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Course Outline
Foundations of Artificial Intelligence: Core Concepts, Classifications, and Clarifications
- Defining the scope of artificial intelligence and dispelling common misconceptions
- Distinguishing between narrow AI and general AI frameworks
- Comparing machine learning, deep learning, and data science disciplines
- Explaining machine learning mechanisms in accessible, non-technical terms
Generative AI and Autonomous Agents in Organizational Contexts
- Evaluating the capabilities and inherent limitations of generative AI systems
- Analyzing the operational mechanics of AI agents
- Reviewing standard organizational applications of generative AI technologies
- Addressing model hallucinations and the boundaries of current toolsets
Data Preparedness: The Essential Foundation for AI Adoption
- Differentiating between structured and unstructured data environments
- Assessing data quality and its critical dimensions for reliability
- Establishing essential data governance practices for leadership
- Explaining why data readiness is a prerequisite for effective AI integration
Strategic Areas for AI Value Creation
- Utilizing the AI opportunity matrix for strategic alignment
- Conducting value chain analysis to identify optimal AI use cases
- Differentiating primary from supporting activities in operational workflows
- Identifying high-impact processes for maximum value generation
Case Studies in AI Success and Operational Lessons
- Examining practical AI applications across various business functions
- Analyzing the factors that drive successful implementation outcomes
- Identifying common failure patterns and strategies for mitigation
Workshop: Identifying AI Opportunities by Departmental Function
- Mapping departmental processes and identifying operational pain points
- Developing specific AI use case concepts for each business area
- Completing a structured AI opportunity canvas
- Facilitating cross-departmental review and discussion of findings
Prioritizing AI Use Cases for Maximum Return on Investment
- Applying value and feasibility scoring methodologies
- Strategizing quick wins versus long-term strategic initiatives
- Implementing the AI project funnel for decision-making
- Selecting initial use cases for immediate execution
AI Governance: Roles, Oversight, and Accountability Structures
- Defining leadership responsibilities for AI within the organization
- Structuring governance roles, committees, and duty allocations
- Comparing Center of Excellence models with distributed ownership approaches
- Adopting best practices for robust AI governance frameworks
Security, Risk Management, and Responsible AI Practices
- Integrating information security and data protection requirements
- Conducting comprehensive risk assessments for AI initiatives
- Applying ethical guidelines to ensure responsible AI deployment
- Establishing trustworthiness in AI systems
Developing an AI-Ready Organizational Structure
- Evaluating organizational AI maturity levels
- Defining necessary skills and competencies for the AI journey
- Managing cultural change and organizational readiness
- Cycling through phases of the AI strategy process
Workshop: Developing the AI Implementation Roadmap and Action Plan
- Consolidating the identified opportunity map
- Establishing implementation phases, quick wins, and key milestones
- Assigning accountability, metrics, and governance checkpoints
- Finalizing the initial roadmap and subsequent action steps
Requirements
- No prior technical or programming expertise is required.
- A professional interest in applying AI within a management or administrative context.
Target Audience
- Senior managers and department heads.
- General managers and executive leadership.
- Leaders overseeing digital transformation and modernization initiatives.
16 Hours
Testimonials (1)
The trainer is patient and very helpful. He knows the topic well.
