AI Jargon for Execs

Large Language Models explained in business terms, what they are, what they can do, and crucially, what they cannot.

AI hallucinations are not a quirk to ignore: they are a business risk with legal, financial, and reputational implications.

What tokens are, why they matter for AI performance, and what "context window" means for how much an AI can process at once.

RAG explained without the jargon: how connecting AI to your own documents makes it dramatically more accurate and reliable.

The difference between a general AI model and one trained on your specific data, and how to decide which approach is right for you.

A business-focused distinction between rules-based AI, predictive AI, and generative AI, and why the difference matters for strategy.

Explaining the "base models" that underpin the AI tools your business is already using or considering.

Cutting through the terminology to explain what a model is, how it is built, and why different models behave differently.

Why the quality of your questions determines the quality of AI outputs, and what good prompting looks like in a business context.

AI agents that plan and act autonomously, explained in plain English with real business examples from 2025.

How grounding connects AI responses to verified, real-world data, and why ungrounded AI in enterprise contexts is a governance risk.

The specific meaning of "Copilot" in Microsoft's AI product line, what it does, what it does not do, and where the boundaries are.

Three terms that executives often conflate, with a clear, practical distinction that helps you ask better questions.

A non-technical explanation of how AI systems store and retrieve information, and why it is relevant to your data strategy.

Explaining AI that works across text, images, audio, and video, and the business use cases opening up as a result.

The hidden instructions that shape how AI behaves in your organisation, and why boards should know they exist.

A non-technical explanation of how bias enters AI systems, with real-world examples from HR, credit, and customer service.

How much text an AI model can process at once, and why this limits (and increasingly enables) complex business workflows.

The process of an AI generating a response, what happens computationally and why it has cost and speed implications at scale.

The foundational concept behind all modern AI, explained with a business analogy that actually makes sense.

Two fundamental AI training approaches explained, and where they remain relevant in choosing AI solutions.

The parameter that controls how creative (or predictable) an AI is, and why it matters for use cases like legal drafting versus brainstorming.

Moving beyond cybersecurity: the specific discipline focused on ensuring AI systems behave as intended, at scale.

The safety framework behind Claude explained in plain English, and why different AI providers take fundamentally different approaches to safety.

The critical difference between consumer ChatGPT and enterprise-grade Azure-hosted models, a distinction with significant data protection implications.

Explaining how AI integrates with your existing software. CRMs, ERPs, email, and what governance you need around those connections.

Two approaches to instructing AI, explained with practical business examples, and guidance on which to use when.

How organisations technically constrain AI behaviour to comply with policy, regulation, and brand standards.

Natural Language Processing explained, and why the term still matters even though LLMs have largely superseded earlier NLP approaches.

Cutting through the technical and academic definitions to give directors a working understanding of what AI governance means in practice.

A plain-English overview of Microsoft's low-code AI builder, what it can do for business functions without requiring a developer.

Demystifying OpenAI's model naming conventions and helping leaders understand which model to use for which business task.

Who made Claude, what makes it different from ChatGPT, and when a business should consider using it.

Google's AI platform explained, its relationship to Google Workspace, its strengths, and where it fits in an enterprise AI strategy.

Why confusing these two technologies leads to the wrong investment decisions and misaligned expectations.

Microsoft, Google, and Anthropic all publish responsible AI principles, here is what they mean in practice for business leaders.

How malicious actors can corrupt AI training data, and the governance controls that protect against it.

The concept of AI transparency, why it matters for regulated industries and how it affects your AI procurement decisions.

How virtual models of real-world systems work, and the AI capabilities that are making them commercially viable at scale.

The emerging phenomenon where AI trained on AI-generated data begins to degrade, and its implications for enterprise knowledge management.

The critical distinction between consumer AI tools and private deployments, and why it matters enormously for data governance.

How AI understands the meaning behind a question rather than just matching words, and why this changes enterprise knowledge management.

The 2017 breakthrough that made ChatGPT, Claude, and Copilot possible, explained without a single equation.

How attackers can embed malicious instructions inside documents that AI reads, a critical enterprise security risk.

How organisations generate artificial training data to overcome privacy constraints, and the governance questions it raises.

A plain-English explanation of the mathematical representation AI uses to understand relationships between concepts.

A non-technical explanation of one of the most common failure modes in AI model development.

Grounding the conversation about Artificial General Intelligence in business reality, separating today's tools from longer-horizon speculation.

The data platform that underpins Microsoft's AI capabilities, and why boards should understand it in the context of AI strategy.

The growing problem of companies claiming AI capabilities they do not have, and the due diligence questions that expose it.