AI Workshop
LIVE INSTRUCTOR-LED
AI Fluency for Leaders
Master the 25 AI concepts every leader should understand so you can make better decisions, challenge proposals, manage risk and lead AI adoption with confidence.
Understand 25 essential AI concepts in one practical session — and know what each means for strategy, investment, risk, workforce and adoption.
3 hours Team Workshop Activities and Re-usable content Certificate of participation
Workshop Overview
Overview
Leaders do not need to become AI engineers, but they do need a working understanding of the concepts shaping AI strategy, investment and transformation.
AI Fluency for Leaders is a fast-paced, practical executive workshop built around 25 essential AI concepts. From machine learning, generative AI and foundation models to RAG, AI agents, hallucinations, governance and AI economics, each concept is explained in plain business language and connected to the decisions leaders are expected to make.
Participants use short examples, scenarios and decision exercises to understand what each concept means, why it matters, what questions to ask and where common misconceptions can lead to poor investment or risk decisions.
Key Outcomes
By the end of this workshop, you'll be able to:
Explain 25 essential AI concepts in clear, non-technical language.
Distinguish AI, machine learning, deep learning, generative AI, foundation models and large language models.
Understand how prompts, tokens, context windows and probabilistic outputs affect AI behaviour.
Recognise why hallucinations occur and why grounding and evaluation matter.
Explain embeddings, semantic search, RAG and fine-tuning — and when each is relevant.
Differentiate AI copilots, AI agents, tool use and orchestrated AI workflows.
Recognise when automation is appropriate and where human-in-the-loop oversight remains essential.
Ask sharper questions about data quality, model performance, security, privacy, IP, bias and governance.
Understand the economics of AI, including inference cost, scalability, ROI and total cost of ownership.
Use a shared AI vocabulary to make better strategic, investment and transformation decisions.
Workshop Agenda
Explore the workshop
1 Theme 1 — AI Foundations
1. Artificial Intelligence (AI) — the umbrella concept; 2. Machine Learning (ML) — systems that learn patterns from data; 3. Deep Learning & Neural Networks — the architecture behind many modern AI capabilities; 4. Generative AI — AI that creates new text, images, audio, code and other content; 5. Foundation Models — large pre-trained models that can be adapted to many tasks.
2 Theme 2 — Language Models & How Generative AI Behaves
6. Large Language Models (LLMs) — models trained to understand and generate language; 7. Multimodal AI — models that work across text, image, audio and video; 8. Tokens & Context Windows — how models process information and the limits of what they can consider at once; 9. Prompting & Prompt Engineering — how instructions, context and structure influence outputs; 10. Hallucinations & Probabilistic Outputs — why confident-sounding answers can still be wrong.
3 Theme 3 — Grounding, Knowledge & Model Quality
11. Embeddings — numerical representations that capture meaning; 12. Semantic Search — finding information by meaning rather than exact keywords; 13. Retrieval-Augmented Generation (RAG) — grounding AI responses in trusted enterprise information; 14. Fine-Tuning & Model Customisation — adapting models for specialised behaviours or domains; 15. Model Evaluation — testing accuracy, relevance, reliability, safety and business usefulness.
4 Theme 4 — Copilots, Agents & Intelligent Workflows
16. AI Copilots & Assistants — AI that supports a person in completing work; 17. AI Agents / Agentic AI — systems that can plan and take multi-step actions toward a goal; 18. Tool Use & Function Calling — enabling AI to interact with applications, APIs and enterprise systems; 19. AI Workflow Orchestration — coordinating models, tools, data and steps into repeatable processes; 20. Automation, Augmentation & Human-in-the-Loop — deciding what AI should do, what people should do and where oversight belongs.
5 Theme 5 — Enterprise Risk, Governance & Value
21. Data Quality, Grounding & Provenance — ensuring AI is based on relevant, trusted and traceable information; 22. AI Security, Prompt Injection & Data Leakage — understanding new attack paths and information risks; 23. Privacy, Confidentiality & Intellectual Property — protecting sensitive data and managing ownership concerns; 24. Bias, Explainability & Responsible AI Governance — establishing controls, accountability and appropriate oversight; 25. AI Economics — understanding inference cost, scalability, ROI and total cost of ownership.
Who It's For
Built for people doing the work
Executives and senior leaders
Business unit and functional leaders
Board and leadership-team members
AI, digital and transformation leaders
Product and innovation leaders
Operations and delivery leaders
Technology leaders who need a shared business language with non-technical peers
Leaders responsible for evaluating AI investments, vendors, use cases or organisational adoption
Hands-on Activities
Learn by doing
Practical exercises you'll complete during the workshop.
AI concept matching: term, meaning and leadership implication
Prompt, context-window and hallucination mini-exercise
RAG vs fine-tuning vs prompting decision scenario
Copilot vs agent vs workflow design challenge
Human-in-the-loop decision exercise
AI security, privacy and governance scenario discussion
AI investment and ROI challenge
Build-your-own leadership question set
Personal AI fluency action plan
You'll Leave With
Practical assets you can reuse
Top 25 AI Concepts for Leaders quick-reference guide
Plain-English AI terminology map
Prompting, RAG and fine-tuning decision guide
Copilot vs Agent vs Automation comparison guide
AI Evaluation and 'Ask Better Questions' checklist
Responsible AI, Security and Governance leadership checklist
AI Investment, ROI and TCO question set
Personal AI Fluency Action Plan
Certification
Certificate of completion
Certificate of participation is included with this workshop.
Instructor
Meet your instructor
Rajiv Goel
AI Transformation Leader and Microsoft Certified Trainer
Senior transformation and AI adoption experience translating complex AI concepts into practical business language, leadership decisions and enterprise transformation actions.
FAQs
Common questions
Is this a technical AI course?
No. The 25 concepts are explained from a leadership and business-decision perspective, without requiring coding or data-science knowledge.
Why focus on 25 concepts?
The workshop provides leaders with a practical core vocabulary covering how modern AI works, how it is applied, where it can fail and what is required to use it responsibly at scale.
Do I need previous AI knowledge?
No. The workshop starts with the fundamentals and builds toward generative AI, agents, governance and AI economics.
Does it cover generative AI and large language models?
Yes. Generative AI, foundation models, LLMs, multimodal AI, prompting, context windows and hallucinations are core concepts.
Does it cover RAG and fine-tuning?
Yes. Leaders learn what RAG, embeddings, semantic search and fine-tuning mean, why they matter and the types of problems each approach is designed to address.
Does it cover AI agents?
Yes. The workshop distinguishes copilots, agents, tool use and orchestrated workflows, including where human oversight is needed.
Will we discuss AI risk and governance?
Yes. Security, prompt injection, data leakage, privacy, IP, bias, explainability, responsible AI and governance are covered from a leadership perspective.
Does it cover the business case for AI?
Yes. Leaders explore AI economics, including scalability, inference cost, ROI and total cost of ownership.
Is the content vendor-specific?
No. The concepts are deliberately transferable across Microsoft, OpenAI, Google, AWS and other AI platforms.
Can this be tailored to our leadership team or industry?
Yes. Examples and scenarios can be adapted to your organisation, sector and AI maturity.
