AI for MBA Students: Cases, Team Projects, and Research

AI assistants help MBA students work through business cases faster, prepare more thoroughly for class discussions, and structure the analysis in team assignments. They are most useful for frameworks and synthesis—not as replacements for your own strategic judgment, which is the core skill the degree is building.

MBA programs are team-intensive. When multiple people are working on a case with AI assistance, the shared assumptions and source links need to live somewhere everyone can see them. Tools like fenbs keep the team's project decisions in one place, so every member and every AI session starts from the same agreed position.

Business case preparation

MBA cases are dense: a company situation, financial data, competitive dynamics, management decisions to be evaluated, and often ambiguous or conflicting information. An AI assistant can help you get oriented quickly—identifying the key facts, the central decision, and the frameworks that apply.

A useful approach: read the case yourself first and form your own initial view. Then ask the assistant to apply a specific framework—Porter's Five Forces, the value chain, the BCG matrix—to the situation you describe. Compare its output to your own analysis. Where they differ is where the interesting discussion lies.

Do not ask the AI to analyze the case for you before you have formed your own view. The case method is specifically designed to develop your judgment under uncertainty—letting AI do the first pass removes the friction that produces learning.

Research and source verification

MBA assignments often require going beyond the case to understand the industry, the competitive context, or the relevant regulations. AI assistants can help you identify what to look for—the industry structure, the key players, the regulatory environment—but the facts themselves must come from primary sources.

For US company data, EDGAR provides SEC filings including 10-Ks and proxy statements. For industry data, trade associations and government agencies publish sector-specific statistics. For academic research on strategy and management, SSRN and your institution's library database are the starting points.

AI assistants sometimes cite papers that do not exist or describe a company's position inaccurately. Verify any specific fact—a market share figure, a financial result, a regulatory requirement—before it appears in your submitted work.

Team assignments and shared work

MBA programs emphasize team-based work because it reflects professional reality. A team of four people, each working with different AI sessions, can easily end up with four slightly different versions of the same analysis—different market size assumptions, different framework applications, different conclusions on the same data.

The solution is a shared record of the decisions the team has made. When the team agrees on the market definition, record it. When two people debate the right framework and choose one, record the choice and the reasoning. AI is useful for the mechanics of team writing: merging draft sections, checking for consistency, suggesting transitions. It is not useful as a substitute for the team deciding what it actually thinks.

Assign clear ownership within the project record. Note which team member is responsible for each section, what the current state is, and what decision or input is needed to move it forward. That clarity prevents duplicate work, lost work, and the confusion that comes from everyone assuming someone else handled an open question.

Interview and discussion preparation

MBA programs include class discussions, case competitions, and recruiting interviews. AI assistants can help you prepare for all of these: ask the assistant to play devil's advocate on your analysis, to ask you the hardest question your professor would ask, or to run through a behavioral interview framework.

For case competitions, practice articulating your recommendation and its assumptions under pressure. For behavioral interviews, frameworks like STAR (Situation, Task, Action, Result) help structure responses. AI can review a response you draft and identify where the action is unclear or where the result is not specific. Record the situations and outcomes you plan to use in STAR responses, so you can refine them across multiple preparation sessions rather than reconstructing from memory each time.

Academic integrity in MBA programs

Business schools are actively developing AI policies, and they vary. Many programs distinguish between using AI as a thinking tool and submitting AI-generated analysis as your own work. The AACSB does not set a single AI policy—your school and your instructors do. When in doubt, disclose how you used AI and ask your professor.

How to do this with fenbs

1. Create a free account and set up a project for the assignment or case. Use a team workspace to share it with teammates.
2. Record the team's agreed assumptions as Decisions and rules: the market definition, the analytical framework chosen, the financial model parameters.
3. Save the verified source links in Links and listings: SEC filings, industry reports, the journal papers you have confirmed.
4. Track each assignment section as a task: whose section is it, what is its current state, and what is still needed.
5. Connect your AI assistant over MCP from Integrations. Ask it to read the project context and decisions before starting work.