The AI Playbook for M&A Leaders: How to Underwrite AI Risk and Opportunity in Every Deal

To diligence artificial intelligence effectively in mergers and acquisitions, deal teams must abandon isolated technical audits and evaluate targets across two synchronized tracks: an AI risk track that stress-tests business-model durability and margin exposure, and an AI value creation track that quantifies operational expansion and revenue scalability. Treating AI merely as a line item in an IT checklist risks missing fundamental structural shifts in target defensibility.

AI has transitioned from an experimental capability into a decisive factor in corporate strategy. According to research from PwC analyzing the 100 largest corporate M&A transactions in 2025, approximately one-third cited AI as part of their core strategic rationale. In technology-heavy sectors, AI rationale appeared in nearly every major deal announcement. However, the presence of AI in an investment narrative cuts both ways: while it introduces unprecedented opportunities for automation and product differentiation, it simultaneously accelerates the depreciation of legacy software architectures, human-labor-centric service models, and standard transactional fee structures.

For private equity deal captains, corporate development heads, and M&A advisers, the diligence mandate requires answering two interconnected questions before underwriting an acquisition. First, to what extent does generative and agentic AI threaten the target's customer retention, unit economics, and competitive barriers? Second, where can post-close AI transformation credibly compress operational expenditures, enhance gross margins, or unlock net-new commercial offerings? Establishing this dual-track perspective early in the deal lifecycle protects investment committees against multiple compression while identifying concrete levers for EBITDA uplift.

Evaluating AI downside risk requires deal teams to probe beyond superficial product demonstrations and examine how commoditized intelligence alters the target's market position. The primary threat is often not total business failure, but severe pricing compression. In services and software businesses where pricing has historically scaled with headcount or seat counts, generative AI and autonomous workflows drastically reduce the labor hours required to deliver outcomes. If a target's customers can achieve the same deliverables using automated tools or lower-cost challengers, the target faces immediate margin erosion.

A comprehensive industry study by industry analysis projects that AI puts $4.7 trillion in corporate profits at stake through 2035, with 71% of sectors confronting structural transformation. industry analysis's executive research further highlights that the percentage of companies reporting a very high risk of disruption in their industry more than doubled between late 2024 and late 2025, with tech sector respondents leading concerns at 44% reporting high or very high risk. When evaluating targets, deal teams should classify exposure into distinct risk vectors:

• Revenue model substitution: Assessing whether agentic tools allow enterprise buyers to bypass intermediate workflow software or outsourced services entirely.
• Pricing power erosion: Determining whether cost-per-seat models are vulnerable to headcount reductions across the target's end-client base.
• Delivery cost asymmetry: Uncovering whether competitors leverage open-weight models and automated orchestration to deliver equivalent solutions at a fraction of the target's cost structure.
• Foundation model dependency: Identifying targets that function merely as thin application wrappers over third-party APIs without proprietary data moats or domain-specific integration logic.
• Data vulnerability: Investigating whether the target's training datasets lack exclusivity, permissions, or contractual protections against customer opt-outs.

Target defensibility hinges on proprietary data, embedded enterprise integrations, and closed-loop domain feedback. When reviewing targets with marketed AI capabilities, buyers must verify whether the technology represents durable competitive differentiation or fragile, easily replicated interface wrappers AI moat due diligence.

The counterpart to risk underwriting is identifying actionable value creation levers that can be underwritten into the investment thesis. AI value creation is rarely about minor productivity tweaks across back-office functions. Instead, it stems from systemic redesigns of core operating models, accelerating time-to-delivery, reducing cost of goods sold (COGS), and expanding addressable markets through automated service offerings.

Leading organizations are already moving from basic experimentation to substantial operational returns. industry analysis's technology research indicates that enterprise leaders scaling AI across core operational workflows have captured between 10% and 25% EBITDA gains over a two-year deployment horizon. In an M&A context, dealmakers must identify where these efficiencies can be realistically realized within a 3-to-5-year holding period without destabilizing ongoing operations.

Assessing Management Transformation Readiness

Capturing AI-driven upside requires assessing executive capability. Management teams often present ambitious technology roadmaps during management presentations, but deal teams must evaluate whether leadership possesses the technical fluency, organizational discipline, and change-management rigor needed to execute. Key diligence criteria include reviewing historical technology adoption velocity, evaluating the depth of in-house technical leadership, and scrutinizing whether incentive structures align with operational automation. Understanding these organizational dynamics ensures that post-close synergy projections reflect executable strategy rather than speculative enthusiasm, a core principle in structured AI impact due diligence.

To ensure rigorous cross-workstream alignment, deal teams must integrate structured AI inquiry into both commercial due diligence (CDD) and technical due diligence (TDD). The following framework provides an institutional checklist for evaluating risk, uncovering opportunity, specifying primary evidence requests, and identifying critical deal-breaker red flags.

Deal teams conducting thorough diligence workflow automation should systematically map these checklist items against the virtual data room (VDR) to identify document gaps and verify claims against underlying evidence.

AI capabilities introduce distinct legal, intellectual property, and regulatory exposures that conventional corporate M&A due diligence often overlooks. In traditional software acquisitions, tech audits focus on open-source license compliance and code cleanliness. With AI-enabled targets, the primary legal liability shifts toward training data provenance, intellectual property ownership, and compliance with evolving global regulations like the European Union AI Act.

If a target trains its core models on datasets scraped in violation of terms of service, protected by third-party copyrights, or governed by restrictive customer contracts, the business faces significant post-close liabilities. In severe cases, regulatory enforcement or IP litigation can result in mandatory model disgorgement, forcing the company to delete trained model weights, algorithms, and derivative datasets. Deal teams must verify that all training data is supported by traceable, explicit commercial licenses and unambiguous customer consent.

To protect transaction value against uncertain liabilities, acquirers increasingly deploy sophisticated deal structuring mechanisms. According to Skadden's analysis of M&A in the AI era, buyers are using earnouts tied to AI-related metrics, with additional consideration payable only if the target achieves defined performance benchmarks, deployment milestones, revenue thresholds, or compute-efficiency goals, to bridge valuation gaps with sellers. The same analysis notes that buyers may hold back a portion of the purchase price through escrows to mitigate the risk of technical underperformance or data rights issues that surface post-closing, and may negotiate enhanced representations and indemnities with longer survival periods and higher indemnity caps to cover risks involving data rights, model performance, and regulatory compliance.

A critical failure mode in modern M&A occurs when financial and commercial models embed aggressive AI-driven margin expansions without accounting for the true cost of execution. Management teams routinely present hockey-stick margin improvements attributed to AI automation. However, underwriting these assumptions requires rigorous stress-testing against necessary capital expenditure (capex), ongoing operational expenditure (opex), and organizational change-management costs.

Inference economics represent a frequent blind spot in financial modeling. While training costs are typically capitalized or incurred upfront, continuous API query calls, dedicated GPU instances, and cloud infrastructure fees constitute recurring COGS. If a target increases user engagement or introduces real-time multimodal features, inference costs can scale proportionally with usage, directly compressing gross margins unless customer contracts pass through compute expenses.

• Audit infrastructure and compute line items: Scrutinize cloud hosting invoices to calculate the exact inference cost per transaction, active user, or deliverable.
• Model technology obsolescence cycles: Factor in the recurring capital required to retrain, fine-tune, or re-platform internal models as underlying foundation architectures evolve.
• Quantify change-management and enablement budgets: Budget realistic investments for employee upskilling, workflow redesign, workflow integration, and ongoing data engineering.
• Run downside sensitivity scenarios: Model the financial impact if automated efficiency gains take longer to achieve or yield only a fraction of management's targeted savings.

Stress-testing these operating assumptions ensures that deal teams underwrite realistic investment cases grounded in verified unit economics rather than inflated valuation multiples valuation premium due diligence.

The depth of analysis required to underwrite AI risk and opportunity in tight deal timelines has placed unprecedented demands on investment professionals. According to industry research's survey research on gen AI adoption in M&A, 40 percent of respondents using generative AI in their deal activities said it shortened deal cycles by an average of 30 to 50 percent. A broader survey by Deloitte confirms this acceleration, finding that 86 percent of corporate and private equity leaders have integrated generative AI into their dealmaking workflows.

Plausity provides an institutional AI platform built specifically for private equity firms, corporate development teams, and M&A advisers navigating complex due diligence. By deploying Data Room Ingestion, deal teams can connect directly to virtual data rooms and process thousands of contracts, financial spreadsheets, board decks, and technical documentation within minutes. The core AI-Analysis Engine cross-references fragmented disclosures across multiple workstreams to verify data integrity and surface critical anomalies.

To streamline risk evaluation and deliverable generation, Plausity integrates specialized intelligence tools across the deal lifecycle:

• Risk Radar: Continuously scans diligence repositories to identify, evaluate, and categorize findings based on materiality, financial impact, legal exposure, and deal relevance.
• Collaboration Hub: Coordinates multi-workstream advisory teams in real time, aligning commercial, technical, and legal findings within a single unified workspace.
• Report Builder: Synthesizes complex analytical findings to draft structured, audit-ready due diligence deliverables with complete clause-level source traceability.

Deal teams preparing to underwrite AI risk and value creation on their next transaction can request a demo to see these capabilities applied to a live data room.

Plausity is an AI-native due diligence and deal intelligence workspace that helps M&A advisory firms, VC and PE funds, corporate development teams and investment-banking teams structure evidence, findings and questions across a data room. Plausity supports evidence extraction, source grounding, findings management and IC preparation — it does not replace human analysts, advisers or investment professionals, does not provide legal, tax, audit, regulatory or investment advice, and does not make autonomous investment decisions. All findings require human review. Built for today's investment and deal teams. Trusted by >200 firms.

To explore the underlying capabilities, see the Plausity AI analysis engine, the findings and risk intelligence and evidence gap detection product pages, and the IC memo product page. For team-level workflows, see how VC and PE funds and M&A advisory firms use Plausity across live deals, and how AI Impact due diligence and value creation workstreams support the analysis.

1. pwc.com
2. skadden.com
3. deloitte.com