Why Attend
Organizations are creating an avalanche of data, and with Artificial Intelligence (AI) technology we can put that data to work in order to increase benefits and reduce costs. With modern technology we can use structured and unstructured data and apply Artificial Intelligence to bring new possibilities to improve decision making, improve company performance and augment human capabilities. However, this new field of science comes with new terminologies, technologies, jobs and organizational processes.
This course provides participants with the AI literacy to be the AI leader in their organizations, to understand AI concepts, to converse on a qualified level with the data specialists, to create an AI strategy, 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 manifestations
- Apply the different AI appearances 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
Target Audience
This course is designed for senior and middle management who recognize that digital transformation 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. Understanding of basic technology concepts such as data and cloud is recommended but not required.
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.
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 as a concept and appearances
- AI as a combination of technologies
- AI in historical perspective
- AI: sense, reason, act
- The thinking in AI: Machine learning
- 9 building blocks
- Algorithms and Engines
- Supervised learning and applications
- Classification: Algoritms like Naïve bayes
- Regression: Algorithms like Linear regression and decision trees
- Semi supervised learning and applications
- Algorithms like Q-Learning, SARSA
- Unsupervised learning and applications
- Clustering: Algorithms like kMeans and hierarchical
- Supervised learning and applications
- Defining an AI approach: teamwork
- Practice with building blocks and use cases
- Reflection and application to own organization
- Creative garage approach to ideate and define an AI Project
- AI opportunity matrix
- Successful use cases by Porter’s value chain
- Primary activities: Inbound operations and outbound marketing and sales and service
- Supporting activities: Admin and finance, HR, research and development, procurement
- Successful use cases by technology
- NLP
- Image recognition
- Machine learning
- Successful use cases by Porter’s value chain
- Running successful AI projects
- Project process
- Ideation & problem definition
- Exploratory data analysis
- Model development
- Implementation
- Skills and capabilities
- Organizational changes
- 10 pitfalls
- Project process
- AI tooling and roadmap
- Technologies: R, Python, Spotfire, Hadoop etc
- Platforms: Ms Azure, IBM Watson, Google Tensorflow
- Roadmap development
- Prepare your first roadmap
- Develop your strategy and tactics to realize an AI project funnel
