AI in M&A: Enhance Accuracy and Speed in Contract Review

This avoids the “garbage in, garbage out” problem that plagues M&A workflows. AI analysis is only as good as the contract set it analyzes—and proper ingestion is half the battle.

Once the contract universe is cleaned up and centralized, the next challenge is bringing order to the due-diligence workflow itself.

Standardizing the Due Diligence Workflow Before Deep Review

AI doesn’t just analyze contracts—it organizes the due diligence workflow itself. Before legal teams dive into interpretation, AI systems accelerate the front-end tasks that typically consume 30–40% of deal time:

• Rapid contract identification: AI locates supplier, customer, IP, and employment agreements relevant to transaction scope without manual sorting.
• Clause-level comparison across versions: AI compares drafts, amendments, and legacy variations, highlighting where terms deviate from your standard acquisition playbooks.
• Issue clustering: Instead of flagging risks one by one, AI groups similar deviations across hundreds of documents, helping legal teams resolve categories of exposure—not isolated findings.
• Document harmonization: AI identifies patterns of non-standard language and consolidates them into structured outputs, enabling reviewers to work in consistent formats.

This workflow optimization is where organizations see their first measurable ROI: fewer hours spent on administrative due diligence and more time spent on strategic interpretation. Generative AI extends this by creating clean summaries, harmonized clause comparisons, and issue briefs, reducing manual write-ups that traditionally slow down diligence teams.

With the groundwork in place, the real advantage of AI becomes clear: it changes not just the speed of review, but the quality of insight.

The Accuracy Paradox: Why AI Actually Reduces Errors

The counterintuitive insight: AI-assisted review doesn’t just accelerate timelines, it improves accuracy precisely because it removes fatigue-driven error patterns.

Human contract review error rates increase measurably after 90 minutes of focused reading. By hour four, missed-clause discovery rates spike 23%. By hour six, reviewers begin confirming biases (reading what they expect to find rather than what’s written). After eight hours, error detection becomes essentially random.

AI systems don’t fatigue. They also don’t suffer from availability bias, the tendency to weight recent conversations about deal risk more heavily than the actual contract language. An AI system flags every instance where a contract uses undefined terms, inconsistent indemnification triggers, or conflicting effective dates. A human reviewer flags them when they happen to appear in the paragraph they’re actively reading.

In M&A due diligence specifically, AI agents now systematically extract and cross-validate:

• Obligation triggers: Which contractual events require buyer notification, consent, or remediation?
• Financial exposures: Where are payment obligations, indemnification caps, and earnout provisions concentrated?
• Continuity risks: Which contracts contain change-of-control provisions that auto-terminate post-acquisition?
• Compliance conflicts: Which agreements contain regulatory terms that conflict with target company’s current compliance state?

This isn’t replacing human judgment—it’s automating the 40% of review time spent on mechanical extraction and fact-checking, freeing lawyers to focus on interpretation: What do these obligations mean for deal economics? Which conflicting terms require renegotiation?

But speed and accuracy only go so far unless the system can understand what the language actually means.

Semantic Understanding: The Competitive Advantage

The distinction between first-generation contract AI and current systems lies in semantic depth.

Older AI systems operated via keyword matching: flag all instances of “indemnity” and present them. Modern AI agents in contract management understand semantic relationships. GenAI models build on this semantic layer by generating human-readable explanations of those relationships, improving clarity for both legal and financial stakeholders. They recognize that a clause requiring “defense by buyer” AND “control of defense by seller” creates structural conflict even though the words “conflict” or “contradiction” never appear in the contract.

This semantic capability becomes critical when dealing with acquisition contracts involving regulatory complexity. Consider a pharmaceutical acquisition where target company contracts contain exclusivity provisions referencing FDA approval timelines. An AI system can map these provisions against the purchase agreement’s representations about regulatory status, identify gaps in how post-close regulatory changes affect exclusivity obligations, and flag downstream implications for earnout calculations—connections that require understanding medical regulatory language plus contract structure plus financial mechanics.

Humans can do this. But the cognitive load scales exponentially with contract complexity. AI systems handle this scaling linearly.

Semantic interpretation matters because every contractual nuance ultimately rolls up into deal value.

Connecting Contract Findings to Deal Economics

Most M&A mispricing isn’t caused by the business model—it’s caused by contractual obligations that weren’t fully quantified. AI now helps bridge legal findings with financial modeling by:

• Aggregating payment obligations across all target contracts (termination fees, minimum commitments, pricing escalation terms).
• Mapping indemnity caps and financial liabilities, which directly influence balance sheet adjustments.
• Flagging contractual revenue risks, such as customers who can exit on change of control.
• Quantifying exposure windows, helping deal teams understand timing and materiality of risks.

This turns contract review from a legal function into a value-protection function. AI makes contractual risk measurable—and therefore negotiable.

When these risks stay hidden, they show up not in the diligence room but in post-close financials.

Why This Matters for Deal Value Protection

The practical impact: contract-driven deal leakage. Research indicates roughly 9% of post-acquisition value is typically lost to uncovered liabilities in acquisition documentation.

Common M&A Contract Risks AI Identifies Faster Than Manual Review

To improve scannability and accuracy, AI systems now flag high-impact risks across categories such as:

• Termination & renewal risks: auto-termination on change of control, non-renewal windows, or unexpected notice periods.
• Indemnification gaps: mismatched responsibilities across related agreements.
• Compliance misalignments: regulatory clauses that contradict target operations.
• Financial liabilities: payment triggers, fee schedules, earnout dependencies, escalation clauses.
• Operational dependencies: exclusivity provisions, supply chain constraints, IP license restrictions.

Traditional review catches perhaps 70% of these. AI-assisted review, deployed strategically, identifies 92-95% when combined with human legal review.

The economics are straightforward: for a $100M acquisition, that 2-3% accuracy improvement prevents $2-3M in preventable disputes. The cost of advanced AI-driven contract risk management platforms typically runs $30-50K annually for transaction support.

Capturing these gains requires more than deploying a tool—it requires reshaping the review process itself.

Building Your AI-Enabled M&A Process

As GenAI becomes embedded in due-diligence workflows, M&A teams increasingly rely on generative agents for first-draft obligation summaries, redline rationales, and integrated deal briefs.

Implementation doesn’t require replacing your legal team. It requires integrating AI tools into existing workflows:

• Pre-review standardization: Feed AI systems your standard purchase agreement language, then use the system to identify where target company contracts deviate systematically. This creates a ranked priority list for human review.
• Parallel extraction: Rather than serial document review, deploy AI to simultaneously extract key obligations from all 200+ transaction documents. Consolidate findings into standardized obligation registers that your legal team reviews and interprets.
• Conflict mapping: Use AI agents to construct obligation dependency graphs showing which contracts contain mutually dependent terms. This visualizes risk concentration and helps your team prioritize deep dives on highest-impact agreements.
• Continuous flagging: Maintain AI systems through post-close periods to flag when integrated contracts trigger obligations that acquisition documentation didn’t adequately address.

Organizations treating this as a technology implementation fail. Those treating it as a workflow redesign—where AI handles mechanical extraction and humans handle interpretation—typically see measurable ROI within 2-3 transactions.

This is where platform choice starts to matter, because not all AI systems are built to handle M&A-specific complexity.

Where AI-Native CLM Platforms Like Sirion Change the Equation

AI tools produce their best results when they’re built into platforms already responsible for managing obligations, versions, amendments, and post-close performance. AI-native CLM systems such as Sirion bring a structural advantage:

• Models trained on millions of enterprise contracts, not generic language corpora.
• Full semantic extraction across the entire document set, including amendments and exhibits.
• Obligation dependency mapping, giving M&A teams a 360° view of risk propagation.
• Explainable AI outputs, enabling lawyers to audit and validate every flagged issue.
• Post-close monitoring, ensuring risks flagged during diligence continue to be tracked during integration.

This is the layer that turns AI from a point-in-time diligence tool into a continuous value-protection engine.