AI for executives (Day 2)

Outline

Introduction to AI

• Course objectives: AI for everyone
• Definitions: AI, machine learning (including deep learning and reinforcement learning), data analytics, customer science, etc.
• Historical context: AI past and present
• Present-day usage
• AI within the corporate context
• Significance and urgency: Why now and why you?

AI in industry today: Practical applications and benchmark leaders

• A selection of practical applications
• Strategic approaches: Commissioning versus procurement (build or buy?)
• AI maturity models and the enabling preconditions within your organization to use AI well (data, skills, tools, etc.)

Data in your organization: What are your raw materials?

• What data assets do you have, where does it live, who owns those systems, where are the gaps, and how could you fill them (e.g., by buying data or installing sensors)?
• What skills do you have access to from your part of the business, and what gaps might there be?
• What tools do you use, where are the gaps, what other options are there?

Project selection: What makes a good AI use case?

• Overview of the four key criteria for successful project selection and how to assess them
• Group exercise: Create a project longlist (often categorized broadly within marketing, operational efficiency, operational effectiveness, and commercial optimization)
• Scoring longlist against key criteria and plenary discussion

Managing an AI project: How is it is different from other projects?

• Similarities and differences
• Project lifecycle and timescales
• Project delivery methodology
• Typical staffing profiles
• Cost estimates for external suppliers (for data science as a service and for consulting)
• Performance metrics
• Operations and maintenance
• Governance and risk management

Leading or supporting a data transformation

• What is data transformation (as opposed to gradual reform), and when might it be necessary?
• The ABCs of data transformation
• Common pitfalls facing successful data transformation initiatives
• Different operational models for data science within a modern organization
• Next steps