Start with one workflow that costs the most manual time. Prove value there before expanding.
Foundational terms
These are the words that show up first, usually before a vendor has explained anything specific about your project. Artificial intelligence (AI): software that performs tasks normally requiring human judgment, such as reading text, recognizing patterns, or making a recommendation. Machine learning: a type of AI where the system improves at a task by learning from data, rather than following rules written by a person. Model: the trained system that actually makes predictions or generates responses. Algorithm: the underlying method a model uses to process data and reach an output. Training data: the historical data used to teach a model how to perform its task. Large language model (LLM): a model trained on vast amounts of text, used to understand and generate natural language.
Automation: using software to carry out a task without a person doing it manually each time.
How AI systems work
This group covers the mechanics that come up once a conversation moves from what AI could do to how it would actually be built or connected. API: a defined way for one piece of software, including an AI service, to send and receive data from another. Integration: connecting an AI tool to your existing systems so data flows between them automatically. Fine-tuning: further training an existing model on your specific data so it performs better on your particular use case. Prompt: the instruction or question given to a language model to produce a response. Inference: the process of a trained model actually generating an output for a new input. Latency: the delay between a request and the system’s response, relevant when speed matters to your process.
Working on something similar?
Let's talk →Automation and agents
Working on something similar?
Let's talk →These terms tend to appear once the conversation shifts from a single tool to a broader process you want to run with less manual effort. Automation: using software to carry out a task without a person doing it manually each time. Workflow: the sequence of steps a process follows from start to finish, whether done manually or automated. AI agent: software that can take a goal, reason through steps, and act using connected systems, rather than following one fixed rule. Trigger: the specific event that starts an automated process. Human-in-the-loop: a design where a person reviews or approves an AI system’s output before it takes effect. No-code platform: a tool that lets you build automations by connecting steps visually, without writing programming code.
Risk and quality terms
These are the words worth understanding before anything goes live, since they describe how a system can quietly go wrong. Hallucination: when an AI model generates a confident but factually incorrect statement. Drift: when a system’s accuracy declines over time because real-world data has diverged from what it was built on. Grounding: designing a system so its answers are based on verified source data rather than general knowledge alone. Bias: systematic errors in a model’s output caused by patterns in its training data. Edge case: an unusual situation that falls outside the standard, expected pattern a system was designed for. False positive and false negative: an incorrect flag raised when nothing was actually wrong, or a real issue that was missed entirely.
Vendor, contract, and business terms
This is the group most directly tied to money and risk, and the one worth reading most carefully before any contract is signed. Proof of concept: a small-scale test built to show whether an idea works technically, before committing to a full build. Pilot: a limited real-world trial of a system on a slice of your actual operation. Scope: the specific, agreed set of work included in a development project. Data ownership: who legally controls and can use the data involved in a system, which should always be stated explicitly in a contract. Vendor lock-in: a situation where switching away from a provider is difficult or costly because of how a system was built. Service level agreement (SLA): a documented commitment about response times, uptime, or support standards. ROI (return on investment): the value a project delivers relative to what it cost to build and run. Scalability: how well a system continues to perform as volume or complexity grows.
Keep this glossary open the next time a vendor call introduces a term you have not heard before. Understanding what a word actually commits you to is often the difference between a contract you feel confident signing and one you sign hoping it works out.
