The Practical AI Glossary for Business Leaders

In the world of AI, a shared, practical vocabulary is crucial. It helps leadership teams have meaningful conversations, create effective strategies, and avoid what we call “deployment theatre,” AI projects that look impressive but deliver no real business value. This glossary is designed for mid-market leaders, providing plain-English definitions for the terms that matter most.

A

Agentic AI

• Definition: A type of AI system that can proactively and autonomously take actions to achieve a goal. Unlike generative AI, which creates content, agentic AI performs tasks.
• Why it Matters: This is the next frontier of business automation. It represents the shift from AI as a creative tool to AI as an autonomous team member.
• Example in Practice: An agentic AI system could be tasked with “find the top three catering vendors for our next event.” It would then search, compare, contact vendors, and present you with the best options.

To begin strategising for this shift, see our comprehensive guide, What is Agentic AI?

AI Governance

• Definition: The framework of rules, policies, and processes that guide the responsible and effective use of AI within an organisation.
• Why it Matters: Without governance, you expose your business to significant risks, including data breaches, compliance failures, and reputational damage.
• Example in Practice: Your AI governance framework would include a policy on which customer data can be used with external AI tools and an approval process for new AI vendors.

For a step-by-step implementation plan and template, checkout our AI governance framework.

AI Readiness

• Definition: How prepared your business is to actually use AI, covering your data, tech stack, skills, and internal culture.
• Why it Matters: Jumping into AI without being ready is the primary cause of stalled pilots and wasted investment. A clear-eyed assessment is the essential first step.
• Example in Practice: An AI readiness assessment might reveal that your sales data is too siloed to effectively train a sales forecasting model, highlighting a critical data strategy issue to fix first.

Conduct your organisation’s official evaluation today with the AI readiness assessment before launching your next pilot.

C

Change Management

• Definition: The structured approach to transitioning individuals, teams, and organisations from a current state to a desired future state. In AI, it’s about managing the human element of adoption.
• Why it Matters: Technology is the easy part. AI adoption fails when people resist the change. Effective change management is non-negotiable for success.
• Example in Practice: A change management plan for a new AI-powered CRM tool would include stakeholder communication, user training, and a feedback loop for the sales team.

 Effectively manage the human side of adoption and resistance, from mid-level managers to your front-line, with the dedicated frozen middle AI change management framework and the actionable guide, Closing the AI Skills Gap playbook.

D

Data Strategy

• Definition: A company’s long-term plan for collecting, storing, managing, sharing, and using its data to achieve business goals.
• Why it Matters: AI is only as good as the data feeding it. Your data strategy is the foundation of your entire AI strategy.
• Example in Practice: A data strategy might involve consolidating customer data from three different systems into a single source of truth before launching a CX automation project.

The first step to aligning your data and AI initiatives is creating a unified plan; our AI roadmap playbook and the proven AI ROI framework provide the necessary structure.

F

Fine-Tuning

• Definition: The process of taking a pre-trained large language model (LLM) and further training it on a smaller, specific dataset to improve its performance on a particular task.
• Why it Matters: Fine-tuning allows you to adapt powerful general models to your specific business context, such as your brand’s tone of voice.
• Example in Practice: You could fine-tune a general chatbot model on your company’s internal knowledge base to create an expert internal support assistant.

Frozen Middle

• Definition: A term for the layer of middle management that often resists or slows down strategic initiatives like AI adoption. This resistance is often driven by fear of obsolescence or a lack of clear direction.
• Why it Matters: The frozen middle is the single biggest human barrier to scaling AI. Engaging and empowering this group is critical.
• Example in Practice: A COO notices that department heads are consistently finding reasons to delay a new AI-powered reporting tool, a classic sign of the frozen middle.

G

Generative AI

• Definition: A category of AI that can create new content, such as text, images, code, and audio, based on the data it was trained on.
• Why it Matters: Generative AI has democratised content creation and offers significant productivity gains, but it does not act autonomously.
• Example in Practice: Using a tool like ChatGPT to draft a marketing email or create an image for a blog post.

H

Hallucination

• Definition: An instance where an AI model generates output that is nonsensical, factually incorrect, or completely unrelated to the input provided. It is essentially the AI confidently making things up.
• Why it Matters: Hallucinations are a major risk when using AI in business contexts, potentially leading to the spread of misinformation or poor decision-making.
• Example in Practice: Asking an AI to summarise a sales report and it inventing a non-existent customer with a multi-million-pound deal.

L

Large Language Model (LLM)

• Definition: A type of AI model trained on vast amounts of text data, enabling it to understand and generate human-like language. GPT-4, Claude 3, and Llama 3 are all examples of LLMs.
• Why it Matters: LLMs are the foundational technology behind most modern generative AI applications, from chatbots to content creation tools.
• Example in Practice: The engine running behind the scenes when you use a public tool like ChatGPT or a built-in feature like Microsoft 365 Copilot.

P

Pilot Fatigue

• Definition: Organisational exhaustion caused by launching too many small AI trials that never scale or deliver measurable business value.
• Why it Matters: Pilot fatigue erodes confidence in AI, wastes resources, and creates cynicism among employees and leadership.
• Example in Practice: The innovation team has run six different AI pilots in the last year, but the finance team has not seen a single one impact the bottom line.

Prompt Engineering

• Definition: The art and science of designing effective inputs (prompts) to guide a generative AI model toward producing the desired output.
• Why it Matters: The quality of your output is directly tied to the quality of your input. Skilled prompt engineering is key to getting value from generative AI.
• Example in Practice: Instead of asking an AI to “write a blog post about AI,” a better prompt would be: “Act as a marketing expert for a mid-market company. Write a 500-word blog post on the benefits of AI for sales teams, using a practical and plain-speaking tone.”

R

Retrieval-Augmented Generation (RAG)

• Definition: A technique that allows a large language model to access and incorporate information from private company files before generating a response.
• Why it Matters: RAG helps to reduce hallucinations and allows AI models to provide answers based on your company’s private, proprietary data.
• Example in Practice: A customer service chatbot using RAG can retrieve a customer’s specific order history from your CRM before generating an answer to their query.

W

Workflow-First

• Definition: An approach to AI adoption that starts by identifying a specific business workflow to be improved, rather than starting with a specific AI tool and looking for a problem to solve.
• Why it Matters: A workflow-first approach ensures that AI initiatives are grounded in real business needs and are more likely to deliver measurable ROI.
• Example in Practice: Instead of asking “What can we do with this new AI tool?”, a workflow-first leader asks, “What is our most inefficient internal process, and could AI help streamline it?”

Frequently asked questions