• Intro to AI and Machine Learning (ML).

• Understanding the types of ML powering AI in business today.

• The importance of training data and how proprietary data can give your firm a competitive advantage.

• Managing limitations and risks associated with Predictive AI.

• Intro to Generative AI (GenAI) and Large Language Models.

• Prompt engineering.

• Using proprietary data to customize GenAI solutions with Retrieval Augmented Generation (RAG) and fine-tuning.

• AI Agents and agentic workflows.

• Open-source versus proprietary GenAI models.

• Managing limitations and risks associated with GenAI.

What to expect

Although all PracticalAI work is client-specific, here are a few examples of common topics.

• The right questions to ask and the right mindset to adopt.

• Deciding where to be on the AI leadership spectrum.

• Identifying high-value AI use cases.

• Measuring success with AI initiatives.

• Why AI is not always the best answer.

• Ethical AI, Responsible AI and AI Governance.

• Thinking strategically about AI going forward.

I. Fundamentals

II. State of the art

III. Moving forward