responses grounded in your actual data.
Related: AI Infrastructure ServicesROI (Return on Investment)
A measure of the profitability of an investment. For AI projects, ROI typically comes from reduced labor costs, faster processing times, fewer errors, and improved customer satisfaction.
Related: ROI CalculatorSaaS (Software as a Service)
Cloud-hosted software accessed via subscription. Many businesses are rethinking SaaS spending as custom AI tools can replace expensive subscriptions at a fraction of the cost.
Related: SaaS Replacement ServicesSAP S/4HANA
SAP’s flagship ERP system built on an in-memory database. It supports AI-driven processes across finance, manufacturing, supply chain, and procurement.
Related: Generative AI in SAP S/4HANASelf-Hosted AI
Running AI models on your own infrastructure (on-premise servers or private cloud) rather than using third-party APIs. This gives full control over data privacy, latency, and costs.
Related: AI Infrastructure ServicesSentiment Analysis
Using AI to determine the emotional tone of text — positive, negative, or neutral. Businesses use sentiment analysis on customer reviews, support tickets, and social media mentions.
Related: Chatbot Development ServicesSHAP (SHapley Additive exPlanations)
A method for explaining individual AI predictions by showing which factors contributed most to the output. SHAP increases transparency and helps build trust in AI decisions.
Related: Audit-Ready AI in ERPToken
The basic unit of text that AI models process. A token is roughly 3/4 of a word. Understanding tokens helps you estimate API costs and stay within model context limits.
Related: ChatGPT Pricing in CanadaTransfer Learning
Applying knowledge from a pre-trained model to a new, related task. Transfer learning makes AI practical for business — you don’t need millions of examples to build effective models.
Related: AI Infrastructure ServicesVector Database
A specialized database that stores and searches embeddings (numerical representations of text, images, or other data). Vector databases power semantic search, recommendation systems, and RAG pipelines.
Related: AI Infrastructure ServicesVendor Consolidation
Reducing the number of software vendors by replacing overlapping tools with integrated solutions. AI accelerates consolidation by connecting remaining tools and automating data flow.
Related: Vendor Consolidation ServicesWorkflow Automation
Using technology to automate multi-step business processes. AI workflow automation handles complex tasks like invoice processing, employee onboarding, or customer follow-ups with minimal human input.
Related: AI Automation ServicesZero-Shot Learning
An AI model’s ability to perform tasks it wasn’t specifically trained for, using only its general knowledge and a text prompt. This is what makes tools like ChatGPT immediately useful without custom training.
Related: How to Use ChatGPTKnow the Terms. Now See Them in Action.
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