Leading with AI

Leading with AI

From Hype to Strategy in One Day

AI has moved beyond isolated experiments and personal productivity tools. Leaders are now being asked to decide where AI should be used, which risks are acceptable, how results should be measured and what controls must be established before AI becomes embedded in important business processes.

This one-day course provides leaders, managers and decision-makers with a practical, non-technical understanding of artificial intelligence, generative AI and AI agents. It focuses on the questions leaders must answer rather than the technical details involved in building models: What can the technology reliably do? Where can it create measurable value? What could go wrong? Who remains accountable? How should an organization progress from controlled experimentation to responsible adoption?

The course reflects the growing shift from AI experimentation toward enterprise implementation, workflow redesign and governed execution. It also introduces current governance considerations influenced by the NIST AI Risk Management Framework, the NIST Generative AI Profile, ISO/IEC 42001 and risk-based AI regulation such as the EU AI Act.

The instructor brings more than 30 years of industry experience and will use practical, industry-demanded content shaped by real operational challenges, business trade-offs and organizational realities. The course avoids an overly academic treatment of AI and concentrates on the knowledge leaders need to make informed, defensible and commercially responsible decisions.

Learning Outcomes

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

• Explain AI, machine learning, generative AI and large language models in business terms
• Distinguish traditional automation, predictive AI, generative AI and AI agents
• Recognize common AI capabilities, limitations and business risks
• Evaluate AI tools, vendors and use cases from a leadership perspective
• Apply foundational prompting principles when directing AI-assisted work
• Identify essential governance, security and human oversight requirements
• Define practical measures for AI pilot success
• Outline a responsible roadmap from experimentation to enterprise adoption

Prerequisites

• No programming or technical background required
• General familiarity with business operations and decision-making
• Basic awareness of organizational risk and change management
• Interest in evaluating AI from a leadership and strategic perspective

Training Outline

1. Artificial Intelligence from a Leadership Perspective
   1. Defining artificial intelligence
   2. Traditional automation versus AI
   3. Predictive AI, generative AI and agentic AI
   4. Business value and strategic relevance
   5. Common misconceptions and unrealistic expectations
2. How Modern AI Works
   1. Data, models and pattern recognition
   2. Training and inference
   3. Machine learning and deep learning
   4. Large language models
   5. Tokens, prompts and context
   6. Foundation models and multimodal AI
   7. Retrieval-augmented generation
3. AI Capabilities and Business Applications
   1. Content creation and summarization
   2. Translation and document analysis
   3. Knowledge search and question answering
   4. Data analysis and decision support
   5. Customer service and employee assistance
   6. Workflow automation
   7. Image, audio and multimodal capabilities
   8. Suitable and unsuitable AI use cases
4. AI Limitations and Risk Awareness
   1. Hallucinations and inaccurate outputs
   2. Non-deterministic behavior
   3. Bias and incomplete data
   4. Privacy and confidential information
   5. Intellectual property concerns
   6. Automation bias and overreliance
   7. Prompt injection and data exposure
   8. Human accountability
5. Prompting for Leaders
   1. Prompts as management instructions
   2. Defining objectives and context
   3. Specifying audience and output requirements
   4. Applying constraints and boundaries
   5. Using approved prompt templates
   6. Reviewing and validating AI outputs
   7. Prompt-related security risks
6. AI Assistants, Copilots and Agents
   1. Chatbots, copilots and digital assistants
   2. Defining AI agents
   3. Goal-oriented and multi-step execution
   4. Tool use and system integration
   5. Human approval points
   6. Levels of autonomy
   7. Agent monitoring and auditability
   8. Risks of excessive permissions and unintended actions
7. Evaluating AI Tools and Vendors
   1. Business problem alignment
   2. Expected value and time-to-value
   3. Accuracy and reliability
   4. Security and data handling
   5. Integration requirements
   6. Vendor stability and support
   7. Licensing and operational costs
   8. Scalability and vendor lock-in
8. AI Governance and Responsible Oversight
   1. Accountability and ownership
   2. Transparency and explainability
   3. Privacy, fairness and safety
   4. AI policies and acceptable use
   5. AI system inventories
   6. Risk classification
   7. Human oversight requirements
   8. Testing, monitoring and incident management
   9. NIST AI Risk Management Framework
   10. ISO/IEC 42001
   11. EU AI Act awareness
9. Building an AI Adoption Roadmap
   1. Assessing organizational readiness
   2. Identifying high-value, low-risk opportunities
   3. Prioritizing use cases
   4. Designing controlled pilots
   5. Defining value, quality and risk metrics
   6. Evaluating pilot outcomes
   7. Moving from pilot to production
   8. Scaling with governance and monitoring
   9. Workforce training and change management
   10. Leadership action priorities

Course Scope

This one-day course provides a practical leadership overview of AI strategy, capabilities, risk, governance and adoption. The timeframe supports meaningful coverage of the major concepts but does not include technical model development, programming, detailed system implementation or certification-level treatment of individual governance standards.

Disclaimer

This training outline is provided as a general instructional guideline and reflects the anticipated course scope at the time of preparation. The trainer reserves the right to amend, reorganize, substitute, expand or omit content where professionally appropriate due to participant requirements, industry developments, regulatory changes, time constraints or other relevant circumstances. Such adjustments may be made without prior notice, provided that the general purpose and intended learning outcomes of the course are reasonably maintained.