Module 1 — AI Opportunity Assessment
Where AI Adds Value
AI creates business value through three primary mechanisms: automation (performing tasks faster and cheaper), prediction (forecasting outcomes and identifying patterns), and personalisation (tailoring experiences to individual users).
Automation replaces repetitive, rule-based tasks: data entry, document processing, basic customer queries, invoice processing, scheduling. RPA (Robotic Process Automation) combined with AI can handle complex workflows that involve judgment, not just rules.
Prediction enables proactive rather than reactive management: predicting customer churn before it happens, forecasting demand to optimise inventory, identifying equipment failures before they occur (predictive maintenance), and scoring leads by likelihood to convert.
Personalisation powers individual-level customisation at scale: product recommendations, personalised marketing messages, adaptive learning paths, dynamic pricing. Netflix's recommendation engine reportedly saves $1 billion annually by reducing churn through personalised content suggestions.
The key question for any business is: where do our most valuable, high-volume, data-rich decisions happen? Those are the prime candidates for AI augmentation.
AI Readiness Assessment
Before implementing AI, organisations must honestly assess their readiness across several dimensions:
Data readiness: Do you have sufficient, high-quality data? Is it accessible, structured, and relevant? Is there a data governance framework? Many AI projects fail because the necessary data doesn't exist or is locked in silos.
Technical readiness: Do you have the infrastructure (cloud computing, APIs, data pipelines) to support AI? Do you have access to technical talent — in-house or through partners?
Organisational readiness: Is leadership committed? Is there a culture of data-driven decision-making? Are employees open to working alongside AI? Is there clarity about AI's role — augmenting rather than replacing human judgment?
Strategic readiness: Is there a clear business case? Are KPIs defined? Is there alignment between AI initiatives and business strategy?
The most common mistake is jumping to technology before understanding the problem. Start with a business problem, not a technology solution. Ask: "What decision do we want to improve?" rather than "How can we use AI?"
Key Takeaways
- AI adds value through automation, prediction, and personalisation
- The best AI opportunities are high-volume, data-rich decision points
- AI readiness spans data, technical, organisational, and strategic dimensions
- Start with a business problem, not a technology solution
- Most AI project failures stem from poor data or lack of organisational readiness
Exercises & Activities
AI Opportunity Mapping
Consider a mid-sized retail company with physical and online stores. Identify three business processes suitable for AI, classifying each as a 'quick win' (implementable in 3 months, moderate impact) or a 'transformational project' (6-18 months, high impact). For each, specify: the problem, the AI approach, the data needed, and the expected business value.
AI Readiness Self-Assessment
For an organisation you know (or a hypothetical one), rate AI readiness on each dimension (data, technical, organisational, strategic) from 1-5. Identify the biggest gap and propose three specific actions to close it.
Interactive AI Tutor Session
Copy this prompt and paste it into your preferred AI assistant (ChatGPT, Claude, Gemini) to begin your interactive tutoring session for this module.
"You are an AI strategy consultant. Teach the student how to assess where AI can add value in a business. Walk through an AI readiness assessment framework. Present a case of a mid-sized company and ask the student to identify three processes suitable for AI, classifying each as a quick win or transformational project. Provide feedback on their analysis."