McTimothy Associates

Artificial Intelligence for Business – (Saturdays Only)

Why Attend

Society and organizations are creating petabytes of data, and with Artificial Intelligence (AI) we can put that data to work in order to improve well-being, increase revenue and reduce costs. With modern technology, we can use internal and external, structured and unstructured data and apply Artificial Intelligence to bring new possibilities to make predictions, improve decision making, improve company performance and augment human capabilities.

However, this new field of science comes with new terminologies and technologies. But it is not just about data and technology. To really create business value with AI you need to scale up from isolated Proof of Concepts to a coherent approach and prepare the organization for effective use of AI. That needs the vision to define the best opportunities for AI to support the business, it needs a framework to understand which capabilities in the organization have to improve, and an implementation strategy to know what to do where and when.

This course provides participants with the AI literacy to be the business AI leader in their organizations, to understand AI concepts and use cases, to converse on a qualified level with the data specialists, to create an AI strategy and develop an AI-ready organization, to know how to set up and run an AI project and to assess the make or buy decision of tooling.

$ 181.29

Event Date: –


Artificial Intelligence (AI) Strategy for Business Professionals

Course Outlines

  • Introduction to Artificial Intelligence (AI), Machine Learning (ML) and Data Science
    • AI in a historical setting and combinatorial technologies
    • Introduction to AI, concepts, narrow and general AI
    • Different types of AI
    • AI – sense, reason, act
    • The thinking in AI: Machine learning
  • Advanced Analytics vs Artificial Intelligence
    • Looking back, now, forward
    • 4 types of data analytics
    • Analytics value chain
  • Algorithms but without technical jargon
    • Supervised learning
    • Unsupervised learning
    • Reinforcement learning
  • Data as fuel for AI
    • Structured and unstructured data
    • The 5 V’s of data
    • Data governance
  • The data engineering platform
    • Just enough to understand the data architecture
    • Big data reference architecture
    • 3 categories of data usage
  • AI opportunity matrix
    • Successful use cases by Porter’s value chain
      • Primary activities
      • Supporting activities
    • Successful use cases by technology
      • NLP
      • Image recognition
      • Machine learning
  • Ideation of AI projects
    • AI Funnel process
    • Several idea generation approaches
    • Prioritize projects
    • AI project canvas
  • Running of AI projects
    • Machine learning life cycle
    • AI machine learning canvas
    • When to make and when to buy AI solutions
  • How to transform to an AI-ready organization
    • Use the AI strategy cycle
    • Dimensions of the AI framework
    • A practical approach to assess the AI maturity of the organization
    • Best organizational structures
    • Benefits of an AI Center of Excellence
    • Skills and competencies
  • AI and ethics
    • Risks of AI
    • Ethical guidelines
    • Realizing trustworthy AI

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