AI Training for Employees

Lesson 1 • Types of AI Systems

Surveys narrow AI, generative AI, and autonomous agents by capability. Connects each type to realistic workplace applications.

Lesson 2 • What AI Actually Is

Defines AI, machine learning, and deep learning with clear distinctions. Establishes shared vocabulary used throughout the entire course.

Lesson 3 • Current AI Landscape

Surveys leading AI tool categories available to your team today. Sets realistic expectations about maturity, limitations, and pace of change.

Lesson 4 • AI Value in Business Contexts

Maps AI capabilities to productivity, decision support, and automation gains. Grounds abstract concepts in tangible organisational outcomes.

Lesson 1 • Organisational AI Policies

Explains how to read, interpret, and apply workplace AI usage policies. Ensures your employees operate within approved boundaries from day one.

Lesson 2 • Data Privacy and Confidentiality

Covers what data must never be entered into AI tools and why. Directly protects your organisation from data leakage and compliance violations.

Lesson 3 • Recognising AI Bias and Errors

Identifies how bias enters AI systems through data and design choices. Trains your employees to spot unreliable or skewed AI outputs before acting on them.

Lesson 4 • Core AI Ethics Principles

Introduces fairness, transparency, accountability, and privacy as guiding values. Provides the ethical foundation for all subsequent AI use in the course.

Lesson 1 • Task-Specific Prompting Techniques

Applies prompting strategies to writing, analysis, summarisation, and ideation tasks. Bridges general technique to the specific work your employees perform daily.

Lesson 2 • Iterative Prompt Refinement

Teaches how to diagnose weak outputs and improve prompts through iteration. Builds the habit of treating prompting as a feedback-driven process.

Lesson 3 • Anatomy of a Good Prompt

Breaks down the components of an effective prompt: role, context, task, and format. Gives your employees a repeatable structure for any AI interaction.

Lesson 4 • Prompt Patterns and Templates

Introduces reusable prompt patterns for common workplace tasks. Reduces trial-and-error by giving your employees proven starting frameworks.

Lesson 1 • Summarisation and Synthesis

Applies AI to condense long documents, meeting notes, and research into key points. Saves time while ensuring critical information is not lost.

Lesson 2 • Quality Control for AI-Generated Text

Establishes a review checklist to catch factual errors, tone mismatches, and policy violations in AI-generated content. Ensures every output meets professional standards.

Lesson 3 • Translation and Localisation Support

Uses AI to translate and adapt content for multilingual audiences. Highlights where human review remains essential for accuracy and cultural fit.

Lesson 4 • Editing and Tone Adjustment

Uses AI to improve clarity, conciseness, and tone in existing drafts. Teaches your employees to direct AI edits without losing their intended meaning.

Lesson 1 • Structuring Research Outputs

Guides your employees in organising AI-assisted research into clear, actionable deliverables. Ensures research translates directly into decisions and recommendations.

Lesson 2 • Competitive and Market Intelligence

Applies AI tools to monitor industry trends, competitor activity, and market signals. Connects research capability to strategic business awareness.

Lesson 3 • Evaluating AI-Sourced Information

Provides criteria for assessing credibility, recency, and relevance of AI-retrieved content. Prevents over-reliance on unverified AI-generated facts.

Lesson 4 • Document and Knowledge Base Analysis

Uses AI to extract insights from large internal document sets and knowledge bases. Reduces time spent manually searching organisational knowledge.

Lesson 1 • Decision Frameworks with AI Input

Integrates AI-generated insights into structured decision-making frameworks. Ensures AI supports rather than replaces human judgement in high-stakes choices.

Lesson 2 • Predictive Insights and Forecasting

Applies AI forecasting tools to project future outcomes from historical data. Teaches your employees to interpret confidence intervals and forecast limitations.

Lesson 3 • Descriptive and Diagnostic Analysis

Uses AI to summarise datasets and identify the causes of past performance trends. Builds the analytical foundation for predictive and prescriptive work.

Lesson 4 • AI-Assisted Reporting and Dashboards

Automates routine report generation and dashboard updates using AI tools. Frees your employees' time from manual reporting for higher-value analytical work.

Lesson 1 • Automating Communication Tasks

Applies automation to email triage, meeting scheduling, and notification routing. Demonstrates immediate productivity gains in the most time-consuming daily tasks.

Lesson 2 • Monitoring and Maintaining Automations

Establishes practices for auditing, troubleshooting, and updating live automations. Prevents automation drift and ensures workflows remain accurate over time.

Lesson 3 • No-Code AI Automation Tools

Introduces no-code platforms that connect apps and trigger AI actions without programming. Enables all employees to build automations regardless of technical background.

Lesson 4 • Identifying Automation Opportunities

Provides a framework for auditing daily tasks and scoring them for automation potential. Focuses effort on high-frequency, rule-based tasks with the greatest time savings.

Lesson 1 • Measuring AI Programme Impact

Defines KPIs for tracking productivity, quality, and adoption outcomes of AI programmes. Connects measurement to continuous improvement cycles.

Lesson 2 • AI Change Management

Addresses resistance, skill gaps, and cultural barriers to AI adoption in teams. Provides practical change management tactics tailored to AI rollouts.

Lesson 3 • Building an AI Use Case Business Case

Structures a compelling business case for AI investment using cost, time, and risk metrics. Equips your employees to advocate for AI initiatives with leadership.

Lesson 4 • Building a Continuous AI Learning Culture

Establishes team rituals, knowledge-sharing practices, and learning habits for ongoing AI growth. Ensures your organisation keeps pace with rapid AI advancement.