AI-Driven Productivity
and Agentic Automation Masterclass - 2 days
Understanding Modern AI, Enterprise AI Platforms, Agentic Systems and the Future of Autonomous Work
Artificial Intelligence has moved beyond experimentation and become a strategic operational capability that is reshaping how organizations function at every level. From automating repetitive administrative tasks to generating executive reports, orchestrating workflows, assisting software development, analyzing financial data and managing enterprise knowledge, AI is fundamentally changing the structure of modern work. Organizations are no longer asking whether AI will impact operations, but rather how quickly they can integrate it responsibly and competitively.
This intensive two-day program is designed to provide participants with both foundational understanding and practical enterprise exposure to modern AI technologies, platforms and automation models. The course covers how AI works internally, how modern Large Language Models operate, how prompt engineering and context engineering influence AI behavior, and how governance and risk management must evolve alongside adoption. Participants will also gain hands-on conceptual exposure to major AI ecosystems including ChatGPT, Gemini, NotebookLM and Claude, as well as emerging agentic AI systems such as Manus and OpenClaw that are rapidly redefining automation boundaries across knowledge work.
The program further explores how AI-driven automation is transforming Global Business Services (GBS), finance, reporting, operations, customer support and general knowledge-worker functions. Emphasis is placed on practical enterprise workflows, operational applicability and current market realities rather than purely academic discussions. The instructor brings over 30 years of industry experience and integrates real-world enterprise transformation practices, operational case studies and commercially relevant AI usage patterns aligned with current industry demand and evolving organizational expectations.
Learning Outcomes
By the end of this course, participants will be able to:
- Understand the foundational concepts behind Artificial Intelligence and Generative AI
- Differentiate between major AI classifications, architectures and operational models
- Explain how Machine Learning, Deep Learning and Large Language Models function
- Understand modern AI ecosystems and enterprise AI platform capabilities
- Identify practical AI use cases across enterprise operational environments
- Evaluate the benefits, limitations and risks associated with AI adoption
- Understand AI governance, compliance, privacy and ethical considerations
- Apply prompt engineering techniques for higher-quality AI interactions
- Apply context engineering principles for complex enterprise workflows
- Utilize ChatGPT features for productivity, research and automation workflows
- Utilize the Google Gemini ecosystem including Gemini CLI capabilities
- Utilize NotebookLM for AI-assisted research, knowledge extraction and document synthesis
- Utilize Claude for enterprise reasoning, analysis and long-context workflows
- Understand the concepts behind AI agents and autonomous AI systems
- Explore emerging agentic AI platforms including Manus and OpenClaw
- Analyze how AI is transforming GBS, finance and knowledge-worker operations
- Design AI-assisted workflow strategies for enterprise productivity improvement
- Evaluate enterprise readiness for AI-driven transformation initiatives
Prerequisites
- Basic computer literacy
- Familiarity with office productivity tools
- General understanding of business operations
- No programming background required
- No prior AI experience required
- Interest in enterprise productivity and digital transformation initiatives
Training Outline
- Introduction to Artificial Intelligence and the Modern AI Revolution
- Understanding Artificial Intelligence
- Definition of Artificial Intelligence
- Historical evolution of AI
- Key milestones in AI development
- AI research versus enterprise AI
- Current AI market landscape
- Enterprise AI adoption trends
- Consumer AI versus enterprise AI systems
- Core AI Terminology
- Artificial Intelligence
- Machine Learning
- Deep Learning
- Neural Networks
- Generative AI
- Foundation Models
- Large Language Models
- Multimodal AI
- Agentic AI
- Autonomous systems
- AI Categories and Classifications
- Narrow AI
- General AI concepts
- Predictive AI systems
- Generative AI systems
- Conversational AI
- Autonomous AI systems
- Reactive systems
- Limited memory systems
- Learning systems
- Enterprise AI Ecosystem Overview
- OpenAI ecosystem
- Google AI ecosystem
- Anthropic ecosystem
- Open-source AI ecosystems
- Enterprise AI platforms
- Cloud AI services
- AI APIs and integrations
- Local AI deployment concepts
- Current AI Industry Trends
- Enterprise AI race
- AI platform competition
- Rise of agentic AI
- AI copilots in enterprise environments
- AI-native workflows
- AI-driven business transformation
- Understanding Artificial Intelligence
- Understanding How AI and Large Language Models Work
- Machine Learning Fundamentals
- Data-driven learning concepts
- Training datasets
- Statistical learning principles
- Pattern recognition
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Deep Learning Fundamentals
- Neural network architecture concepts
- Layers and weights
- Embeddings
- Tokenization
- Training processes
- Model optimization concepts
- Large Language Model Architecture
- Transformer architecture concepts
- Attention mechanisms
- Context windows
- Token prediction concepts
- Inference processes
- Reasoning limitations
- Hallucination concepts
- Model Development and Alignment
- Pre-training concepts
- Fine-tuning concepts
- Reinforcement learning from human feedback
- Alignment and safety tuning
- Enterprise AI model customization
- Retrieval augmented generation concepts
- Multimodal AI Systems
- Text processing
- Image understanding
- Audio and speech processing
- Video understanding
- Cross-modal reasoning
- Real-time AI interactions
- AI Infrastructure Concepts
- GPU acceleration
- Cloud AI infrastructure
- AI inference environments
- API-driven AI consumption
- Local AI execution concepts
- Edge AI concepts
- Machine Learning Fundamentals
- AI Potential, Opportunities and Enterprise Transformation
- Enterprise Productivity Transformation
- AI-assisted knowledge work
- AI-enhanced decision support
- Workflow acceleration
- Intelligent process automation
- AI-enhanced reporting
- AI-assisted communications
- AI Use Cases Across Business Functions
- Finance and accounting
- Human resources
- Customer support
- Procurement operations
- Legal and compliance
- Marketing and communications
- Research and analytics
- Software development
- AI in Global Business Services
- Shared services transformation
- Intelligent service operations
- AI-driven ticket management
- Automated reporting workflows
- AI-assisted operational support
- Enterprise knowledge automation
- AI and Knowledge Worker Transformation
- Human-AI collaboration
- AI copilots for professionals
- AI-enhanced productivity
- Workflow augmentation
- Operational restructuring trends
- AI-native organizational models
- Enterprise AI Strategy Concepts
- AI readiness assessment
- AI capability maturity
- AI transformation roadmaps
- Organizational AI adoption
- Enterprise AI operating models
- Enterprise Productivity Transformation
- Risks, Governance and Responsible AI
- AI Risks and Challenges
- Hallucinations and misinformation
- Data privacy risks
- Security concerns
- Bias and fairness challenges
- Intellectual property concerns
- Compliance exposure
- Operational overdependence
- Responsible AI Principles
- Ethical AI concepts
- Transparency considerations
- Explainability concepts
- Human accountability
- Responsible deployment practices
- AI oversight models
- AI Governance Frameworks
- Enterprise AI governance
- AI policy creation
- Governance committees
- Model accountability
- AI lifecycle governance
- AI operational controls
- AI Security and Compliance
- Sensitive data protection
- Enterprise AI access controls
- Confidential information risks
- Regulatory developments
- Audit considerations
- Compliance frameworks
- Organizational and Workforce Implications
- Workforce disruption considerations
- Organizational resistance
- Change management
- AI reskilling requirements
- Future workforce models
- Human oversight requirements
- AI Risks and Challenges
- Prompt Engineering and Context Engineering
- Prompt Engineering Fundamentals
- Understanding prompts
- Instruction clarity
- Prompt structure design
- Context-aware prompting
- Role prompting
- Iterative prompting techniques
- Advanced Prompt Engineering
- Few-shot prompting
- Multi-step prompting
- Chain-of-thought concepts
- Persona prompting
- Structured output prompting
- Prompt chaining concepts
- Workflow prompting strategies
- Context Engineering Concepts
- Context windows
- Long-context optimization
- Persistent memory concepts
- Knowledge grounding
- Retrieval augmentation
- Context persistence
- Enterprise context management
- Prompt and Context Optimization
- Improving response consistency
- Reducing hallucinations
- Improving analytical reasoning
- Managing ambiguity
- Task decomposition
- Enterprise workflow optimization
- AI Interaction Best Practices
- Prompt testing methodologies
- Validation approaches
- Human review workflows
- AI output quality assurance
- Enterprise prompting standards
- Prompt Engineering Fundamentals
- ChatGPT Enterprise Productivity and Automation
- ChatGPT Platform Overview
- ChatGPT ecosystem overview
- GPT model families
- ChatGPT interface navigation
- Workspace organization
- Enterprise usage models
- Core ChatGPT Features
- Conversational AI interactions
- File upload capabilities
- Document analysis
- Summarization workflows
- Data interpretation
- Image understanding
- Voice interaction capabilities
- Web browsing features
- Advanced ChatGPT Features
- Custom GPTs
- Memory capabilities
- Project organization
- Deep research capabilities
- AI-assisted reasoning workflows
- Code generation capabilities
- Automation-oriented workflows
- ChatGPT for Enterprise Productivity
- Executive reporting
- Meeting summarization
- Policy analysis
- Presentation preparation
- Operational reporting
- Email drafting
- Knowledge management
- AI-assisted documentation
- ChatGPT Operational Best Practices
- Prompt optimization
- Data handling practices
- Validation workflows
- Governance considerations
- Enterprise AI policies
- ChatGPT Platform Overview
- Google Gemini Ecosystem and Gemini CLI
- Google Gemini Platform Overview
- Gemini model ecosystem
- Gemini multimodal capabilities
- Gemini architecture concepts
- Google AI ecosystem overview
- Gemini Workspace Integration
- Gmail integration
- Google Docs integration
- Google Sheets integration
- Google Slides integration
- Google Drive integration
- Meeting assistance workflows
- Gemini Advanced Features
- Long-context analysis
- AI reasoning workflows
- Real-time collaboration
- Research workflows
- Multimodal AI operations
- Enterprise productivity scenarios
- Gemini CLI and AI-Assisted Terminal Workflows
- Gemini CLI architecture
- Command-line AI operations
- AI-assisted scripting
- Developer productivity workflows
- Infrastructure support workflows
- CLI automation concepts
- AI-assisted operational tooling
- Google AI Ecosystem Expansion
- Google AI Studio overview
- Gemini APIs
- AI agent concepts within Google ecosystem
- AI workflow orchestration
- Enterprise AI integration possibilities
- Google Gemini Platform Overview
- NotebookLM and AI-Augmented Knowledge Management
- Introduction to NotebookLM
- NotebookLM architecture overview
- AI-powered research assistant concepts
- Source-grounded AI interactions
- Enterprise knowledge synthesis
- AI-assisted knowledge management
- Working with NotebookLM
- Uploading and organizing sources
- PDF ingestion workflows
- Document repository integration
- Research notebook creation
- Context-aware querying
- Cross-document analysis
- AI-Assisted Research and Knowledge Extraction
- Summarization workflows
- AI-generated study guides
- Cross-document synthesis
- Audio overview generation
- Knowledge extraction workflows
- Research acceleration techniques
- NotebookLM Enterprise Use Cases
- Policy and SOP analysis
- Compliance documentation review
- Internal knowledge repositories
- Audit preparation support
- Finance documentation analysis
- Operational knowledge management
- Enterprise research workflows
- NotebookLM Governance and Best Practices
- Source validation strategies
- Hallucination risk management
- Sensitive document handling
- Knowledge organization methods
- Enterprise governance considerations
- Introduction to NotebookLM
- Claude AI for Enterprise Reasoning and Analysis
- Claude Platform Overview
- Claude ecosystem overview
- Claude model families
- Constitutional AI concepts
- Enterprise positioning
- Claude Productivity Workflows
- Long-form document analysis
- Enterprise reasoning workflows
- Strategic analysis support
- Research summarization
- Structured analytical workflows
- Claude for Enterprise Operations
- Financial analysis support
- Business process analysis
- Governance and policy review
- Knowledge extraction
- Workflow documentation
- Operational intelligence support
- Claude Best Practices
- Effective prompt design
- Context optimization
- Long-context workflows
- Validation and review processes
- Enterprise governance alignment
- Claude Platform Overview
- Agentic AI and Autonomous Workflow Systems
- Understanding Agentic AI
- Definition of AI agents
- Autonomous execution systems
- Multi-step reasoning concepts
- Goal-oriented AI systems
- Persistent AI operations
- AI task orchestration
- Agent Architectures and Operational Models
- Tool-using AI systems
- Memory systems
- Multi-agent architectures
- Human-in-the-loop systems
- Workflow orchestration
- Autonomous planning concepts
- Manus AI Concepts and Capabilities
- Manus ecosystem overview
- Autonomous task execution
- Multi-step workflow automation
- AI-driven operational assistance
- Enterprise automation concepts
- OpenClaw and Open-Source Agentic AI
- OpenClaw architecture overview
- Messaging-driven AI operations
- AI workflow orchestration
- Persistent agent execution
- Tool integrations
- Open-source automation concepts
- AI Agents in Enterprise Operations
- Autonomous reporting workflows
- AI-driven operational monitoring
- Finance process automation
- Intelligent task delegation
- AI-based executive assistance
- Enterprise orchestration concepts
- Understanding Agentic AI
- AI Automation Across GBS, Finance and Knowledge Work
- AI Automation in Global Business Services
- Shared services transformation
- AI-driven service operations
- Ticket automation
- Intelligent escalation handling
- Automated operational reporting
- AI-assisted support environments
- AI in Finance Operations
- Accounts payable automation
- Accounts receivable automation
- Reconciliation support
- Financial variance analysis
- Audit preparation support
- Forecasting assistance
- Financial reporting acceleration
- AI Automation in Administrative Functions
- Calendar management automation
- Email automation
- Meeting coordination
- Document processing
- Internal communications automation
- AI-powered research support
- AI Transformation of Knowledge Work
- AI copilots for professionals
- Intelligent task prioritization
- AI-assisted analysis
- Productivity acceleration
- Human-AI collaboration models
- Future workforce implications
- Enterprise AI Adoption Strategy
- AI transformation roadmaps
- Governance integration
- AI operating models
- Workforce transition planning
- Enterprise AI capability development
- AI maturity assessment
- AI Automation in Global Business Services
- Future Trends in AI and Enterprise Automation
- Emerging AI Technologies
- Autonomous enterprise systems
- Persistent AI workers
- AI operating systems
- Enterprise AI orchestration
- AI-native workflows
- Future Workforce Models
- Human-AI hybrid organizations
- Digital labor concepts
- AI-managed workflows
- Enterprise restructuring trends
- Future enterprise operating models
- AI Industry and Market Evolution
- Open-source AI growth
- Enterprise AI competition
- AI regulation trends
- Economic implications of AI
- Enterprise adoption acceleration
- Long-Term AI Transformation Outlook
- Autonomous enterprise operations
- AI-first organizational design
- AI-enabled decision ecosystems
- Evolution of enterprise productivity
- Future of operational automation
- Emerging AI Technologies
Disclaimer:
This training outline is intended solely as a general guideline for the proposed course structure, scope and learning objectives. The actual delivery sequence, depth of coverage, demonstrations, technologies, tools, exercises and topic emphasis may be adjusted, expanded, condensed or otherwise modified by the trainer at his professional discretion based on participant profiles, organizational requirements, operational priorities, technological advancements and evolving industry practices without prior notice.
Practical, connected learning
My wider training approach brings hands-on implementation and systems thinking together, connecting technology with real operational needs.