FA-0561AI for Leaders & BusinessAgentic & Generative AI

AI Essentials for Leadership

A one-day introduction to concepts, governance and adoption

A non-technical leadership workshop on AI capabilities, tool evaluation, prompting, oversight and a pilot-to-scale adoption roadmap.

Introduction

Why this course

This one-day course gives leaders a practical vocabulary for discussing AI and making initial adoption decisions. It explains machine learning, deep learning, language models and tool-using agents without requiring programming.

Participants consider capabilities and limitations, evaluation criteria, governance responsibilities and a high-level adoption roadmap. The course supports informed questions and planning; it does not implement production systems or guarantee business impact.

Learning outcomes

The course teaches participants to:

Explain AI, machine learning, deep learning and language models in accessible terms.
Recognise variability, hallucinations, bias and unexpected behaviour in modern AI systems.
Compare AI tool capabilities using cost, reliability, data, security and integration criteria.
Guide prompt use with clear instructions, examples and output evaluation.
Explain agent boundaries, tool access and human oversight.
Identify responsible-use and governance responsibilities.
Outline a measured pilot, evaluation and adoption roadmap.

Prerequisites

No technical or programming background required
Basic comfort with strategic, high-level discussions
Openness to conceptual thinking and risk-based tradeoffs
A desire to lead responsibly into the AI era

Training outline

8 modules

011 — AI from a manager's perspective3 topics

AI as a concept: beyond headlines
- Traditional automation vs adaptive intelligence
- Task-specific AI and general-intelligence concepts: distinguish demonstrated capability from speculation.
Key dimensions: perception, decision, interaction
Common misconceptions (AI will “think like humans,” full autonomy too fast)

022 — How modern AI works4 topics

Data, pattern learning, predictions and decisions: distinguish model output from human or system action.
Training vs inference (analogy: “learning phase” vs “doing phase”)
Tradeoffs: accuracy vs explainability, speed vs thoroughness
Risk areas: overfit, bias, unpredictability

033 — Machine learning, deep learning and LLMs4 topics

Machine learning: learning from historical data
- Supervised (input → label), unsupervised, reinforcement as business metaphors
Deep learning: layered “neural” abstractions
- Why deep learning is widely used for images, speech and unstructured data; suitability depends on the task and evidence.
Large Language Models (LLMs): generative text systems
- Tokenization and context (without math)
- Pretraining + fine-tuning logic
- Retrieval augmentation (hybrid systems)
Emergence & non-determinism
- Unexpected behaviours or capabilities: evaluate rather than assume intelligence or intent.
- Output variability: generation settings and system context can affect repeated responses; not every AI system is inherently non-deterministic.
- Risks: hallucinations, sensitivity to phrasing, hidden failure modes

044 — Capabilities, tools and evaluation4 topics

Core capability buckets: text, translation, summarization, question answering, image/audio, decision support
Architectural pattern: tools + models + orchestration
Evaluation criteria from a business lens: cost, latency, reliability, explainability, vendor stability
Integration challenges: data pipelines, compliance, security, scaling

055 — Prompting and output review4 topics

Why prompts matter: your textual instructions are the “interface” to the model
Core strategies:
- Instruction style (clear directive)
- Example style (show “good answers”)
- Request relevant evidence, assumptions or a concise rationale; use model-appropriate prompts rather than treating step-by-step reasoning prompts as universally necessary.
- Iterative refinement (test, adjust, compare)
Manager’s role: setting guardrails for prompt use, reviewing prompt templates, monitoring output quality
Prompt risk awareness: sensitivity, prompt injection, ambiguous instructions

066 — Tool-using agents in business4 topics

What is an “agent”?
- Goal-oriented, multi-step, using tools, with a feedback loop
Potential uses: research assistance, workflow orchestration and decision support, with defined limits.
Key design decisions managers should know:
- Scope boundaries and safeguards
- Visibility, audit logs, human oversight
- Fail-safe mechanisms, intervenability
Risks to watch: objective misalignment, “drift,” tool misuse

077 — Governance and responsible oversight5 topics

Why governance matters: risk, accountability, legal obligations and public trust.
Core principles: fairness, transparency, accountability, safety, privacy
Organizational levers:
- Governance bodies or committees
- Risk review, auditing, red teaming
- Documentation, logging, incident response
- Model version control, change tracking, risk thresholds
Applicable laws, contractual requirements and voluntary frameworks: distinguish obligations from guidance.
Governance in practice: balancing innovation vs control

088 — A measured adoption roadmap3 topics

Personal adoption (for managers)
- Experiment with small use cases, build intuitive grounding
- Stay current, connect with practitioners, ask the right questions
- Develop a “critical mindset” toward AI outputs
Organizational adoption stages
- Identify high-value, low-risk pilot use cases
- Define metrics (value, error rate, cost, time saved)
- Pilot, evaluate and iterate; scale only when evidence and operational readiness support it.
- Build infrastructure, data capability, integration, maintenance
- Build governance, compliance, stakeholder alignment
- Training, change management, culture shift
Common project pitfalls and illustrative lessons
- Underestimating integration, data quality, ongoing monitoring
- Overhyping results, underpreparing for errors
- Model drift, versioning, operational debt
- Resistance from staff, misalignment of expectations

A programme built around your team.

Share your training goals and requirements.