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AI For Value Creation

Programme contents

AI and Value Creation

  • 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.



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
  • Analysis and debate

Register for the course here

Pedro Carvalho 3576

More Information

Contact our Program Advisor for further information and advice.