Overview of current AI technologies and applications.
AI as a disruptive innovation in different sectors.
How you can create significant new value and solve your biggest business challenges using AI technologies.
AI Fundamentals 1
Machine Learning.
Neural Networks.
Deep Learning.
AI Fundamentals 2
Natural language processing.
Large language models.
AI Strategy
AI's implications for business strategy.
How to develop and execute an AI strategy to create a competitive advantage.
How to create an ecosystem to develop AI projects.
Case study.
AI and Business Transformation: developing an AI capacity
The impacts of (Gen)AI on Business Transformation
The (Gen)AI journey, its complexity and transformative potential
(Gen)AI Centre of Excellence (CoE) Playbook
AI for Productivity
GGen AI: key concepts and the Microsot AI & Open AI partnership.
AI across the MSFT Platform: Adopt Microsoft Copilot stack, Expand Microsoft Copilot or Develop your own copilot:
Microsoft Copilot Stack Overview.
Practical applications, use cases, benefits and efficiency gains (including real indicators resulting from the EAP - Early Adopters programme)
From apprehension to adoption: how to start and develop the AI journey.
AI for Automation
Introduction to AI
Definition and history of AI.
Types of AI: narrow vs. general.
Main AI technologies
Machine learning, deep learning and natural language processing.
Computer vision and robotics
AI in business
Use cases in all sectors (e.g. healthcare, finance, manufacturing).
Impact on productivity, innovation and competitiveness.
Automation and optimisation of business processes
RPA (Robotic Process Automation) and its applications.
Integration of AI to improve the efficiency of business processes.
Strategic adoption of AI
Developing an AI strategy for organisations.
Assessing readiness and overcoming challenges.
AI for Marketing & Content Creation
Improving the customer experience with AI.
Use generative AI as a creative tool.
AI & Data
Generative AI, evolution of technology.
Generative AI vs. traditional AI/ML.
Impact of generative AI on the economy.
Generative AI use cases by sector.
Main risks of generative AI.
Ethics of AI
Concerns about the use of AI.
AI transparency, bias and privacy issues.
Project
Case studies:
AI for ALL. The case of iCapital, Vanda Jesus, I Capital
AI applications in our product: Predictive analyses and Computer vision
Internal GenAI application - iChat
AI for Developers / Copilot application for Github
AI and data-driven strategy. The case of Sonae.
Strategic approaches to customer portfolio management based on AI, including customer lifecycle, customer lifetime value and customer portfolio management
Personalised approaches to customer interaction based on AI, including propensity models, Next Best Action, churn prediction, sequential pattern mining, dynamic pricing
