Introduction:
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 the 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 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.
Targeted Groups:
- Those who understand that continuous improvement, innovation, and disruption are part of doing business and want to be prepared and reap the benefits of Artificial Intelligence.
- The managers to identify what AI can do for them and drive Digital Transformation, rather than understand the technical methodologies of what happens underneath its hood.
Course Objectives:
At the end of this course the 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.
Targeted Competencies:
- AI Best Practice Application.
- AI Change Management.
- AI Business Translator.
- AI Project Management.
Course Content:
Unit 1: Introduction to Artificial Intelligence (AI), Machine Learning (ML), and Data Science:
- AI in historical settings 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: Algorithms but without technical jargon:
- Supervised learning
- Unsupervised learning
- Reinforcement learning
Umit 4: Data as fuel for AI:
- Structured and unstructured data
- The 5 V’s of data
- Data governance
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
- Primary activities
- Supporting activities
Unit 7: Successful use cases by technology:
- NLP
- Image recognition
- Machine learning
Unit 8: Ideation of AI projects:
- AI Funnel process
- Several idea generations approach
- Prioritize projects
- AI project canvas
Unit 9: Ideation of AI projects:
- AI Funnel process
- Several idea generations approach
- Prioritize projects
- AI project canvas
Unit 10: How to transform into an AI-ready organization:
- Use the AI strategy cycle
- Dimensions of the AI framework
- A practical approach to assessing the AI maturity of the organization
- Best organizational structures
- Benefits of an AI Center of Excellence
- Skills and competencies
Unit 11: AI and ethics:
- Risks of AI
- Ethical guidelines
- Realizing trustworthy AI
