The course offers a one-day comprehensive introduction to the world of Generative Artificial Intelligence (AI) tailored for business professionals. It begins by demystifying the basics of AI and delves deep into the nuances of Generative AI, contrasting it with Discriminative models. Participants are exposed to the capabilities and limitations of these models, supported by real-world case studies. The course emphasizes practical skills, introducing prompt engineering and the fine-tuning of models for specific business scenarios. It underscores the transformative potential of Generative AI across various industries, from marketing to financial forecasting, and provides hands-on activities and workshops. The program culminates in a group project, encouraging participants to conceptualize a Generative AI business proposal. Complementing the modules are supplementary materials and resources, ensuring a holistic understanding and application of Generative AI in the business realm.

Target Audience

The Artificial Intelligence Business Essentials course is focussed on individuals with an interest in, (or need to implement) AI in an organisation, especially those working in the following capacities:

  • C-Suite
  • Senior Managers
  • Organisational change practitioners and managers
  • Business change practitioners and managers
  • Program and planning managers
  • Service provider portfolio strategists / leads
  • Process architects and managers
  • Business strategists and consultants
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Access to OpenAI ChatGPT would be beneficial but not mandatory. There are no other prerequisites.

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Delegates will learn how to

  • Introduce Generative AI: Provide participants with a foundational understanding of Generative AI and its distinction from Discriminative models.
  • Explore Capabilities & Limitations: Delve into the strengths and challenges associated with Generative AI, emphasizing aspects like content creation, ethical considerations, and potential biases.
  • Highlight Real-World Applications: Showcase the transformative potential of Generative AI across various sectors, including marketing, design, and financial forecasting.
  • Foster Collaborative Learning: Encourage group activities and discussions, fostering collaborative learning and the exchange of innovative ideas.
  • Promote Ethical Use: Emphasize the ethical considerations and responsibilities when deploying Generative AI in a business context.
  • Guide Implementation Strategies: Offer insights into best practices for implementing and scaling Generative AI in businesses, from pilot projects to widespread adoption.
  • Facilitate Hands-on Experience: Engage participants in workshop, role-playing, and group projects, ensuring they can practically apply the knowledge gained.
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Module 1: Introduction to Generative AI

  • Begins with a welcome and an overview of the course.
  • Introduces Generative AI, discussing what it is and how it differs from Discriminative models.
  • Traces the historical evolution of Generative Models.
  • Offers an overview of ChatGPT and related models.
  • Engages participants with a group activity focused on interacting with Generative Models.

Module 2: Understanding Capabilities and Limitations

  • Offers a deep dive into how Generative Models work.
  • Highlights the strengths of Generative AI, such as content creation and data augmentation.
  • Discusses the limitations and challenges associated with Generative AI, including ethical considerations, biases, and unpredictability.
  • Encourages a group discussion where participants can envision how Generative AI might fit into their own businesses.

Module 3: Real-World Applications in Business

  • Explores the enhancement of creativity in design through Generative AI, touching upon domains like fashion, product design, and architecture.
  • Discusses how Generative AI can be used to deliver personalized customer experiences.
  • Delves into the role of Generative AI in financial forecasting.
  • Engages participants in a role-play activity where they pitch Generative AI solutions to stakeholders.

Module 4: Prompt Engineering and Fine-tuning Models

  • Introduces the concept of Prompt Engineering.
  • Discusses the art and science behind crafting effective prompts.
  • Covers the fine-tuning of generative models to cater to specific business needs.
  • Presents a case study on how companies, including OpenAI, leverage prompt engineering.

Module 5: Implementing Generative AI in Business and Wrap-Up

  • Offers insights into best practices for implementing Generative AI, from strategic planning to execution.
  • Emphasizes ethical considerations when deploying Generative AI.
  • Outlines a roadmap for scaling Generative AI from pilot projects to larger implementations.
  • Culminates with a group project where participants develop a Generative AI business proposal.
  • Concludes with closing remarks, a Q&A session, and a feedback session.
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