Artificial Intelligence (AI) essentials refer to the structured understanding of the models, systems, and logic frameworks that define intelligent machine behavior. It focuses on the theoretical elements that underpin the design and function of intelligent technologies across business and technical domains. This training program presents the theoretical foundations, algorithmic structures, and data-driven logic behind AI systems. It emphasizes the institutional implications of AI and the role of strategic alignment in managing its integration responsibly and effectively.
Identify the foundational elements and conceptual structures of artificial intelligence.
Distinguish between core AI domains, including machine learning, natural language processing, and expert systems.
Outline methods for evaluating AI readiness across business functions and organizational structures.
Explore ethical, regulatory, and governance considerations related to AI deployment.
Gain the skills to structure AI strategy elements within enterprise-level planning models.
Technology Strategists.
IT Managers.
Data Analysts.
Innovation Officers.
Business Transformation Leaders.
Definitions and historical evolution of AI.
Major fields under the AI umbrella.
Distinction between narrow and general AI.
Relationship between AI, data science, and automation.
AI capabilities in strategic planning and forecasting.
Structure of AI systems and components.
Overview of machine learning models.
Neural networks and their functional roles.
Knowledge-based systems and reasoning engines.
Data flow within AI decision structures.
Organizational domains influenced by AI.
Frameworks for AI-readiness evaluation.
AI’s impact on roles, decision-making, and process flows.
Planning structures for responsible AI adoption.
Models for aligning AI with operational objectives.
Core ethical principles in AI development.
Bias, transparency, and accountability frameworks.
Global regulatory trends and compliance standards.
Governance models for enterprise AI.
Stakeholder roles in ethical oversight.
Elements of a structured AI strategy.
How to set long-term AI objectives and use-case criteria.
Oversight on the integration process of AI into digital transformation plans.
KPIs for AI performance and organizational value.
Role of leadership in guiding AI implementation direction.
