Artificial Intelligence (AI) Principles and Practices |Smart

Overview

Why Attend

Society and organizations are creating petabytes of data, and with Artificial Intelligence (AI) we can put that data to work in order to improve well-being, increase revenue and reduce costs. With modern technology we can use internal and external, structured and unstructured data and apply Artificial Intelligence to bring new possibilities to make predictions, improve decision making, improve company performance and augment human capabilities.

However, this new field of science comes with new terminologies and technologies. But it is not just about data and technology. To really create business value with AI you need to scale up from isolated Proof of Concepts to a coherent approach and prepare the organization for effective use of AI. That needs a vision to define the best opportunities for AI to support the business, it needs a framework to understand which capabilities in the organization have to improve, and an implementation strategy to know what to do where and when.

This course provides participants with the AI literacy to be the business AI leader in their organizations, to understand AI concepts and use cases, to converse on a qualified level with the data specialists, to create an AI strategy and develop an AI ready organization, to know how to set up and run an AI project and to assess the make or buy decision of tooling.

Course Methodology

This courses applies a variety of interactions, ranging from team-work on case studies, to individual work on applying templates to their own experience, to group discussions about joint challenges.

Course Objectives

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

Explain AI as a concept and all its applications
Apply the different AI applications in the business value chain
Demonstrate the technologies and algorithms behind AI
Apply best practices in an AI project with its activities
Assess the available and necessary skills and competencies
Discuss on a qualified level with business and data specialists on relevant topics
Create and execute an AI strategy and develop an AI ready organization

Target Audience

This course is designed for senior, middle and high potential management who recognize that digital transformation and AI is unavoidable; and for those who understand that continuous improvement, innovation and disruption is part of doing business and want to be prepared and reap the benefits of Artificial Intelligence.

In short, this course is for managers wanting to identify what AI can do for them and to drive Digital Transformation, rather than understand the technical methodologies of what happens underneath its hood.

Understanding of basic technology concepts such as data and cloud is helpful but not required.

Target Competencies

AI Best Practice Application
AI Change Management
AI Business Translator
AI Project Management

Course Outline

Introduction to Artificial Intelligence (AI), Machine Learning (ML) and Data Science
- AI in historical setting and combinatorial technologies
- Introduction to AI, concepts, narrow and general AI
- Different types of AI
- AI - sense, reason, act
- The thinking in AI: Machine learning
Advanced Analytics vs Artificial Intelligence
- Looking back, now, forward
- 4 types of data analytics
- Analytics value chain
Algorithms but without technical jargon
- Supervised learning
- Unsupervised learning
- Reinforcement learning
Data as fuel for AI
- Structured and unstructured data
- The 5 V’s of data
- Data governance
The data engineering platform
- Just enough to understand the data architecture
- Big data reference architecture
- 3 categories of data usage
AI opportunity matrix
- Successful use cases by Porter’s value chain
  - Primary activities
  - Supporting activities
- Successful use cases by technology
  - NLP
  - Image recognition
  - Machine learning
Ideation of AI projects
- AI Funnel process
- Several idea generation approaches
- Prioritize projects
- AI project canvas
Running of AI projects
- Machine learning life cycle
- AI machine learning canvas
- When to make and when to buy AI solutions
How to transform to an AI ready organization
- Use the AI strategy cycle
- Dimensions of the AI framework
- Practical approach to assess the AI maturity of the organization
- Best organizational structures
- Benefits of an AI Center of Excellence
- Skills and competencies
AI and ethics
- Risks of AI
- Ethical guidelines
- Realizing trustworthy AI

Schedule & Fees

2026 Schedule & Fees

4 - 8 Oct, 2026

English

Per participant USD 2,900

4 - 8 Oct, 2026

Arabic

Per participant USD 2,900

8 - 12 Nov, 2026

English

Per participant USD 2,900

8 - 12 Nov, 2026

Arabic

Per participant USD 2,900

2027 Schedule & Fees

3 - 7 Jan, 2027

English

Per participant USD 2,900

3 - 7 Jan, 2027

Arabic

Per participant USD 2,900

7 - 11 Feb, 2027

English

Per participant USD 2,900

7 - 11 Feb, 2027

Arabic

Per participant USD 2,900

4 - 8 Apr, 2027

English

Per participant USD 2,900

4 - 8 Apr, 2027

Arabic

Per participant USD 2,900

9 - 13 May, 2027

English

Per participant USD 2,900

9 - 13 May, 2027

Arabic

Per participant USD 2,900

4 - 8 Jul, 2027

English

Per participant USD 2,900

4 - 8 Jul, 2027

Arabic

Per participant USD 2,900

8 - 12 Aug, 2027

English

Per participant USD 2,900

8 - 12 Aug, 2027

Arabic

Per participant USD 2,900

3 - 7 Oct, 2027

English

Per participant USD 2,900

3 - 7 Oct, 2027

Arabic

Per participant USD 2,900

7 - 11 Nov, 2027

English

Per participant USD 2,900

7 - 11 Nov, 2027

Arabic

Per participant USD 2,900

Artificial Intelligence (AI) Principles and Practices

Overview

Why Attend

Course Methodology

Course Objectives

Target Audience

Target Competencies

Course Outline

Schedule & Fees

2026 Schedule & Fees

2027 Schedule & Fees

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