Make an Interactive Case Study: BigBear.ai’s Turnaround and What Learners Can Extract

Hook: Turn a real corporate pivot into a classroom that trains strategic thinkers

Students and instructors struggle to find high-quality, realistic case materials that combine finance, strategy, and public-sector risk. BigBear.ai’s 2025–2026 turnaround — eliminating debt and acquiring a FedRAMP-approved AI platform — is a timely, multi-dimensional story you can use to teach strategic decision-making, financial restructuring, and government contracting risk. This interactive case study template turns that corporate pivot into a hands-on group project with measurable learning outcomes.

Why this case matters in 2026

As we move through 2026, three connected trends make this case especially relevant:

• Government cloud and AI procurement is mainstream. FedRAMP approvals now accelerate enterprise access to federal contracts, and educators must teach students how compliance transforms competitive advantage.
• Financial restructuring and debt elimination are strategic tools. More companies in the tech and defense-adjacent sectors completed restructurings in 2024–2025 to free capital for growth; analyzing trade-offs between deleveraging and pursuing M&A is a core skill.
• AI governance and vendor risk are top-of-mind. With new regulation and continuous monitoring expectations, learners need risk frameworks that combine ethical and legal playbooks, cybersecurity, and business continuity.

Case overview: BigBear.ai’s pivot (teaching summary)

Use this one-paragraph summary at the start of your case packet. In late 2025 BigBear.ai eliminated its near-term debt obligations and completed an acquisition of a FedRAMP-approved AI platform. The actions improved solvency and opened federal contracting avenues, but the company still faced falling revenue and concentrated government client exposure. The strategic question for teams: should the company use its reset balance sheet to double down on government AI services, diversify into commercial markets, pursue additional M&A, or return capital to shareholders?

Learning objectives

• Apply financial restructuring concepts to model post-debt capital structures and cash-flow scenarios.
• Assess acquisition value using discounted cash flow (DCF) and scenario analysis that includes regulatory and contracting risk.
• Construct a risk register for government-facing AI offerings, integrating continuous monitoring and compliance requirements.
• Practice strategic decision-making: recommend a 24-month plan with resource allocation, KPIs, and contingency triggers.
• Develop presentation, negotiation, and persuasive writing skills by preparing a board memo and investor Q&A.

Case materials you should prepare

Provide students with a compact packet: a financial snapshot, acquisition details, and regulatory context. Below is a checklist of materials instructors can create or curate.

• Condensed financials (last 3–5 years): income statement, balance sheet, cash-flow statement.
• Acquisition term sheet summary (price, integration assumptions, earnouts).
• FedRAMP baseline: what approval level was acquired (e.g., Moderate/High), documentation checklist, and continuous monitoring obligations.
• Customer concentration table (percent revenue by client segment, government vs commercial).
• Press releases and analyst snippets from late 2025–early 2026 to simulate market reaction (market event guidance).
• Optional: redacted internal emails or memos to create role-playing empathy and negotiation scenarios.

Classroom structure and timeline

This case works well over 3–6 weeks, adaptable for an undergraduate course, MBA strategy class, or executive education module.

1. Week 1: Case distribution, readings on FedRAMP and federal procurement, team formation.
2. Week 2: Finance workshop — building a base financial model (DCF / sensitivity analysis).
3. Week 3: Risk workshop — creating a risk register and stakeholder map.
4. Week 4: Strategy session — teams craft a 24-month strategic plan and board memo.
5. Week 5: Presentations and Q&A, including a mock board/boardroom simulation.
6. Week 6: Debrief and reflection; optional extension project (investor pitch or regulatory compliance plan).

Group project: Assignment brief (student-facing)

Divide the class into teams of 4–6. Each team plays the role of BigBear.ai leadership for 24 months and must submit four deliverables.

Deliverables

• Board memo (3–5 pages): Executive recommendation on strategy (prioritize one: federal growth, commercial diversification, M&A, or capital returns). Include rationale, expected financial outcomes, KPIs, and integration risks.
• Financial model (spreadsheet): Three scenarios (base, aggressive, downside) with revenue drivers, margins, capex, and valuation outputs. Include sensitivity tables and a short appendix explaining assumptions.
• Risk register and mitigation plan: Identify top 8–10 risks (e.g., revenue concentration, FedRAMP continuous monitoring failure, cyber breach, integration delays), classify by likelihood and impact, and propose controls and contingency triggers. For legal and ethical issues, consult an ethical & legal playbook.
• 15-minute presentation + 10-minute Q&A: Present to a panel acting as the board and investors. Panel will probe assumptions and test operational readiness.

Roles within each team

• CEO / Strategy lead: Owner of the board memo and strategic narrative.
• CFO / Financial modeler: Builds the spreadsheet, calculates valuation impacts, and prepares sensitivity analyses.
• Head of M&A or Business Development: Leads acquisition integration or partnership strategy.
• Head of Compliance & Security: Designs FedRAMP continuous monitoring plan and the risk register entries tied to government contracting.
• Communications lead: Prepares investor messaging and press Q&A drafts. Consider advanced client-retention and comms strategies when advising investors.

Teaching notes: How to run workshops and grade effectively

Use rubrics and real-time feedback to reinforce learning. Below are recommended grading components and workshop agendas.

Grading rubric (100 points)

• Board memo clarity and strategic logic — 25 points
• Quality and realism of financial model — 25 points
• Depth and practicality of risk register — 15 points
• Presentation effectiveness and Q&A handling — 20 points
• Peer evaluation and teamwork — 15 points

Workshop agendas

• Finance workshop (2 hours): Walk through constructing a DCF with scenario branches and teach Monte Carlo simulation basics for revenue volatility. Provide optional Jupyter templates and a low-cost hardware primer (see local lab options) if students want an inexpensive compute environment.
• Risk workshop (1.5 hours): Teach risk scoring (1–5 likelihood, 1–5 impact), controls mapping, and response plans. Include a short cyber tabletop on a hypothetical FedRAMP finding.
• Presentation coaching (1 hour per team): Speed-run Q&A drills and teach how to answer investor due-diligence questions.

Practical tools and data sources for students

Equip learners with accessible tools and datasets so the case focuses on analytical thinking, not software friction.

• Spreadsheets: Google Sheets or Excel (include template model with linked scenario tabs). If budgets are tight, consider guidance on when to replace paid suites with free tools like LibreOffice for the course.
• Basic Python (optional): Jupyter notebooks for Monte Carlo (pandas, numpy) — useful in business analytics courses.
• Risk register template: A simple CSV/Google Sheet with columns for risk, likelihood, impact, owner, mitigation, trigger.
• FedRAMP and procurement resources: Redacted summaries of continuous monitoring requirements and contract vehicles. (Instructors: use public FedRAMP documentation to craft an appendix.)
• Market context: Aggregate government IT spending trends and AI procurement signals from late 2025 and early 2026 — provide students with a short reading list to ground assumptions (see coverage on cloud vendor and procurement events).

Sample analytical exercises

These exercises deepen financial and risk thinking. Provide them as optional problem sets or in-class prompts.

Exercise A — Debt-elimination valuation impact

1. Calculate equity value per share under two capital structures: pre-debt and post-debt elimination. Show how the cost of capital changes with leverage reduction.
2. Discuss how cleaner balance sheets influence winning government contracts that require financial stability assessments.

Exercise B — FedRAMP sensitivity

1. Model a scenario where the acquired FedRAMP platform delivers a 20% revenue lift from government contracts but requires 10% of revenue reinvestment in compliance and continuous monitoring.
2. Run a downside scenario where a regulatory audit imposes two quarters of revenue disruption; assess cash runway and covenant risk.

Exercise C — Monte Carlo for revenue volatility

1. Use Monte Carlo to simulate 1,000 revenue paths given mean growth and volatility inputs. Compute probability of breaching a minimum cash covenant within 12 months.
2. Translate results into a board-level recommendation (e.g., raise a small bridge facility or cut discretionary spend).

Risk analysis framework for government-AI vendors

Teach students a compact, repeatable framework for companies operating at the intersection of AI and government contracting.

1. Regulatory & compliance risk: FedRAMP controls, audit frequency, and certification renewals.
2. Operational risk: Integration complexity, data handling, supply chain dependencies.
3. Cybersecurity risk: Potential breaches, incident response readiness, and cyber insurance gaps. For privacy and data-handling guidance, see privacy checklists.
4. Market risk: Customer concentration and procurement timing.
5. Reputational & ethical risk: Misuse of AI by customers, bias, or privacy-intensive deployments. Consult an ethical & legal playbook when building mitigation measures.

For each category, require teams to propose a measurable control, an owner, and a trigger that escalates to the board.

Advanced module: Policy and ethics (optional)

In 2026, AI policy is a classroom imperative. Add a one-week module where teams map how evolving regulation (domestic & international) could change BigBear.ai’s addressable market and compliance costs.

• Include discussion of emerging US AI directives and international procurement standards that started shaping contracting in late 2024–2025.
• Ask students to prepare an ethics statement for government AI deployments and a short plan for independent verification of model behavior.

Real-world connections and instructor tips

Bring in guest speakers where possible: a Chief Procurement Officer from a government agency, a former FedRAMP auditor, or an investment professional who worked on restructurings. Use recent press coverage from late 2025–early 2026 to make the simulation current. Encourage students to think like stakeholders — investors care about returns and covenants; procurement officers care about security and continuity.

"A cleaned-up balance sheet plus FedRAMP approval is powerful, but it is not a single-path guarantee of growth. Strategy must bridge finance, compliance, and customer diversification."

Assessment: What good answers look like

Here are signals to look for when grading team submissions and presentations.

• Consistent assumptions across the financial model and board memo (no optimistic revenue in memo but conservative model).
• Explicit contingency plans (e.g., trigger points for cost curtailment or bridge financing) with numeric thresholds.
• Risk register that assigns owners, timelines, and measurable KPIs for mitigation actions.
• Clear linkage between FedRAMP capabilities and revenue channels: which contract vehicles, what procurement timing, and how to scale post-win.
• Ethics and compliance considerations that show awareness of AI-specific reputational risks. Use the ethical & legal playbook as a reference.

Extensions: Tailoring the case to different course levels

• Undergraduate: Focus on strategy, SWOT, and a simplified financial model.
• MBA: Full financial modeling, Monte Carlo simulations, and stakeholder negotiation simulations.
• Executive education / professional development: Short, intensive 2-day workshop with a live role-play board session and a deliverable investor memo.

Practical takeaways for learners (actionable advice)

• When analyzing a turnaround, always test at least three scenarios and be explicit about cash-runway implications. Consider lessons from micro-subscription cash planning when modelling runway.
• FedRAMP approval is a market-entry enabler — but factor compliance operating costs and audit risk into your model.
• Debt elimination buys strategic optionality; your recommendation should quantify the most valuable optionality and the cost of forgoing it.
• Use a short, prioritized risk register: if you can only fund three mitigations, which deliver highest risk reduction per dollar?
• Practice clear, investor-ready storytelling: tie numbers to a strategic narrative and summarize trade-offs in one slide.

Future-facing: Why this skillset matters beyond the case

By 2026, employers expect graduates to combine finance, compliance literacy, and AI-savvy judgement. This case trains students to evaluate acquisitions and restructurings in a world where regulation, cybersecurity, and rapid technology change are the norm. These are the decision-making muscles that hiring managers in government-focused contractors, consulting firms, and investment shops will value.

Instructor resources and readings (recommended)

• FedRAMP program documentation and continuous monitoring guidelines (publicly available resources for instructors to summarize).
• Selected late-2025 and early-2026 industry briefings on government AI procurement trends.
• Academic articles and practitioner guides on financial restructuring and covenant analysis.
• Short primers on Monte Carlo simulation for business classes (Jupyter notebooks for advanced students) — include low-cost compute recommendations like a Raspberry Pi lab.

Wrap-up: Classroom outcomes and reflection

After running this case, students should be able to:

• Build a coherent financial model with scenario planning for a company undergoing restructuring.
• Design a compliance-driven go-to-market plan for government AI offerings.
• Assess and prioritize risks with measurable mitigation plans.
• Advocate persuasively to multiple stakeholders under time pressure.

Call to action

Ready to add this case to your syllabus? Download our instructor packet (model templates, risk register, and grading rubric) and adapt the scenario for your course level. If you teach strategy, finance, or tech policy, use BigBear.ai’s 2025–2026 pivot as a living lab to train students in the cross-disciplinary skills employers demand in 2026. Contact us for customized materials or a guest speaker session to bring the case to life. For course tooling advice, see guidance on when to replace paid suites and how to run low-cost Jupyter workflows.

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