Strategic Business Transformation using AI/ML

Programme Modules

• Pillar 1: Digital Transformation

  Module 1: The State of Disruption
  
  

  • Discuss the use of specific emergent technology for improving business performance
  • Identify how new technologies can disrupt existing industries and enable digital transformation
  • Explain the power of networks and how to use them for business purpose
  • Recognise the ways to harness emergent technologies to benefit businesses and enable disruption

  Module 2: Business Strategy Under Digital Transformation
  
  

  • Discuss the history and importance of business strategies
  • Describe the consequences of digitalisation of business strategies
  • Assess business strategy cases with process innovation and product and service innovation
  • Examine the future of business strategies in a fast-changing environment

  Module 3: Digital Business Models for a Digital Age
  
  

  • Identify opportunities that enable a firm to deliver greater values to its customers
  • Explain how segmentation enables a company to remap customers for digital growth
  • Create a value proposition for a company/brand
  • Evaluate the value proposition of a firm and reimagine it to achieve higher growth
  • Outline a plan for an organisation to create new values or grow more values for customers

  Module 4: Digital Business Implementation: Challenges and Risks
  
  

  • Evaluate the effectiveness of a new business model implementation by an organisation
  • Describe how organisations use emotions to create a reflex reaction to make customers aware of their digital solution
  • Identify how reflective communication enables customers to believe that a brand can deliver on its emotional promise
  • Create a matrix of solution expectations and experiences by segment for an organisation
  • Create a customer journey for a customer segment using the customer journey framework

  Module 5: Business Model Innovation
  
  

  • Explain the concept of business model innovation and how it transforms the entire organisation
  • Outline the growth plan of a business using the business model canvas
  • Identify the different triggers used by technology to enable a business model innovation

  Module 6: Business Model Innovation with Digital Networks
  
  

  • Draw the ecosystem of a company and discover the ways to enhance its value proposition with the help of new partners
  • Identify the appropriate approach to be an ecosystem builder/effective leader in digital business ecosystems
  • Outline an action plan to kickstart and grow a business ecosystem for an organisation

  Module 7: The Digital Organisation
  
  

  • Define your organisation - design, resources, people
  • Discuss the key barriers to the success of digital transformation
  • Create an executive summary for an organisation to adopt digitalisation
  • Examine your strengths to organisational understanding
• Pillar 2: Data Science & Analytics for Strategic Decisions

  Module 8: Leveraging Data as a Competitive Edge
  
  

  • Define the key terminologies of Data Science
  • Illustrate the different levels of Data Analytics and their significance to decision-making
  • Evaluate the applications of Data Analytics and its role in creating new business opportunities
  • Explore the features of data and data-enabled insights to attain sustainable competitive advantage

  Module 9: Data Analytics in Action
  
  

  • Identify appropriate analytical approach to resolve a business problem
  • Evaluate if your organisation is data-driven
  • Apply descriptive statistics to business data and obtain insights from it
  • Compare trends in data and obtain insights to leverage them for business performance
  • Analyse an organisation’s Omnichannel strategies and their impact on sales
  • Identify appropriate data/insights and determine suitable analysis approach for a business problem

  Module 10: Basic Statistics for Data Analysis
  
  

  • Illustrate hypothesis testing and types of errors
  • Apply various data analysis techniques to compare independent data sets to obtain insights
  • Apply strategic decision-making using data analysis techniques

  Module 11: Predictive Analytics
  
  

  • Apply the basics of regression to analyse the strength of variables and their impact
  • Predict variable impact using optimal model fit and regression effects
  • Build a logistic regression model to test and predict expected outcomes
  • Apply predictive analytics using logistic regression to optimise organisational efficiency

  Module 12: AI/ML for Data Analytics
  
  

  • Explain ML techniques and the ML workflow
  • Analyse Neural Networks and Deep Learning with examples
  • Evaluate the applications of AI in Retail, Healthcare and Manufacturing
  • Explain the concept of Machine Learning and its role in driving organisational productivity
  • Outline how Machine Learning algorithms can be applied to achieve optimal analytical accuracy
  • Explore the facets of Neural Networks and Deep Learning to understand how to build a programme that learns from data
  • Demonstrate how analytics can be combined with experiments to produce effective business strategies

  Module 13: Addressing Key Challenges and Risks in Data Science Projects
  
  

  • Identify the key challenges to Data Science projects and strategise solutions to overcome them
  • Identify project-level risks and examples of failed Data Science projects
  • Predict the success of your Big Data project using the DATA technique
  • Apply the DELTA Framework and the DELTA Plus Model to optimise your organisation’s Data Analytics journey

  Module 14: Data Science and the Future
  
  

  • Identify the drivers, expected outcomes and technology enablers for Industry 4.0
  • Identify the components for AI success that can be leveraged to strengthen organisational capabilities
  • Discuss the challenges in the implementation of AI in systems
  • Evaluate an organisation’s digital transformation journey and explain the ways to sustain their competitive advantage
• Pillar 3: Artificial Intelligence (AI) - Led Business Strategies

  Module 15: An Overview of Artificial Intelligence and Machine Learning (AI and ML)
  
  

  • Explain the concepts of Artificial Intelligence and Machine Learning along with their significance
  • Explain the significance and types of Data and Analytics in the context of business
  • Evaluate if your organisation is data-driven
  • Analyse the best-practices of undertaking a data-driven transformation

  Module 16: AI and ML Technologies Survey
  
  

  • Explain the principles of Supervised and Unsupervised Learning and describe the differences between them
  • Explain the difference between Regression and Classification
  • Explore the facets of Neural Networks and build a programme that learns from data
  • Explain how Convolutional Neural Networks apply to images and computer vision problems
  • Survey unsupervised learning algorithms such as clustering, matrix factorisation and representation learning
  • Describe Recurrent Neural Networks and Transformer-based architectures and explain how they impact language understanding

  Module 17: AI/ML Applications and Trends
  
  

  • Evaluate the applications of AI in finance, energy, education, and security sectors
  • Discuss the benefits and limitations of AI
  • Describe the application of AI framework for business
  • Analyse how AI can be leveraged as a driver for digital transformation

  Module 18: AI Led-Business Transformation
  
  

  • Describe the process for AI strategy creation and identify potential reasons for failure
  • Evaluate the Buy vs Build platforms for optimising AI-led business transformations
  • Identify AI opportunities and understand how to align them with digital business strategies
  • Explore various facets of the future of AI
  • Compare correlation and causality and describe their significance to enhance business performance
  • Apply experimentation to business problems to make effective inferences
  • Explain Multivariate, A/B and Multi-Armed Bandit testing and their significance in understanding user behaviour
  • Evaluate the effectiveness of a firm’s experimental design to make data-informed recommendations for business growth

  Module 19: Framework for Model Evaluation and Deployment of AI and ML
  
  

  • Identify the key challenges to Data Science projects and strategise solutions to overcome them
  • Identify project-level risks and examples of failed Data Science projects
  • Predict the success of your Big Data project using the DATA technique
  • Apply the DELTA Framework and the DELTA Plus Model to optimise your organisation’s Data Analytics journey

  Module 20: Ethics
  
  

  • Define the concept of ethics and explain its significance in the context of organizations and technology
  • Establish the purpose and relevance of ethics with regard to AI/ML, and define the corresponding ethical codes and guidelines
  • Analyse the main challenges involved in ethics and industry applications
  • Explore the values and ideas related to AI/ML, technology, data, and ethics
  • Evaluate how challenges relating to ethics and its industry applications can be addressed

  Module 21: Privacy and Regulations in AI and ML
  
  

  • Explain the guidelines for ethics and trust related to AI
  • Examine the AI-specific regulations in law and the extent of regulatory governance.
  • Analyse the concept of Personal Data Protection and the impact of AI on data rights and obligations
  • Describe the role of AI in eCommerce and the legality of Electronic Advertising and Marketing
  • Outline a plan that can help an organisation improve its compliance with AI ethical guidelines and law