B-School Deans Confront AI, Geopolitics, and the Future of MBA Education

Business School Leadership Under Pressure: What to Do Next

Artificial intelligence, fractured geopolitics, and volatile economies are reshaping how graduate business schools must operate. A recent 70-minute remote panel with leaders from IMD, Rotman School of Management, Hong Kong University of Science and Technology, and Insead underscored one theme: uncertainty is the new baseline, and institutions need a clear plan.

For educators and administrators, this isn't theory. It's curriculum, policy, operations, and outcomes. Here's a practical playbook drawn from the issues raised-and the moves that will matter.

What's Driving the Pressure

• AI is spreading across every function and industry, forcing quick updates to curriculum, pedagogy, and integrity policies.
• Geopolitical fragmentation increases compliance risk, disrupts global careers, and complicates cross-border partnerships.
• Economic volatility challenges student ROI expectations, employer demand, and school finances.

Curriculum Priorities for the Next 12 Months

• AI across the core: Set baseline AI literacy in the first term. Require hands-on labs using common tools, model limitations, prompt strategy, and audit basics. Tie every course to at least one AI-supported assignment.
• Data to decisions: Push practical statistics, causal inference, experimentation, and dashboards. Students should design tests, read lift charts, and defend trade-offs.
• Geo-economics in practice: Teach sanctions, export controls, supply chain reroutes, and localization. Use live cases with shifting constraints and ask for plan A/B/C.
• Responsible tech and policy: Embed bias testing, privacy, copyright, and model governance. Reference public standards so students learn a common language.
• Execution under uncertainty: Scenario planning, option value, and risk-adjusted capital allocation belong in finance, strategy, and operations.

Teaching and Assessment That Fit the AI Era

• Assessment mix: More oral defenses, live case write-ups, whiteboard sessions, and data room "practicals." Keep take-home work but require process logs and artifact versioning.
• Clear AI-use policies: Define allowed vs. banned AI support by assignment. Require disclosure notes ("tools used, prompts, model version"). Grade on decision quality and ownership.
• Studio-style delivery: Short sprints, peer critique, and deliverables that mirror workplace artifacts: memos, dashboards, board packs, and model cards.

Career Outcomes: What Employers Want to See

• Evidence over claims: Portfolios with reproducible analysis, prompt libraries with rationale, and decisions backed by data.
• Regulatory awareness: Graduates who can spot exposure across jurisdictions and propose compliant workarounds.
• Operator mindset: Ability to ship, iterate, and handle constraints-budget, data quality, local rules.

Operational Moves for Deans and Program Directors

• Faculty upskilling: Fund short AI teaching fellowships and summer labs. Pair subject experts with technical coaches.
• Model governance for classrooms: Provide approved tools, data privacy guidance, and logging. Don't push risk to individual instructors.
• Partnerships: Co-develop briefs with employers, NGOs, and public agencies. Focus on cross-border issues and compliance-heavy sectors.
• Regional hedging: Build exchange options across regions to manage sudden policy changes without derailing student plans.
• Access and funding: Expand scholarships and flexible schedules. Volatility hits applicants unevenly; protect diversity and reach.

Admissions in the AI-Assist Era

• Publish AI rules for applications and interviews. Ask for an authenticity statement.
• Use structured interviews and practical tasks to evaluate judgment, communication, and learning agility.
• Value evidence of impact under constraint over polished prose.

Preparing Students to Find Opportunity in Unlikely Places

• Constraint-led innovation: Start with what's scarce-data, time, capital-and design minimum viable moves.
• Local context first: Teach students to adapt models and go-to-market strategies to local rules, languages, and norms.
• Public sector and SMB focus: Impact and hiring are growing where budgets are tight and problems are concrete.

12-Month Action Plan

• Quarter 1: Publish AI and assessment policies. Install approved tools and data controls. Launch faculty training. Add AI literacy to orientation.
• Quarter 2: Embed AI assignments in the core. Pilot geo-risk modules with scenario drills. Stand up an integrity review process.
• Quarter 3: Expand employer co-labs and capstones. Require portfolios for graduation. Add oral defenses in key courses.
• Quarter 4: Audit outcomes, refine policies, and scale what worked. Share a public accountability report.

Useful References

• OECD AI Principles for responsible AI language that maps well to classroom policy and governance.
• IMF on geo-economic fragmentation to inform country-risk teaching and partnership planning.
• AI upskilling paths by job role to quickly resource faculty and student learning tracks.

Why This Matters

The bar has moved. Employers expect graduates who can use AI well, respect rules across borders, and make decisions under pressure. Schools that respond with clarity-policy, curriculum, and evidence of impact-will set the standard.

Credit to the panel of leaders-David Bach (IMD), Susan Christoffersen (Rotman), Stephen Shih (HKUST), and Francisco Veloso (Insead)-for surfacing the hard problems and pointing to practical next steps.