AI Fundamentals for Everyday Work and Team Automation

AI Fundamentals for Business Innovation is a 5-day foundation course that explains what artificial intelligence, machine learning and process automation are, and where they fit in daily work. It is for professionals with no technical background who must judge AI ideas. You take back the AI Use Case Canvas, completed for one task from your own team. The course is delivered by Mercury Training Center.

About This Course

Colleagues, vendors and leaders now talk about AI in every meeting, yet many professionals cannot tell a sound proposal from hype. To follow this course you only need to work with spreadsheets, reports or routine workflows. You practice by sorting real tasks, reading simple model results and filling in a canvas step by step.

Who It Is For

• Team leads responsible for deciding which routine tasks are worth automating
• Specialists responsible for reports, forecasts or customer records who will work beside AI tools
• Project staff responsible for gathering requirements for new software that includes AI features
• Support function staff responsible for HR, finance or procurement processes that vendors want to automate
• New managers responsible for answering questions from their teams about AI and job change

This course is not for engineers who want to build or train models, who are better served by a hands-on machine learning engineering course, nor for senior leaders setting an organization-wide AI direction, who are better served by a professional-level AI leadership course.

Competencies You Will Build

• AI Vocabulary: you explain AI, machine learning, deep learning and generative AI in plain words to colleagues
• Task Fit Screening: you sort team tasks into rules-based, predictive or generative and pick the right tool family
• Model Output Reading: you read accuracy, error and confidence figures and say what they mean for a decision
• Prompt Writing: you write clear, structured prompts for generative assistants and check the answers
• Risk Spotting: you name bias, privacy and error risks in a proposed AI use before it starts
• Pilot Framing: you describe a small AI trial with a clear goal, owner and success measure

What You Will Be Able to Do

By the end of the course you will be able to:

• From a list of your team's weekly tasks, select the two best suited to automation and explain why
• Given a vendor demonstration, write five questions that test what the tool really does
• Using a sample confusion matrix, state how often a model would be wrong and what that costs
• Given a customer service transcript, draft a prompt that produces a usable summary and a reply
• Using a short checklist, record the privacy and fairness points to raise before a trial
• From one chosen task, complete a one-page canvas ready to discuss with your manager

Course Content

Day 1: What AI Is and Is Not

• Plain-Language Definitions of AI, Machine Learning, Deep Learning and Generative AI
• Rules-Based Automation Versus Learning Models in Office Workflows
• Narrow AI Examples From Banking, Retail and Healthcare Services
• Common Myths About AI Replacing Jobs and What the Evidence Shows
• Building a Personal AI Glossary for Team Conversations

Day 2: How Machine Learning Learns

• Training, Testing and Prediction Explained With a Spreadsheet Example
• Supervised, Unsupervised and Reinforcement Learning in Everyday Tools
• Classification and Forecasting Use Cases in Sales and Maintenance
• Reading Accuracy, Precision, Recall and the Confusion Matrix
• Why Record Quality and Volume Shape Model Results

Day 3: Automation and Generative Assistants

• Robotic Process Automation for Invoices, Onboarding and Approvals
• Large Language Models and How Chat Assistants Produce Text
• Prompt Structure: Role, Task, Context and Format
• Checking Generated Answers for Errors and Invented Facts
• Matching Tasks to Rules, Prediction or Generation

Day 4: Responsible Use and Risk

• Bias and Fairness Risks in Hiring and Lending Examples
• Privacy and Confidentiality When Using Public AI Assistants
• NIST AI Risk Management Framework Core Functions for Beginners
• Human Review Points and Escalation in Automated Steps
• Questions to Ask Vendors Before a Trial

Day 5: Practicing the AI Use Case Canvas

• Exercise: Screening Your Team Tasks With the Day 3 Matching Method
• Exercise: Reading Results of a Sample Forecasting Model
• Exercise: Writing and Testing Prompts for a Real Work Task
• Exercise: Recording Risks and Review Points for the Chosen Task
• Completing and Presenting Your AI Use Case Canvas

Case Studies and Exercises

The following are suggested activities that you can adapt to your own work.

• Case study: A hospital front desk wants to automate appointment reminders; you decide whether rules or a learning model fits and justify it.
• Case study: A retail chain receives a demand forecasting tool demonstration; you list what the error figures mean for stock orders.
• Exercise: An HR team wants a chat assistant for policy questions; you write prompts and note the privacy limits.

What You Take Back

You take back the AI Use Case Canvas, filled in for one task from your own team. In your first month back you use it to brief your manager, ask vendors sharper questions and agree whether a small trial is worth running.

• Task description and the tool family that fits it
• Expected benefit and the measure that shows success
• Risks, privacy points and human review steps
• Questions for vendors and a proposed trial outline

Quick Answers (FAQ)

What do I need to know before an AI fundamentals course?

You need no coding or statistics. If you use spreadsheets, read reports or follow routine workflows at work, you can follow every session.

How does AI Fundamentals for Business Innovation differ from a professional AI strategy course?

It stays at foundation level: concepts, tool families and first judgments about one task. A professional strategy course assumes this knowledge and focuses on organization-wide roadmaps, investment and governance structures.

What is the difference between machine learning and automation in AI fundamentals?

Automation follows fixed rules that a person writes, such as routing an invoice. Machine learning finds patterns in past records and makes predictions, such as which invoice is likely to be late.

What do I take back from AI Fundamentals for Business Innovation?

You take back the AI Use Case Canvas, completed for one real task from your team, with its fit, benefit, risks, review points and a trial outline ready for discussion with your manager.

For Your Manager

After the course, your team member can explain core AI, machine learning and automation concepts in plain words, screen team tasks for automation fit and question vendor claims. They return with the AI Use Case Canvas completed for one team task, which they can first use in your next planning meeting to decide whether a small, low-risk trial is worth running.