AI Productivity Foundation And Practitioner (The Users) in Kiev

AI Productivity Foundation and Practitioner Course Outline

Module 1: Generative AI at Work: Foundations

• How Generative AI Works and LLMs Explained
• Generative AI vs Traditional Software
• Approved Tools and Model Selection at PMI
• Activity: Mapping Approved Tools to Your Role
• Data Rules, Confidentiality and Regulated Data
• Exercise: Identifying Safe vs Unsafe Lab Inputs

Module 2: Governance, Risk and Responsible Use

• PMI Organisational AI Policy Overview
• Human Accountability in AI-Assisted Work
• Data Privacy and Confidentiality Obligations
• Intellectual Property and AI-Generated Content
• Bias in AI Outputs and How It Arises
• Hallucination: Causes, Patterns and Risk
• Prompt Injection: What It Is and Why It Matters
• Exercise: Spotting Governance Red Flags in AI Use

Module 3: CO-STAR Prompt Engineering

• Why Prompt Structure Drives Output Quality
• The CO-STAR Framework: All Six Elements Explained
• Writing Effective Context and Objective Statements
• Calibrating Style, Tone and Audience
• Lab: Build Your First CO-STAR Prompt
• Iterative Prompt Refinement Techniques
• Fact-Checking and Validating AI Outputs
• Lab: Refine and Template a Prompt for Reuse

Module 4: Prompting for Workplace Communication

• Prompting for Executive Summaries and Briefings
• Cross-Functional Communication: Adapting Tone and Register
• Creating Internal Communications with AI Assistance
• Lab: Draft an Internal Announcement Using CO-STAR
• Generating Training Content and Presentation Narratives
• Lab: Build a Presentation Structure from a Business Brief
• Prompting for Structured Recommendations
• Exercise: Separate Evidence, Assumptions and Recommendations

Module 5: Analysing Business Documents and Data

• Preparing Documents for AI-Assisted Analysis
• Analysing Reports, Charts and Presentations with AI
• Lab: Analyse a Fictional Business Report
• Structuring Queries for Deep Document Insight
• Uploading and Querying Approved Spreadsheet or CSV Data
• Lab: Analyse a Fictional CSV Dataset for Business Insight
• Interpreting AI-Generated Data Summaries Critically
• Exercise: Challenge an AI Summary Against the Source Data

Module 6: Source-Grounded Research and Citation

• Source-Grounded Research: Principles and Prompt Structure
• Validating Citations and Checking Source Integrity
• Lab: Conduct a Source-Grounded Research Task
• Separating Evidence from AI Assumptions
• Distinguishing Recommendations from Stated Facts
• Exercise: Audit an AI Research Output for Reliability
• Building Trustworthy AI-Assisted Reports

Module 7: RAG, Role-Based Assistants and Agentic Workflows

• RAG Fundamentals and How It Reduces Hallucination
• Grounded Knowledge Sources: What They Are and How to Use Them
• Role-Based AI Assistants: Design and Use Cases
• Configuring a Role-Based Assistant for a Business Function
• Human-Supervised Agentic Workflows Explained
• Guardrails and Oversight in Agentic AI Tasks
• Lab: Design a Role-Based Assistant Persona for Your Team
• Exercise: Map a Business Process to a Supervised Workflow

Module 8: Microsoft 365 Integration and No-Code Automation

• Generative AI in Microsoft 365: Copilot and Approved Features
• Using AI Assistance in Word, Excel and PowerPoint
• Lab: Use M365 Copilot Features on a Fictional Document
• No-Code Automation Principles and PMI-Approved Tools
• Lab: Design a No-Code Workflow for a Routine Business Task
• Exercise: Evaluate an Automation for Governance and Safety

Module 9: Testing, Monitoring and Productivity Measurement

• Testing and Monitoring AI Outputs for Quality and Compliance
• Defining and Benchmarking AI Productivity Metrics
• Exercise: Build a Personal AI Productivity Scorecard
• Embedding Responsible AI in Your Daily Workflow
• Action Planning: Your 30-Day AI Adoption Commitment

Who Should Attend this AI Productivity Foundation and Practitioner Course?

This AI Productivity Foundation and Practitioner Course equips delegates with practical knowledge to use AI tools more effectively for research, content creation, analysis, and everyday professional tasks. This training can benefit a wide range of professionals, including:

• Marketing Professionals
• Business Analysts
• Content Creators
• Project Managers
• Entrepreneurs
• Operations Professionals
• Consultants

Prerequisites for the AI Productivity Foundation and Practitioner Course

There are no formal prerequisites for attending the AI Productivity Foundation and Practitioner Course. However, basic familiarity with digital tools and common workplace processes will support better understanding during the training.

AI Productivity Foundation and Practitioner Course Overview

The AI Productivity Foundation and Practitioner Course provides delegates with practical knowledge of using AI tools to improve workplace productivity, communication, analysis, and creative workflows. It introduces key AI concepts, prompt engineering techniques, multimodal tools, data analysis methods, and custom AI assistants for everyday business use.

This course helps delegates structure effective prompts, validate AI-generated outputs, reduce errors, and use AI responsibly across emails, reports, presentations, research, and campaign tasks. Delegates will also explore tools for visual creation, voice generation, video outreach, data analysis, and workflow automation.

This 4-Day course by The Knowledge Academy is designed to help delegates build confidence in applying AI to real workplace tasks. Upon completion, delegates will be able to create structured prompts, automate repetitive work, analyse information efficiently, and use AI tools to support faster, smarter decision-making.

AI Productivity Foundation & Practitioner Course Objectives

• To understand the science behind Large Language Models (LLMs), tokenisation, context windows, and common AI output limitations
• To apply structured prompt engineering techniques such as CO-STAR and Chain-of-Thought prompting
• To create visual, written, audio, and video assets using multimodal AI tools for professional workflows
• To analyse datasets, perform research, and identify trends using AI-powered analysis tools
• To utilise AI assistants within common workplace platforms to support faster and more efficient work

Upon completing this AI Productivity Course, delegates will be able to use AI tools confidently to improve productivity, automate routine tasks, generate insights, and enhance the quality of their work. They will also learn how to apply structured prompting, create AI-assisted content, and effectively use personalised AI assistants within their daily workflows.

What’s Included in this AI Productivity Foundation and Practitioner Course?

• World-Class Training Sessions from Experienced Instructors
• AI Productivity Foundation and Practitioner Examination
• AI Productivity Foundation and Practitioner Certificate
• Digital Delegate Pack

AI Productivity Foundation and Practitioner (The Users) Examination

To achieve the AI Productivity Foundation and Practitioner Certification, candidates will need to sit for an examination designed to assess their understanding of practical AI tools, responsible usage, and how AI can be applied to enhance day-to-day workplace productivity. The exam evaluates the candidate’s ability to use AI for research, communication, automation, and decision support while following ethical and organisational guidelines. The exam format is as follows: 

• Question Type: Multiple Choice 
• Total Questions: 50 
• Total Marks: 50 Marks 
• Pass Mark: 70%, or 35/50 Marks 
• Duration: 60 Minutes 
• Open Book/ Closed Book: Open Book