Strategy decisions now come with AI already in the room. MBA students use it to scan markets, model scenarios, and draft recommendations before a case discussion begins. The course therefore needs to answer a basic question early: how has AI changed the way a business wins?
What AI actually changes about enterprise advantage
Access to AI is no longer much of a moat. Competitors can use the same models. What differs is the data a firm brings to the model, how quickly it notices a change, and whether its governance makes an AI-supported decision reliable enough to use. Access is commoditized; disciplined use is not.
Use the following lenses in case discussions. The full set works as a rubric, but a case will usually need only three or four of them.
| Course lens | What students should answer |
|---|---|
| Enterprise edge | How does AI change industry structure, operating advantage, and the firm's ability to defend a position against rivals? |
| New moat | Does the advantage come from proprietary data, faster sensing, better scenario discipline, or governance that makes AI-supported strategy reliable? |
| Evidence and risk | Does the recommendation rest on traceable evidence, realistic assumptions, and risk controls leadership can defend, or does it rest on a confident-sounding output? |
| Corporate scope | Does AI change which businesses the firm should be in — where to expand, divest, integrate, or partner rather than build? |
| Execution capability | Can the firm actually implement this AI-enabled strategy, given its talent, change-management capacity, and workforce readiness? |
| Regulatory and ethical exposure | Does the recommendation account for the data privacy, IP, bias, and compliance risk created by using AI at this scale? |
That framework gives the course its starting point. Students then need to learn how to work with AI, not just describe its effects. Three areas belong together:
| Teach this | Why it matters if you skip it |
|---|---|
| Durable principles — how these models reason, and where that reasoning breaks down | Without it, students memorize this year's tool and understand nothing about next year's |
| Judgment — the discipline to catch a confident output that's wrong | Without it, you graduate students who can prompt but cannot defend a decision in front of a board |
| Practical tactics — how to put AI to work inside a live case by Monday morning | Without it, the principles stay theoretical and never touch a real decision |
The intended habit is simple: treat an AI output as a useful junior-associate memo. It may help, but it still has to be checked by the person making the recommendation.
What AI reasoning gets structurally wrong
The adoption gap is closed. The judgment gap isn't.
McKinsey's 2025 global survey found that 88% of respondents used AI in at least one business function, up from 78% a year earlier. The World Economic Forum identifies AI and information processing as a major force reshaping business. On campus, the numbers move the same direction: Cengage found that 83% of surveyed U.S. students considered AI literacy important, and HEPI reported that 95% of surveyed UK undergraduates used AI, with 68% considering AI skills essential.
The syllabus problem is no longer whether students will adopt AI. They already have. The missing piece is verification: testing an output before treating it as evidence or advice.
Text-only AI exercises are not enough. Students should have to defend a management decision when evidence, assumptions, and model output point in different directions. Put AI economics, data advantage, model risk, scenario uncertainty, and human approval into graded cases instead of treating them as stand-alone topics.
They also need practical routines for using AI without handing it the analysis.
Trade collection work for decision work
Put the whole chain of reasoning on one canvas
Jeda.ai is a visual AI workspace with framework-based outputs, document and data analysis, a collaborative canvas, and multi-model comparison. In this course, a team can keep its industry map, scenario matrix, source notes, and final recommendation on one screen. That makes the reasoning chain easier to inspect than a set of disconnected chat threads.
Start with approved sources and constraints. Then compare outputs from more than one model and check each claim against the original evidence before submission. A second model supplies another output, not an independent source. Students need to learn that distinction early.
Jeda.ai also offers an agentic MBA case workspace. Students can create, upload, edit, or select a skill; assign it to a specialist agent; and build a small agent team in a sandbox. Before work starts, the team reviews the Orchestrator's plan, adds the case question and shared files, keeps agent-specific files with the right agent, and resolves skill or team gaps. Reviewing the plan means the team approves an approach rather than simply accepting what the agents return.
The 45-minute test that reveals everything
Build the rest of the course
Use the sample syllabus, exercises, quizzes, projects, and instructor guide to extend the activity.
A useful pilot ends with one strategic choice, one rejected alternative, and the evidence that separated them.
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