Case Study Exercise: Analyzing Risk After an AI Platform Acquisition

Hook: Why this case matters to students, instructors, and decision-makers

Executives, investors, and classroom learners face a common pain point: how to judge whether an AI M&A deal is a leap forward or a hidden liability. BigBear.ai eliminated debt and acquired a FedRAMP-approved AI platform in late 2025. That combination can restore confidence — but falling revenue and concentrated government exposure make the deal a strategic high-wire act. This case study exercise is built to teach rigorous risk analysis and revenue forecasting after an AI acquisition, using a real-world inspired scenario for business-school discussion, classroom assessment, and executive rehearsal.

Executive summary for fast readers

BigBear.ai stands at a strategic inflection point. The company removed legacy debt and bought a FedRAMP-approved platform that could unlock federal sales and premium margins. Yet revenue trends show declines across legacy services, and government contract concentration increases political and programmatic risk. In 2026, with faster government AI adoption and tighter compliance scrutiny, the right strategic play requires three things: disciplined scenario modeling, quantified risk-adjusted forecasts, and a short list of operational KPIs to de-risk execution.

Learning objectives

• Apply a structured framework for M&A risk assessment in the context of AI and government contracting.
• Build scenario-based revenue forecasts that reflect integration, FedRAMP-driven wins, and downside concentration risk.
• Estimate risk-adjusted valuations and recommend mitigation priorities for executives and investors.

Case materials: facts, timeline, and baseline assumptions

Present the following materials to learners. These numbers are simplified for teaching; instructors can adapt to live filings and market data.

Timeline (compact)

• Q3 2025: BigBear.ai completes debt elimination plan.
• Q4 2025: Acquisition announced of a FedRAMP-approved AI platform that supports secure government workloads.
• Late 2025 / Early 2026: Government agencies publish updated AI acquisition guidance and increase funding for operational AI pilots.

Baseline financials and deal assumptions (USD millions)

• Trailing 12-month revenue: 80
• Revenue trend: -8% YoY (legacy services declining)
• Revenue split: Government 60, Commercial 20
• Acquisition headline price: 40 (assumed for exercise)
• Integration and implementation cost (one-time): 10
• Incremental annual opex to maintain FedRAMP compliance: 4
• Management forecast for platform-driven revenue uplift: +15% incremental compound per year (optimistic)
• Discount rate for valuation exercises: 12% (reflects small-cap / execution risk)

Analytic framework: how to structure the M&A risk and revenue analysis

Use a layered approach combining qualitative assessment and quantitative scenario modeling. The core steps below are classroom-ready and executive-ready.

1. Stakeholder map and dependency analysis: Identify top customers, contract durations, single points of failure, and margin sensitivity.
2. Risk taxonomy: Classify strategic, operational, regulatory, financial, and reputational risks tied to government concentration and FedRAMP obligations.
3. Scenario revenue modeling: Build Best / Base / Worst cases with explicit assumptions for contract wins, churn, and pricing.
4. Valuation and decision metrics: Use NPV, payback period, and risk-adjusted expected value to decide whether the acquisition meets strategic thresholds.
5. Mitigation plan: Specify actions, owners, timelines, and KPIs to reduce probability and impact of the top 3 risks.

Why FedRAMP changes the calculus in 2026

In 2026 the FedRAMP ecosystem is more consequential. Agencies accelerated AI procurement pilots in late 2024 through 2025 and have become selective about security posture and vendor reliability. Owning a FedRAMP-approved platform is a competitive advantage, but it also creates recurring compliance costs and larger contract scrutiny. Any case study must weigh the market-access premium against higher operational demands and political risk.

Strategic risks to analyze (with classroom probabilities)

Provide students with probability-impact estimates. Encourage debate; numbers are hypotheses to test by research.

• Contract concentration: 0.60 probability of a major customer churn event within 24 months; impact: -25 revenue (high).
• Integration failure: 0.30 probability that integration timelines slip by 12 months; impact: -15 revenue and +6 cost (medium-high).
• Regulatory/compliance escalation: 0.20 probability of a FedRAMP audit finding requiring remediation spending; impact: +8 cost, potential reputational delay (medium).
• Commercial market adoption shortfall: 0.40 probability that commercial sales fail to scale beyond niche pilots; impact: -10 revenue (medium).
• Macro / budget risk: 0.25 probability of federal budget cuts to non-essential AI programs within two years; impact: -12 revenue (medium).

Use expected value calculations to prioritize mitigation. For example, expected revenue loss from contract concentration = probability 0.60 x impact 25 = expected loss 15. That single risk could consume most of the acquisition upside and should be a top mitigation target.

Revenue trends and scenario modeling

Provide a 3-year projection in three scenarios. These numbers are classroom inputs to teach sensitivity and portfolio thinking.

Scenario assumptions (annualized)

• Base case: legacy decline stabilizes to -2% as integration offsets churn; platform contributes +10% incremental revenue in year 1 ramping to +25% by year 3.
• Best case: legacy stabilizes, platform adoption accelerates to +40% incremental by year 3 with cross-sell into commercial market.
• Worst case: legacy decline continues at -8%, platform sales lag; net revenue falls further.

Sample 3-year revenue table (USD millions)

• Year 0 baseline: 80
• Base Year 1: 80 x 0.98 + (80 x 0.10) = ~86 (stabilize + platform ramp)
• Base Year 2: 86 x 0.99 + (86 x 0.15) = ~100
• Base Year 3: 100 x 0.99 + (100 x 0.25) = ~124

Contrast with Worst case: Year 1: 80 x 0.92 = 73.6 (platform fails to add meaningful growth). By Year 3 revenue could be ~65. Best case Year 3 revenue could exceed 150. Teach students to compute CAGR and the sensitivity of valuation to these outcomes.

Valuation and decision thresholds

Compute NPV of incremental free cash flows attributable to the acquisition under each scenario. Use a simplified approach: incremental EBITDA margin contribution, capex for integration, and incremental opex for FedRAMP maintenance.

• Assume incremental EBITDA margin on platform revenue: 30%
• Integration capex: 10 (year 0)
• Incremental opex: 4 annually

Example Base-case NPV (simplified): incremental EBITDA Year 1 = (platform revenue uplift 8 x 0.30) = 2.4. Subtract opex 4 = -1.6 (loss in year 1). Growth improves in years 2-3. Discount at 12% and sum to estimate whether NPV exceeds the headline price of 40. In many plausible base cases the acquisition breaks even only if Year 3 platform traction materializes and contract concentration is mitigated.

Classroom assessment: tasks and scoring

Use this as a graded case assignment. Time: 2-3 hours for students; 30-45 minutes for presentations.

Assignment (individual or team)

1. Produce a two-page executive memo to the Board: buy, hold, or divest recommendation and three prioritized mitigation actions.
2. Construct a 3-year revenue forecast under Best / Base / Worst scenarios with explicit assumptions and sensitivity analysis.
3. Quantify three highest-impact risks using expected value and recommend owners and KPIs for each mitigation.
4. Calculate simplified NPV for acquisition under Base case; include break-even sensitivity on platform growth and contract churn probabilities.

Grading rubric (100 points)

• Executive memo clarity and recommendation: 20 points
• Revenue model and scenario logic: 30 points
• Risk quantification and mitigation plan: 25 points
• Valuation calculation and sensitivity: 15 points
• Presentation and defense (if team): 10 points

Instructor notes and example answers

High-performing student answers will do three things: anchor forecasts in measurable drivers (pipeline, win rates, contract lengths), demonstrate trade-offs between market access and compliance cost, and provide a defendable recommendation based on risk-adjusted value, not wishful thinking.

Example Board memo (summary)

Recommendation: Hold and conditional invest. Rationale: The FedRAMP platform materially improves federal addressable market and margins if platform traction reaches 20-25% of legacy revenue within 24 months. However, current revenue decline and contract concentration present tail risk. We recommend a 12-month staged integration budget with four gating KPIs: 1) top-3 pipeline conversion rate for federal pilots, 2) retention of top two government customers, 3) time-to-FedRAMP re-certification milestones, and 4) commercial pipeline contribution target.

Sample mitigation plan

• Protect top customer relationships: assign dedicated account teams and guarantee service-level continuity. KPI: zero loss of top-2 accounts for 12 months.
• Staged go-to-market: focus federal pilots with immediate low-risk deployments, delaying large bespoke integrations. KPI: conversion rate 25% on pilot to contract within 9 months.
• Cost containment for FedRAMP: automate compliance monitoring and shift to predictable subscription-based controls. KPI: keep incremental FedRAMP opex below 4 per year post-integration.

Practical, actionable advice for executives and investors

• Insist on gated milestones for integration spending. Convert a lump-sum acquisition expense into staged funding tied to contract wins and technical milestones.
• Hedge concentration risk by cross-selling to non-federal agencies and commercial verticals using the FedRAMP certification as a credibility lever.
• Measure early — set three-month sprint KPIs for pilots so management can pivot before full-scale integration.
• Insure execution — consider third-party warranty or an earnout structure to align seller incentives with post-close performance.
• Stress-test budgets for compliance: FedRAMP recertifications and audits can generate one-time shocks; plan a 10-20% contingency in year 1.

2026 trends and future predictions relevant to this case

Late 2025 and early 2026 saw increased federal AI pilot budgets, more rigorous agency procurement guidelines, and a maturing market for FedRAMP-certified offerings. Expect the following in 2026:

• Higher value for FedRAMP-approved platforms but also higher operational expectations for demonstrable security and explainability.
• Shorter buying cycles for proven AI pilots, but longer evaluation periods for mission-critical deployments as agencies emphasize assurance.
• Greater investor scrutiny on AI M&A outcomes, with attention to post-close KPIs rather than headline approvals.
• New AI-specific contracting vehicles and modular purchase agreements that favor vendors offering standard interfaces and compliance automation.

For BigBear.ai-like companies, 2026 will reward disciplined operators who treat FedRAMP as a platform for growth, not just a marketing credential.

Teaching note: this case is not about whether FedRAMP is valuable. It is about whether management can convert certification into sustainable revenue while managing government concentration and integration risk.

Benchmarks and quick metrics to track post-acquisition

• Pipeline conversion rate for federal pilots (target >20% within 9 months)
• Top-3 customer revenue retention (target 100% for 12 months)
• Platform gross margin (target >40% after scale)
• Time-to-contract for pilot converts (target <9 months)
• Compliance cost as percentage of platform revenue (target <5%)

Wrap-up: how to use this case in class or the boardroom

As a business-school case study and assessment, this exercise blends qualitative judgment and quantitative rigor. Use it to teach M&A due diligence, scenario modeling, and stakeholder-aligned execution planning. For executives and investors, the same tools provide a repeatable playbook: quantify your most material risks, build gated investments, and measure progress with operational KPIs.

Key takeaway: a FedRAMP-approved acquisition offers tangible strategic upside in 2026, but that upside is conditional. Robust risk analysis and disciplined revenue trend modeling are the difference between an accretive acquisition and a costly distraction.

Call to action

Ready to run this case in your classroom or executive workshop? Download the instructor slide deck, editable financial model, and grading rubric from our case kit. Or contact us for a customized executive simulation that runs this scenario with live market data and investor panels.

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