The AI terms every executive is expected to already know.

Artificial intelligence is software that performs tasks normally requiring human judgment, such as reading documents, answering questions, or predicting outcomes from data.

Why it mattersAI is a category, not a product. The useful question for a business is never whether to buy AI, but which specific task is slow or costly enough to be worth automating.

Machine learning is a way of building software that learns patterns from examples instead of being programmed with explicit rules.

Why it mattersML fits problems where the rules are too messy to write by hand, like fraud detection or demand forecasting. It needs clean historical data to work, which is usually the real constraint.

A large language model is an AI system trained on vast amounts of text that can read, write, summarize, and answer questions in natural language.

Why it mattersLLMs power most of the recent AI wave, from chatbots to document processing. They are rented by usage, so cost scales with how much text you send and receive.

Generative AI is AI that produces new content, such as text, images, code, or audio, rather than only classifying or scoring existing data.

Why it mattersIt is the difference between software that answers a fixed set of questions and software that drafts a proposal, a reply, or a design. The output still needs review before it reaches a customer.

A neural network is the underlying structure most modern AI models use to learn patterns, loosely inspired by how the brain connects signals.

Why it mattersYou rarely need to think about neural networks directly, but they explain why AI models are so capable and also why they can be hard to predict or explain.

Training data is the collection of examples an AI model learns from, and its quality sets the ceiling on how well the model can perform.

Why it mattersMost AI project delays trace back to data that is missing, messy, or locked in systems that do not talk to each other. Budget for data work before model work.

Inference is the act of running a trained AI model to get an answer, as opposed to training, which is the earlier work of building the model.

Why it mattersInference is what you pay for every time a user interacts with your AI feature. A feature that is cheap to prototype can still be expensive to run at scale.

Cost signalA recurring cost per use, not a one-time build cost.

A foundation model is a large, general-purpose AI model, such as GPT or Claude, trained once by a major lab and then adapted to many specific uses.

Why it mattersBuilding on a foundation model means you rent proven capability instead of training from scratch, which is why serious AI features can now ship in weeks rather than years.

Fine-tuning is further training a foundation model on your own examples so it adopts a specific tone, format, or task.

Why it mattersPowerful, but usually the wrong first move. For most business problems, retrieval and prompting are cheaper and far easier to update than a fine-tuned model.

Cost signalHigher setup and upkeep cost than prompting or RAG.

Retrieval-augmented generation lets an AI model answer using your own documents by fetching the relevant passages at the moment of the question.

Why it mattersRAG is how you get an AI assistant that knows your policies, contracts, or product docs without retraining a model. It is the default approach for most business knowledge tasks.

Prompt engineering is the practice of writing clear instructions and examples that reliably get the output you want from an AI model.

Why it mattersIt is the cheapest lever for improving AI quality and often closes most of the gap before any expensive model work is needed.

Embeddings are numerical representations of text, images, or other data that let software measure how similar two things are in meaning.

Why it mattersEmbeddings are the engine behind semantic search and RAG. They are why a search for cancel my plan can find a document titled subscription termination.

A vector database stores embeddings and finds the closest matches quickly, so an AI system can retrieve the most relevant information in real time.

Why it mattersA vector database is the storage layer that makes semantic search and RAG fast at high volume. For small datasets you may not need a dedicated one; at enterprise volume you will.

The context window is the amount of text an AI model can consider at once, covering both your input and its response.

Why it mattersIt sets a hard limit on how much a model can read in a single pass. Larger windows cost more per use, which is why retrieval is often smarter than pasting everything in.

Tokens are the small chunks of text that AI models read and generate, roughly three-quarters of a word each, and the unit most AI usage is billed in.

Why it mattersYour AI running cost is essentially a token bill. Longer prompts and longer answers cost more, so cost control is a design decision, not an afterthought.

Cost signalThe primary unit of ongoing AI cost.

Temperature is a setting that controls how varied or predictable an AI model's output is, from strict and repeatable to loose and creative.

Why it mattersLow temperature suits tasks that need consistency, like data extraction. Higher temperature suits brainstorming. The wrong setting is a common cause of unreliable output.

A system prompt is the standing instruction that sets an AI assistant's role, rules, and boundaries for every conversation.

Why it mattersIt is where you encode brand voice, guardrails, and what the assistant must never do. A weak system prompt is a common source of off-brand or unsafe answers.

An AI agent is software that plans and takes actions toward a goal, calling tools and other systems, rather than only answering a single question.

Why it mattersAgents move AI from advice to action, like processing a refund end to end. That power raises the stakes, so they need clear limits and human oversight on consequential steps.

Agentic AI describes systems that operate with a degree of autonomy, deciding the next step themselves instead of following a fixed script.

Why it mattersIt is the current frontier and the current hype magnet. The practical version is narrow and well-supervised, not a fully autonomous employee.

A multi-agent system splits a complex job across several specialized AI agents that coordinate, such as one researching and another writing.

Why it mattersIt can handle workflows too broad for a single agent, but every added agent adds cost and points of failure. It is worth it only when the task genuinely has distinct roles.

Orchestration is the coordination layer that decides which model, tool, or step runs when, and passes information between them.

Why it mattersThe gap between a demo and a dependable system lives here. Most of the engineering effort in a real AI product goes into orchestration, not the model itself.

Tool use, also called function calling, is an AI model's ability to trigger real actions, like querying a database or sending an email, instead of only producing text.

Why it mattersIt is what connects an AI model to your actual systems. Without it, an assistant can describe an action; with it, the assistant can perform one.

The Model Context Protocol is an open standard for connecting AI models to external tools and data sources through a consistent interface.

Why it mattersMCP reduces the custom plumbing needed to give AI access to your systems, which lowers integration cost and avoids lock-in to one vendor's connectors.

Workflow automation is software that carries a multi-step business process from start to finish with little or no manual handling.

Why it mattersAI lets automation reach steps that used to need a person, like reading a varied invoice. The savings come from the whole process, not any single clever step.

Robotic process automation uses software bots to mimic the clicks and keystrokes a person would perform in existing applications.

Why it mattersRPA is reliable for rigid, repetitive tasks but brittle when screens or rules change. Pairing it with AI handles the judgment steps it cannot.

Human-in-the-loop is a design where a person reviews or approves an AI system's output before it takes effect on high-stakes steps.

Why it mattersIt is the practical answer to AI mistakes: let the model do the volume, and keep a human on the decisions that carry real cost or risk.

A hallucination is when an AI model states something false with full confidence, inventing a fact, source, or number that looks plausible.

Why it mattersHallucination is the central trust problem with AI. Grounding answers in your own data and citing sources is how serious systems keep it rare and catchable.

Guardrails are the rules and checks that keep an AI system inside safe, on-brand, and compliant behavior.

Why it mattersThey are what stop an assistant from giving legal advice, leaking data, or going off-script. For any AI that faces customers, guardrails are a requirement, not a nice-to-have.

Evals are structured tests that measure how well an AI system performs on your specific task, using real examples and clear pass or fail criteria.

Why it mattersWithout evals, AI quality is a matter of opinion and every change is a gamble. They are how you know an update improved things rather than quietly broke them.

Model drift is the gradual decline in an AI system's accuracy as the real world moves away from the data it learned from.

Why it mattersAn AI feature is not done at launch. Without monitoring, performance decays silently, which is why running AI is an ongoing operating cost, not a project that ends.

Bias in AI is systematic unfairness in a model's outputs, usually inherited from patterns in its training data.

Why it mattersBeyond the ethics, biased AI is a legal and reputational liability in hiring, lending, and healthcare. It has to be tested for, because it rarely announces itself.

Explainability is the ability to understand and communicate why an AI system produced a particular result.

Why it mattersRegulators, auditors, and customers increasingly expect a reason, not just an answer. Low explainability limits where you can safely deploy AI, especially in regulated sectors.

Red teaming is deliberately attacking your own AI system to find ways it can be tricked, misused, or made to fail before real users or attackers do.

Why it mattersIt is standard practice for any AI that handles sensitive data or decisions. Finding the failure yourself is far cheaper than finding it in the press.

Grounding is tying an AI model's answers to verified sources, such as your own documents, so responses can be traced and trusted.

Why it mattersGrounding is the main defense against hallucination. A grounded answer can show its receipts, which is what makes AI usable for compliance-sensitive work.

An API is a defined way for two software systems to talk to each other, and it is how most AI capability is delivered into your own products.

Why it mattersUsing AI through an API means you rent capability on demand instead of hosting models yourself. Your cost and availability then depend on a provider you should choose deliberately.

Closed models, like GPT and Claude, are rented from a provider through an API, while open models can be downloaded and run on infrastructure you control.

Why it mattersThe choice trades convenience against control. Closed models are faster to start; open models can be cheaper at scale and keep data in-house, at the cost of running them yourself.

Inference cost is the ongoing expense of running an AI model each time it is used, driven mainly by how much text goes in and out.

Why it mattersInference cost is the line item that surprises teams after launch. A feature has to be priced and designed around its running cost, or a popular one can quietly become a loss.

Cost signalAn ongoing operating cost that scales with usage.

Latency is the delay between a user's request and the AI system's response.

Why it mattersIt shapes whether AI feels helpful or frustrating. Some quality gains come from slower, larger models, so latency is a real trade-off against accuracy and cost.

On-premise means running AI on infrastructure you own and control, while cloud means using shared infrastructure rented from a provider.

Why it mattersMost teams should start in the cloud for speed. On-premise becomes worth its higher cost mainly when data residency, compliance, or scale demand it.

A proof of concept tests whether an idea can work, a pilot tests it with a small group of real users, and production is the fully supported system everyone relies on.

Why it mattersMost of the cost and effort sits between a working demo and production. Confusing a successful PoC with a finished product is the most common AI budgeting mistake.

Cost signalProduction typically costs several times more than the PoC.

Build versus buy is the decision between developing custom software and adopting an existing off-the-shelf product.

Why it mattersBuy when the problem is common and the tool fits; build when the problem is your competitive edge or nothing off the shelf matches your workflow. The honest answer is often a mix.

AI readiness is how prepared an organization is to adopt AI, measured across its data, systems, skills, and processes.

Why it mattersThe blocker to AI value is usually not the model but the data and workflows around it. An honest readiness check prevents spending on AI the business cannot yet absorb.

Personally identifiable information is any data that can identify a specific person, and data privacy is the practice of protecting it throughout an AI system.

Why it mattersFeeding customer data into AI tools without controls is a common and serious exposure. Where data goes, how long it is kept, and who can see it must be settled before launch.

GDPR is the European Union regulation governing how personal data is collected and used, and it applies to any organization handling the data of EU residents, wherever that organization is based.

Why it mattersCompliance is not optional and the fines are material. For AI, the hard parts are consent, the right to an explanation, and knowing exactly what data a model was exposed to.

Model governance is the set of policies and controls for approving, monitoring, and documenting the AI models an organization uses.

Why it mattersAs AI spreads across a company, governance is what keeps it auditable and accountable. Its absence is what turns a promising pilot into an unmanaged risk.

Shadow AI is employees using AI tools that the organization has not approved or does not know about.

Why it mattersShadow AI is where most corporate data leaks happen. A ban rarely works; it just drives the behavior underground. Sanctioned tools that are good enough to use openly work better.

An AI policy is a company's written rules for how AI may and may not be used, covering data, approved tools, and human oversight.

Why it mattersA clear policy is what lets a team move fast without stepping on legal or reputational landmines. It turns AI from a free-for-all into a managed capability.

An audit trail is a complete record of what an AI system did and why, so any decision can be reconstructed and reviewed later.

Why it mattersIn regulated work, an answer you cannot trace is an answer you cannot defend. Traceability is what makes AI decisions accountable to auditors, regulators, and customers.