AI Dictionary for Small Business Owners

How often an AI tool produces correct, useful output.

A simple plan for introducing AI over time — which use cases first, who is trained when, what policies apply, and how progress is measured.

Software that can work toward a goal using instructions, tools and approved access — for example, drafting a customer reply, then waiting for a human to review and send it.

A chat-style tool that helps a person work faster — drafting, summarising, answering questions and organising notes — while the person stays in charge of the result.

The set of rules, roles and checks a business uses to keep artificial-intelligence tools safe, accurate and appropriate for the work it does.

How consistently and safely a business already uses AI — from occasional individual use, to team workflows, to organisation-wide processes with clear rules and review.

A short written document that tells staff which AI tools are approved, what information must never be pasted into them, and when a human review is required before sending or acting on AI output.

How prepared a business is to adopt AI tools responsibly — covering staff training, data hygiene, approved use cases and human review.

The set of practical risks that come with using AI — inaccurate output, data leakage, reputational damage, over-reliance and unclear accountability.

A defined set of steps a computer follows to produce a result.

A structured way for two software systems to talk to each other.

Software that performs tasks normally associated with human thinking — reading, writing, summarising, comparing and pattern-spotting — using models trained on large amounts of information.

Using software to handle a repetitive task from start to finish, sometimes with AI in the middle.

When an AI system produces uneven or unfair results because of the data it learned from.

An AI software development assistant that understands an existing codebase and helps developers build, improve and maintain software.

Business information — pricing, contracts, employee data, customer records — that should stay inside the business.

The background information an AI assistant is given alongside a request — company details, examples, tone — so its answer fits your business rather than being generic.

Protecting personal and confidential information from unnecessary exposure.

Using AI to prepare summaries, comparisons and options that help a person decide — without letting the AI make the decision itself.

Whether an AI tool can show why it produced a particular output.

AI that produces new content — text, images, code or summaries — in response to a request.

When an AI system produces information that sounds confident but is inaccurate or invented.

A person checking AI output before it is used or sent.

A workflow where AI does the first draft or analysis and a person makes the final decision.

Connecting an AI tool to systems the business already uses — email, calendar, CRM, documents — so it can help with real work instead of standalone chat.

The type of AI model behind assistants like ChatGPT and Claude.

A Large Language Model — the type of AI behind tools like ChatGPT and Claude.

The branch of AI where software learns patterns from examples instead of being told every rule.

The underlying AI engine that produces answers.

Tools that let non-developers build workflows, forms or small applications by clicking and configuring rather than writing code.

Any information that identifies a person — name, email, phone, ID, health, employment details.

A limited real-world rollout — one team, one workflow, one month — used to learn what works and what doesn't before adopting AI more widely.

The practical measure of AI's business value: time saved on real tasks, at acceptable quality, after human review.

The instructions you give an AI tool.

The practical skill of writing clear instructions to an AI assistant so it produces useful output.

A type of attack where hidden instructions inside a document, email or webpage try to manipulate an AI assistant into doing something it shouldn't.

A small, time-boxed test to see whether an AI tool actually works for the business before committing money, training and process change to it.

Using AI in a way that is fair, accurate, transparent, respectful of privacy and clearly accountable — with a person answerable for outcomes, not the tool.

Staff using personal or unapproved AI tools for work tasks without oversight — often with sensitive business data pasted into public chatbots.

The information a model learned from.

Being open about where AI is used in the business — with staff, customers and, where required, regulators.

A specific task where AI is expected to help — for example, drafting quotations or summarising customer emails.