AI Productivity Playbook

Practical AI systems for faster work, better output, and scalable productivity in a day

Artificial intelligence has quietly shifted from being a specialized technology to becoming a general work interface. It reads documents, listens to meetings, drafts communications, analyzes information, generates visuals, builds websites, and increasingly completes multi step tasks on your behalf. Productivity gains today do not come from knowing technical internals. They come from understanding how to think with AI, how to structure work so AI can assist effectively, and how to choose the right tool for the right outcome.

This two day course is designed to help professionals build that leverage. It begins with a clear, practical grounding in what AI is and how it works, then maps the current AI landscape so participants can orient themselves without confusion. From there, the course goes very deep into tools and workflows across text, documents, presentations, automation, images, marketing assets, websites, accounting support, agentic systems, and command line style interfaces. The focus throughout is real workplace use, not theory.

The instructor brings over 30 years of industry experience and teaches based on how AI is actually being used and demanded in modern organizations, not academic abstractions.

Learning Outcomes

By the end of the course, participants will be able to:

Explain how modern AI systems work and why their strengths and limits are predictable.
Navigate the current AI ecosystem with confidence and clarity.
Select appropriate AI tools for writing, research, presentations, automation, design, operations, and decision support.
Use structured prompting and context design to reliably improve output quality.
Build repeatable AI assisted workflows for daily, weekly, and strategic work.
Apply AI safely and professionally with attention to accuracy, privacy, and organizational standards.
Understand how agentic AI systems can plan and execute tasks across tools.

Prerequisites

Comfort using a computer, browser based applications, and standard office tools.
Familiarity with documents, spreadsheets, presentations, and email.
No programming background required.
Access to at least one major AI assistant is recommended but not mandatory.

Detailed Training Outline

Foundations of Artificial Intelligence
1. What artificial intelligence means in practical terms
  1. Narrow AI versus general capability
  2. Generative AI versus analytical and rules based systems
2. How modern AI systems work conceptually
  1. Training data and learned patterns
  2. Tokens, context, and why phrasing matters
  3. Why AI can sound confident and still be wrong
3. Core building blocks explained simply
  1. Large language models as prediction engines
  2. Embeddings as representations of meaning
  3. Retrieval augmented generation for grounded answers
  4. Tools and actions as extensions beyond text
4. Strengths and limitations
  1. Pattern recognition at scale
  2. Creativity versus precision
  3. Situations where human judgment must lead
5. Reliability and trust
  1. Hallucinations and fabrication
  2. Incomplete context and hidden assumptions
  3. Over summarization and loss of nuance
6. Professional usage mindset
  1. AI as collaborator, not authority
  2. Review, verification, and accountability habits

The AI Landscape and Ecosystem
1. Major categories of AI tools
  1. Conversational assistants
  2. Research and search driven systems
  3. Office suite copilots
  4. Agentic and task execution systems
  5. Creative and generative media tools
  6. Automation and workflow platforms
  7. Local and command line driven tools
2. Where AI appears in daily work
  1. Browsers and search
  2. Desktop and operating systems
  3. Mobile devices
  4. Embedded inside enterprise platforms
3. Evaluating tools for professional use
  1. Accuracy and consistency
  2. Transparency and traceability
  3. File and data handling capabilities
  4. Privacy and compliance considerations
  5. Integration with existing workflows
  6. Cost models and licensing realities
4. The shift toward integrated AI environments
  1. AI embedded directly into documents, meetings, and systems
  2. Cross application context awareness
  3. AI as a layer rather than a destination

Prompting and Context Design for Productivity
1. Prompt structure that consistently works
  1. Role definition
  2. Objective clarity
  3. Context and constraints
  4. Output format specification
  5. Quality criteria and validation checks
2. Prompting patterns for everyday work
  1. Brainstorming and ideation
  2. Drafting structured documents
  3. Editing, rewriting, and tone adjustment
  4. Summarization for decisions and actions
  5. Planning, outlining, and sequencing work
3. Iterative collaboration with AI
  1. Ask first workflows
  2. Critique and revise loops
  3. Multiple option generation
  4. Comparative evaluation
4. Building a personal prompt system
  1. Reusable prompt templates
  2. Task specific prompt libraries
  3. Prompt adaptation for different audiences

Conversational and General Purpose AI Assistants
1. Capabilities and use cases of leading assistants
  1. Drafting and editing written communication
  2. Analyzing documents and reports
  3. Supporting decision making
  4. Acting as a thinking partner
2. OpenAI ChatGPT
  1. Multimodal interaction with text, files, images, and voice
  2. Long form drafting and refinement workflows
  3. Personal productivity and planning use cases
3. Google Gemini
  1. Search integrated reasoning
  2. Research oriented workflows
  3. Deep analysis and exploration patterns
4. Microsoft Copilot ecosystem
  1. Copilot chat versus application embedded copilots
  2. Word, Excel, PowerPoint, Outlook, and Teams usage patterns
  3. Organizational context grounding
5. Anthropic Claude
  1. Long document handling
  2. Structured writing and analysis
6. Meta AI assistants
  1. Messaging based assistance
  2. Lightweight everyday queries
7. Choosing the right assistant for the task
  1. Creativity versus precision
  2. Speed versus depth
  3. Context length and document handling

Research, Knowledge, and Document Intelligence Tools
1. Research first AI systems
  1. Source grounded answers
  2. Citation driven exploration
2. Research notebooks and knowledge bases
  1. NotebookLM
    1. Working with trusted sources
    2. Asking questions of your own documents
    3. Generating briefs, FAQs, and summaries
  2. AI inside knowledge management platforms
    1. Notion AI
    2. Turning notes into structured knowledge
    3. Project and decision documentation
3. Document processing and understanding
  1. Ingesting PDFs, scans, and reports
  2. Extracting tables, key facts, and obligations
  3. Contract and policy review assistance
  4. Compliance and risk scanning patterns

Agentic AI and Task Execution Systems
1. What agentic AI means in practice
  1. Planning steps toward a goal
  2. Executing across tools
  3. Monitoring progress and adjusting
2. Manus
  1. End to end task execution
  2. Research to output pipelines
  3. Presentation creation from raw inputs
  4. Multi document synthesis
  5. Operational task delegation
3. Agent patterns for professional work
  1. Research agents
  2. Reporting and analysis agents
  3. Content production agents
  4. Operations and administration agents
4. Guardrails for agentic systems
  1. Human approval checkpoints
  2. Scope limitation
  3. Auditability and traceability

Presentation and Content Creation at Scale
1. AI assisted presentation workflows
  1. Outline to narrative development
  2. Slide structure and flow
  3. Speaker notes and talking points
2. Advanced presentation generation
  1. Manus for automated deck creation
    1. Ingesting briefs, reports, and data
    2. Generating structured slide stories
    3. Iterative refinement cycles
3. Office suite based presentation support
  1. Microsoft PowerPoint with Copilot
  2. Google Slides with AI assistance
4. Quality control for presentations
  1. Message hierarchy
  2. Audience alignment
  3. Visual density and clarity

Generative AI Beyond Text
1. Image generation for professional use
  1. Marketing visuals
  2. Concept imagery
  3. Presentation graphics
2. Design and branding tools
  1. Canva AI
    1. Text to design workflows
    2. Brand kit consistency
    3. Social and marketing asset generation
3. Website creation without coding
  1. AI website builders
  2. Landing page generation
  3. Content structure and copy generation
4. Video and audio generation
  1. Explainer videos
  2. Voiceovers and narration
  3. Training and onboarding materials
5. Responsible use of generative media
  1. Brand alignment
  2. Accuracy and claims
  3. Ethical considerations

Automation Platforms and AI Driven Workflows
1. Automation fundamentals
  1. Triggers, actions, and conditions
  2. Human in the loop design
2. Microsoft Power Platform
  1. Power Automate with AI models
  2. Document processing flows
  3. Email, forms, and approvals automation
3. AI Hub
  1. Centralized AI model management
  2. Connecting AI services to workflows
4. Cross system automation use cases
  1. Document intake to summary
  2. Meeting notes to task creation
  3. Reporting pipelines
5. Governance and safety
  1. Permissions
  2. Error handling
  3. Monitoring outcomes

Command Line and Keyboard Driven AI Tools
1. Understanding CLI style AI interaction
  1. Speed focused workflows
  2. Minimal interface environments
2. Gemini CLI
  1. Text analysis and transformation
  2. Batch style workflows
3. Local and private AI environments
  1. Ollama
  2. Running models locally
  3. Privacy focused use cases
  4. Offline work scenarios
4. When command line tools make sense
  1. High volume text processing
  2. Sensitive information handling
  3. Power user productivity habits

AI for Finance, Accounting, and Operations
1. AI assisted financial workflows
  1. Invoice processing
  2. Expense categorization
  3. Financial summaries and reports
2. Agentic accounting assistance
  1. Manus in operational finance
  2. Cross system data synthesis
  3. Audit preparation support
3. Integration with automation platforms
  1. Power Automate financial workflows
  2. AI driven document extraction
4. Risk and accuracy considerations
  1. Verification requirements
  2. Human oversight checkpoints

Building a Personal AI Productivity System
1. Designing daily AI workflows
  1. Email and communication
  2. Meetings and follow ups
  3. Document creation
2. Weekly and monthly workflows
  1. Planning and prioritization
  2. Reporting and updates
  3. Retrospectives and reviews
3. Tool stack design
  1. Primary assistant
  2. Research and document tools
  3. Automation layer
  4. Creative generation tools
4. Measuring productivity impact
  1. Time saved
  2. Quality improvements
  3. Reduced rework and friction

Disclaimer: The trainer reserves the right to adapt, reorder, expand, condense, or substitute any part of the course content at their sole discretion during delivery. Such modifications may be made in real time based on available training time, evolving technology, tool availability, live demonstrations, organizational context, and the skill level, interests, and learning pace of participants. The course outline represents an indicative scope rather than a fixed agenda, and specific topics, tools, or depth of coverage may change without prior notice to ensure maximum relevance, practicality, and learning impact.

Practical, connected learning

My wider training approach brings hands-on implementation and systems thinking together, connecting technology with real operational needs.

AI Productivity Playbook