Artificial Intelligence AI for Business Professionals

Overview:

Introduction:

Petabytes of data are being generated by society and businesses; thanks to artificial intelligence (AI), we can use this data to enhance wellbeing, boost revenue, and cut expenses. This training program equips participants with the AI literacy they need to be the business AI leaders in their organizations.

Program Objectives:

At the end of this program, participants will be able to:

• Describe the idea of AI and all of its uses.

• Utilize various AI applications throughout the corporate value chain.

• Showcase the AI-related technologies and algorithms.

• Using an AI project's efforts, implement best practices.

• Examine the skills and abilities that are both available and required.

• Discuss pertinent issues in length with business and data experts.

• Create and implement an AI strategy, and build an organization that is AI ready.

Targeted Audience:

• Administrative professionals seeking to enhance efficiency with AI tools.

• Individuals interested in integrating ChatGPT into administrative workflows.

• Decision-makers aiming to leverage AI-powered data analysis for informed decisions.

Program Outline:

Unit 1:

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.

Unit 2:

Advanced Analytics vs Artificial Intelligence:

• Looking back, now, forward.

• 4 types of data analytics.

• Analytics value chain.

Unit 3:

Data as fuel for AI:

• Structured and unstructured data.

• The 5 V’s of data.

• Data governance.

Unit 4:

Algorithms but without technical jargon:

• Supervised learning.

• Unsupervised learning.

• Reinforcement learning.

Unit 5:

The data engineering platform:

• Just enough to understand the data architecture.

• Big data reference architecture.

• 3 categories of data usage.

Unit 6:

AI opportunity matrix:

• Successful use cases by Porter’s value chain.

• Successful use cases by technology.

Unit 7:

Ideation of AI projects:

• AI Funnel process.

• Several idea generation approaches.

• Prioritize projects.

• AI project canvas.

Unit 8:

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.

Unit 9:

Running of AI projects:

• Machine learning life cycle.

• AI machine learning canvas.

• When to make and when to buy AI solutions.

Unit 10:

AI and ethics:

• Risks of AI.

• Ethical guidelines.

• Realizing trustworthy AI.