Scenario Bank — The Five Cases

Module 4, Unit 1 | Case study selection

By the end of this section, you will be able to:

• Understand how the five case scenarios are structured and why they are equivalent in challenge (K22)
• Read an assigned case for operational evidence, stakeholder tension, data constraints and human impact (S23)
• Use the scenario brief to prepare for workflow mapping, problem diagnosis and team planning (K22, S23)

Your first task — before Session 1. Read the Boardroom Brief and Activity Overview and browse the five case studies below. Then scroll down and submit your preferences using the form at the bottom of this page. Your instructor will use your rankings to assign groups — people with similar preferences will be grouped together where possible. If you do not submit, your instructor will assign you to a group.

How to use this page. Your instructor will assign groups using your stated preferences where possible. The five prepared cases are equivalent in difficulty, so every group is working with the same analytical challenge even when the sector differs.

Five Organisations, One Problem Shape

Each case is set in a UK-based mid-size organisation. Every scenario gives you:

• an organisation snapshot and business context
• two process descriptions with pain points
• stakeholder voices that do not all agree
• proposals already on the table
• data realities and constraints

Facilitator note. Assign groups before Session 1 where possible. Use the five scenarios as the default set, but match them deliberately to learner interests, sectors or project themes if that will improve engagement.

Your job is not to find the flashiest AI idea. Your job is to diagnose the real bottleneck, decide whether AI or automation is viable, and make a credible recommendation that a leadership team could act on.

The Five Cases

Submit Your Preferences

Once you have read the summaries above and browsed the individual case studies, rank all five scenarios below from 1 (your first choice) to 5 (your least favourite). Your ranking helps your instructor form groups around shared interests — your top choice is not guaranteed if there is high demand, in which case your next preference will be used.

KSB evidence focus

• K22: Collaborative working principles to explore AI and automation solutions and implement prototypes, pilots or proof of concepts.
• S23: Work with others to achieve agreed outcomes or outputs. Provide evidence-based analysis and insight to leaders on the likely human impacts of automation projects.