Artificial Intelligence Foundations for IT and Business

Course Length: 16 hours.

Build a clear, practical understanding of AI and how it transforms organizations.

This course introduces IT and business leaders to the essential foundations of AI through theory, practical demonstrations, and applied exercises. Participants gain a holistic understanding of core concepts, lifecycle stages, benefits, risks, ethics, governance, and real-world business applications. Ideal for professionals beginning their AI journey, the program provides a structured foundation to scale AI responsibly and strategically across the organization.

Introduction to Agile and Scrum Methodologies

Audience

IT and business professionals beginning their AI journey
IT governance officers
Digital transformation professionals
Developers and analysts seeking a broad understanding
Innovation and technology decision-makers

Objectives

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

Understand core AI concepts and foundational principles
Identify types of AI and cross-industry applications
Recognize benefits, risks, and challenges of AI adoption
Analyze the AI implementation lifecycle
Apply ethical principles and governance frameworks
Evaluate use cases and emerging trends
Understand how AI technologies support business processes
Reinforce learning through practical exercises

Course Content

Key concepts
Historical evolution of AI
Current AI landscape in business and technology
Expectations and realities of AI

Quiz: Exploring expectations

Narrow AI vs. General AI
Generative, predictive, and cognitive AI
Applications in key sectors: healthcare, finance, manufacturing, retail, government, etc.

Quiz: Exploring AI approaches by industry sector

Overview of Machine Learning, Deep Learning, and Natural Language Processing
Common algorithms and their purposes
Foundation models and their impact
Discussion of recommendations by approach

Responsible AI principles
Ethical and social risks
Regulatory frameworks and international standards

Discussion: Example of defining an ethical scope

Risk identification in use cases
Analysis of technological and organizational risks
Tools to prioritize AI initiatives

Exercise: Example of a Statement of Applicability (SoA) for an AI solution

Lifecycle phases: exploration, design, development, implementation, monitoring
Critical success factors at each phase
Roles and responsibilities in adoption

Exercise: Example of critical success factors in AI adoption

Required technological infrastructure
Data as an AI enabler
Integration with business processes

Exercise: Identifying AI support mechanisms

Technical challenges: scalability, accuracy, maintenance
Human challenges: cultural change, training, leadership
Enabling technologies: cognitive services, AI platforms, RAG, agents, etc.
Discussion of technical challenges based on organizational expectations

Benefits of AI architectures and their differentiators
Decision optimization (trade-offs)
Innovation in products and services
Documented success cases

Exercise: AI business case

Frameworks for enterprise AI adoption
AI solution architecture
AI portfolio governance and management
Enterprise architecture exercise in an AI environment

Understanding the scope of AI maturity assessment in an organization
Identifying AI opportunities in real processes
Simulating the adoption lifecycle for a selected case
Final evaluation and recap of acquired knowledge

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