In this blog post I'm going to define 60 AI marketing terms in plain English for non-technical business owners, the version with the definitions you need, not the version that uses three more technical terms to define each one.
Most "AI glossaries" you'll find online were written by engineers for engineers, or by content marketers who copy-pasted Wikipedia. Neither is useful when you're a founder or marketing leader trying to decide whether to spend on something called an "agent framework" or "RAG pipeline."
This one is different. Every term below is defined assuming you don't have a technical background, with an example of how it applies to marketing work specifically, and (where relevant) whether it's worth caring about in 2026.
I've been a marketing consultant for twenty-one years. I went all in on AI in 2024. I'm not a coder. If I can understand these terms well enough to use them in client work, you can too.
By the end of this blog you'll understand the AI vocabulary you need for buying conversations, vendor evaluations, and reading consulting proposals without nodding through terms you don't know.
Quick navigation
Terms are grouped:
- Foundations (1-12): the basics, what AI is and how it works
- AI types and capabilities (13-25): the categories of AI tools you'll encounter
- Workflow terms (26-38): how AI gets built into your business
- Marketing-specific terms (39-50): AI vocabulary used in marketing contexts
- Risk and policy (51-60): governance, ethics, compliance
Foundations
1. Artificial Intelligence (AI)
Software that performs tasks normally requiring human judgement, recognising patterns, making decisions, generating content. In marketing context, AI now usually means "large language model" (see #4).
2. Machine Learning (ML)
A subset of AI where software learns patterns from data instead of being explicitly programmed. The technology underneath most modern AI.
Worth caring about in 2026? Not directly, you don't buy "ML." You buy specific AI tools that use ML under the hood.
3. Algorithm
A specific set of rules a computer follows. "Google's algorithm" = the set of rules Google uses to rank pages. Most AI is built on multiple interacting algorithms.
4. Large Language Model (LLM)
The kind of AI that powers ChatGPT, Claude, Gemini, etc. It's trained on huge amounts of text and can generate human-sounding language in response to prompts. The defining AI technology of 2024-2026.
5. Generative AI (GenAI)
AI that creates new content, text, images, audio, video, code. As opposed to AI that only classifies or predicts. LLMs are a type of generative AI.
6. Prompt
The instruction you give an AI. "Write me a 200-word LinkedIn post about X" is a prompt. The quality of your prompts determines the quality of your output.
7. Prompt engineering
The skill of writing prompts that consistently produce useful AI outputs. Not a sustainable job category by itself in 2026, but a critical skill within marketing roles.
8. Tokens
The units AI counts when processing text. Roughly: 1 token = 0.75 words. "How are you" = 4 tokens. AI vendors charge by tokens consumed. Affects your AI tool costs.
9. Context window
How much text an AI can consider at once. ChatGPT-5 might have a 200k-token context window, about 150,000 words. Bigger windows = AI can work with more documents, longer transcripts, etc. Currently a major axis of competition between AI vendors.
10. Hallucination
When AI confidently makes up false information. Common failure mode. The reason every AI output needs human review before customer-facing use.
11. Training data
The text/images/etc. an AI was trained on. Affects what the AI knows and what biases it has. Most major LLMs are trained on huge swathes of public internet content plus licensed data.
12. Cutoff date
The date the AI's training data stops. ChatGPT might "know" things up to January 2025 but not after. Affects accuracy for current-events queries.
AI types and capabilities
13. Chat AI
AI you interact with via conversation. ChatGPT, Claude, Gemini are chat AIs. The default starting point for marketers.
14. AI assistant
Marketing term for a chat AI configured for a specific purpose. Often just a prompt template with a friendly name and UI.
15. AI agent
AI that takes actions on its own, sends emails, updates records, makes API calls. More autonomous than chat AI. Currently overhyped in 2026 marketing; most "AI agents" are just workflows with LLM steps.
16. Multi-agent system
Multiple AI agents working together. Currently mostly research, with limited commercial deployment. Sceptical it'll mature into widespread marketing use this year.
17. RAG (Retrieval-Augmented Generation)
A technique where AI looks up relevant information from your documents before answering. Lets AI use your internal knowledge without retraining. Essential for internal knowledge tools.
18. Fine-tuning
Re-training a general-purpose AI on your specific data. Expensive and rarely needed. Most marketing use cases don't require fine-tuning, good prompting + RAG covers it.
19. Embeddings
Numerical representations of text that let AI compare meaning. Powers semantic search, recommendation engines, and similarity matching. Mostly invisible to end users.
20. Vector database
A database that stores embeddings. Required for RAG and semantic search. Examples: Pinecone, Weaviate, Qdrant. You probably don't pick one yourself, your AI tools choose.
21. Foundation model
The big general-purpose AI underneath everything. GPT-5, Claude 4, Gemini 2 are foundation models. Vendors build specialised tools on top of them.
22. Model API
Programmatic access to an AI model. Required for building AI workflows. OpenAI API, Anthropic API, Google Gemini API are the major ones.
23. Inference
The act of running an AI to produce output. "Inference costs" = how much you pay for AI usage. Distinct from training (a one-time cost the vendor bears).
24. Multimodal AI
AI that handles multiple input types, text, images, audio, video. Modern AI is increasingly multimodal. Important for marketing: lets AI analyse screenshots, audio recordings, video frames.
25. Reasoning model
A newer category of AI that "thinks" through problems step-by-step. OpenAI's o-series, Claude 3.7+, and DeepSeek-R1. Better for complex problems, slower and more expensive than standard models.
Workflow terms
26. AI workflow
A multi-step process where AI does part of the work and humans do another part. The unit of actual AI implementation in business. Different from "use AI" (which is too vague to act on).
27. AI pipeline
Sequence of automated steps including AI. More technical term for an AI workflow. Same idea.
28. Integration platform
Tools that connect different software via APIs. n8n, Make.com, Zapier. Where most non-technical founders build their AI workflows.
29. Webhook
A small URL one system calls when an event happens. Triggers AI workflows. Mostly invisible to marketers.
30. API (Application Programming Interface)
The way one piece of software talks to another. AI tools have APIs. So do your CRM, email platform, etc. Connecting via APIs is how you integrate them.
31. Trigger
What starts an AI workflow. "New contact created" or "Calendar event added" or "Daily at 9am."
32. System prompt
The standing instructions that shape how an AI behaves in a workflow. "You are a sales operations specialist. You categorise inbound leads as...", that's a system prompt.
33. Few-shot prompting
Providing 2-5 examples of desired output in your prompt. Dramatically improves output quality for specific formats.
34. Chain of thought
A prompting technique where you ask the AI to show its reasoning. Improves accuracy for complex problems.
35. Function calling / Tool use
When AI invokes external functions (like "look up the prospect in CRM" or "send an email"). The mechanism that lets AI take actions, not just generate text.
36. Structured output
When AI returns data in a specific format (JSON, table, etc.) instead of free text. Critical for workflows that need to use the output programmatically.
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37. Guardrails
Rules that constrain AI behaviour. "Never recommend competitors." "Always include a disclaimer." Implemented via system prompts and output filters.
38. Human-in-the-loop (HITL)
A workflow design where humans review AI output before it takes effect. The right pattern for almost all marketing AI. The wrong pattern is "AI does everything without review" (see Hallucination, #10).
Marketing-specific terms
39. AI content generation
Using AI to draft marketing copy. Effective for first drafts and operational tail (SEO meta, image alt text, social blurbs). Penalised by Google when used unsupervised for primary content.
40. AI personalisation
Using AI to vary marketing content per recipient. Effective when based on real data. Damaging when "personalisation" is just inserted-token templating that prospects can detect.
41. AI lead enrichment
Using AI to fill in missing data about a lead, company size, industry, role, etc. From public sources or paid data providers. useful for sales prioritisation.
42. AI lead scoring
Using AI to assign a prioritisation score to leads. Combines firmographic + engagement data. useful when transparent (i.e., the score comes with a reasoning sentence).
43. AI segmentation
Using AI to group customers/prospects by behaviour or characteristics. Either replaces or refines manual segmentation. Useful for engagement campaigns.
44. Predictive lead scoring
A subset of AI lead scoring that predicts conversion likelihood. Sounds powerful, often overhyped. Real predictive accuracy depends on your conversion volume, under 100 conversions/month, the predictions are noise.
45. AI proposal drafting
Using AI to draft client proposals from discovery notes. High-ROI workflow for consultancies and agencies.
46. AI content brief
A document AI generates to inform content writing, target keyword, SERP analysis, recommended outline. Useful as input to human writing.
47. AI SDR / AI BDR
"AI sales development representative", software that performs outreach autonomously. Mostly damaging in 2026. Buy with extreme caution, see my AI sales workflows guide for why.
48. AI customer success agent
Software that automates customer success workflows. Generally damaging for relationship-based businesses. Useful for self-serve high-volume products.
49. AI ad creative
Using AI to generate ad images, video, copy. Works for high-volume testing. Quality below human creative for brand-defining work.
50. AI SEO
Using AI to help with SEO tasks, keyword research, content briefs, schema generation, internal linking. Highly effective when used as a multiplier on human judgement, dangerous when used to mass-produce content (see Google's helpful content update).
Risk and policy
51. AI ethics
The broad area of considering AI's social impact. Includes bias, fairness, transparency, accountability. Worth caring about even for small businesses, your AI outputs reflect on your brand.
52. AI bias
When AI systematically produces unfair or skewed outputs. Often inherited from training data. Important to watch for in any AI workflow touching hiring, customer evaluation, or pricing.
53. AI governance
The framework of policies, processes, and accountability for AI use in a business. Relevant once you have 3+ AI workflows running.
54. AI policy
Your written rules for AI use. Should cover: what AI can be used for, what it can't be used for, what data can be sent to AI, how outputs are reviewed, who owns outputs.
55. Data privacy
Rules and practices for protecting personal data. GDPR (UK/EU), CCPA (California), and similar laws. Critical when AI processes customer or prospect data.
56. PII (Personally Identifiable Information)
Data that identifies a real person, names, emails, phone numbers, addresses. Subject to data privacy law. AI workflows often touch PII; needs careful handling.
57. Data residency
Where data is physically stored. Some jurisdictions require data to stay in-country. Affects which AI vendors you can use for sensitive data.
58. Model deprecation
When an AI vendor retires a specific model version. Affects existing workflows. Plan for it.
59. AI Act (EU)
European regulation on AI use. Phased rollout 2024-2026. Affects how AI can be used in regulated contexts (hiring, credit, etc.). Marketing AI mostly falls outside the strict provisions but governance basics still apply.
60. AI transparency requirements
Various rules (EU AI Act, various US state laws) requiring disclosure when AI is used. Trend is toward more disclosure. Erring on the side of transparency is the safe play.
How to use this glossary
Three rules:
Rule 1: When a vendor uses a term you don't recognise, ask them to define it without using other technical terms. If they can't, they don't understand it either.
Rule 2: Don't be afraid to ask "so what?" Many AI terms describe technology that doesn't affect business outcomes. The "so what?" question filters the substance from the noise.
Rule 3: Update your vocabulary annually. AI terminology shifts fast. Half the terms above will be considered dated by 2027. The fundamentals (LLM, hallucination, RAG, agent, workflow) will stick.
Frequently asked questions
Why aren't terms like "GPT-5" or "Claude 4" defined? These are product names that change every few months. The glossary covers durable concepts, not specific products.
Do I need to understand all 60 terms? No. Foundations (1-12) and Workflow (26-38) are the must-knows. Others are useful when they come up.
Where can I find more comprehensive glossaries? Microsoft, Google, and Anthropic all publish glossaries. They're more thorough and more technical than this one. Use this for working understanding, theirs for deeper study.
Is there a quiz format of this? Not yet. May add interactive versions later.
Can I copy this for my own team? Yes, with attribution. Link back to this page.
Want me to teach this to your team?
I'm Lilach Bullock. I've been a marketing consultant for twenty-one years. I went all in on AI in 2024. I work with founders and marketing leaders who want AI to move their numbers, not just their tool stack.
Related: running a guest posting campaign
More on AI for your business: AI consultants for UK small businesses, how to get your business cited by ChatGPT and Perplexity, what a fractional AI officer actually does, and when you are ready, work with Lilach on AI implementation.
I go much deeper on this in the AI marketing guide.
