AI Automation Course

Lesson 1 • The Automation Opportunity Landscape

Maps the categories of tasks most suitable for AI automation across business functions. Helps learners identify high-value targets before building anything.

Lesson 2 • How Modern AI Models Work

Explains the core mechanics of large language models and predictive AI without requiring maths. Grounds later prompt and workflow design in accurate mental models.

Lesson 3 • What AI and Automation Actually Mean

Distinguish AI, machine learning, and automation as distinct but overlapping disciplines. Establishes shared vocabulary used throughout the course.

Lesson 4 • Key Tools and Platforms Overview

Surveys the major categories of AI automation tools available today. Sets context for tool selection decisions covered in later chapters.

Lesson 1 • Prompting Techniques for Structured Output

Teaches methods that force AI to return JSON, tables, lists, or other parseable formats. Structured output is the bridge between AI responses and downstream automation steps.

Lesson 2 • Testing and Iterating Prompts

Introduces a disciplined testing loop for validating prompt reliability across varied inputs. Prevents brittle prompts from breaking production automations.

Lesson 3 • System Prompts and Persistent Instructions

Covers how system-level prompts set persistent behaviour across a session or workflow. Enables consistent AI persona and rule enforcement in automated systems.

Lesson 4 • Anatomy of an Effective Prompt

Breaks down the structural elements that make prompts consistent and controllable. Directly enables the reliable AI outputs required in automated pipelines.

Lesson 1 • Error Handling and Workflow Reliability

Introduces strategies for catching failures and keeping workflows running despite errors. Builds the reliability mindset required before deploying automations to real users.

Lesson 2 • Understanding Triggers, Actions, and Logic

Explains the event-driven model that underlies all workflow automation platforms. This mental model is the prerequisite for designing any multi-step automation.

Lesson 3 • Building Your First AI-Powered Workflow

Guides learners through constructing a workflow that calls an AI model and uses its output. Produces the first end-to-end automation combining AI with app integrations.

Lesson 4 • Connecting Apps and Services

Covers how to link third-party applications through pre-built connectors and authentication. Enables data to flow between tools without manual transfer.

Lesson 1 • Webhooks for Real-Time Automation

Covers how to receive and send webhooks to trigger automations from external events. Enables real-time, event-driven workflows rather than scheduled polling.

Lesson 2 • Handling Files and Documents in Workflows

Teaches how to process, generate, and route files such as PDFs and CSVs automatically. Extends automation capability to document-heavy business processes.

Lesson 3 • REST API Fundamentals for Automators

Explains HTTP methods, endpoints, headers, and response codes in practical terms. Provides the foundation for calling any API from within an automation workflow.

Lesson 4 • Making API Calls from Workflows

Demonstrates how to configure HTTP request steps inside no-code platforms. Unlocks access to any API-enabled service beyond pre-built connectors.

Lesson 1 • What AI Agents Are and How They Work

Defines the agent architecture: perception, reasoning, tool use, and action loops. Distinguishes agents from simple prompt-response automations built in earlier chapters.

Lesson 2 • Multi-Agent Coordination Patterns

Introduces orchestrator-worker and peer agent architectures for complex task decomposition. Enables automation of processes too large or varied for a single agent.

Lesson 3 • Testing and Controlling Agent Behaviour

Provides methods for evaluating agent reliability, catching runaway actions, and enforcing guardrails. Responsible agent deployment requires systematic behavioural testing before production.

Lesson 4 • Designing Agent Goals and Instructions

Covers how to write agent system prompts that encode goals, constraints, and decision rules. Well-designed instructions are the primary control mechanism for agent behaviour.

Lesson 1 • Evaluating AI Output Quality at Scale

Introduces automated and human-in-the-loop methods for grading AI outputs continuously. Enables quality assurance without manually reviewing every output in high-volume workflows.

Lesson 2 • Maintaining and Improving Live Automations

Covers the ongoing maintenance cycle: reviewing performance, updating prompts, and adapting to changes. Automations degrade without active maintenance as data and tools evolve.

Lesson 3 • Defining Quality for AI Automation Output

Establishes measurable quality criteria for AI-generated content and automated decisions. Without defined quality standards, testing has no target and monitoring has no baseline.

Lesson 4 • Building an Automation Test Suite

Guides construction of unit, integration, and end-to-end tests for automation workflows. Systematic testing catches regressions before they reach users or corrupt data.

Lesson 1 • Automating Lead and Customer Workflows

Builds automations for lead capture, qualification, routing, and follow-up communication. Demonstrates AI automation applied to a high-volume, high-value business function.

Lesson 2 • Automating Data Collection and Reporting

Builds pipelines that gather, process, and summarise data into actionable reports automatically. Eliminates manual data wrangling that consumes significant analyst time.

Lesson 3 • Process Mapping Before Automation

Teaches how to document current-state processes to identify automation insertion points. Skipping this step is the leading cause of automations that solve the wrong problem.

Lesson 4 • Automating Content and Communication

Creates workflows that draft, personalise, and distribute content and messages at scale. Covers the most common AI automation use case across all business types.

Lesson 1 • Change Management for Automation Adoption

Addresses the human side of deploying automations that change how people work. Adoption failure is the most common reason technically sound automations deliver no value.

Lesson 2 • Governance and Risk Management

Establishes policies for approving, auditing, and retiring automations across an organisation. Governance prevents uncontrolled automation proliferation and associated operational risk.

Lesson 3 • Scaling Infrastructure and Operations

Covers the technical and operational changes needed when automations grow in volume and complexity. Scaling without infrastructure planning causes reliability and cost problems.

Lesson 4 • Measuring Business Impact of Automation

Defines how to track and report the financial and operational value delivered by automations. Quantified impact justifies continued investment and guides future prioritisation.