- Ansarada’s AI virtual data room, drawing on insights from over 55,000 transactions, has compressed M&A due diligence timelines from months to days by interpreting document content and surfacing critical insights automatically.
- The shift fundamentally alters deal economics by enabling comprehensive review of all contracts, not just samples, at significantly reduced cost and with improved risk identification.
- Leading AI legal platforms, including Luminance and Litera’s Kira Systems, are moving beyond document review toward autonomous contract negotiation and agentic AI workflows, requiring legal teams to prioritise secure, domain-specific AI operating models in 2026.
AI is no longer just organising documents in M&A deals it is reading them, questioning them and flagging risks faster than any legal team could manually. Platforms like Ansarada, drawing on more than 55,000 transactions, are compressing due diligence timelines from months to days. The technology has moved from useful to operationally essential, and legal teams that haven’t adapted are already behind.
The New Speed of Deals: AI Data Rooms Redefine Due Diligence
For decades, virtual data rooms replaced physical document rooms, providing audit trails, permission controls and secure information sharing, but a core constraint persisted: documents still required manual reading, interpretation and cross-referencing. Generative AI is now dismantling that constraint. According to a Bloomberg Law report, generative AI can process entire contract portfolios, extract structure, compare clauses, identify deviations and answer portfolio-level questions at a speed and cost that was previously unthinkable. What was once a nice-to-have has become critical operating infrastructure.
The practical impact extends well beyond speed. Due diligence is no longer bounded by human reading capacity. Instead of locating documents, deal teams can now query what those documents actually say, with every AI-generated response linked back to source material for verification. This enables a comprehensive review of all contracts in a data room, a significant departure from traditional risk-based sampling. According to a McKinsey report, AI-driven review can compress deal cycles and reduce costs meaningfully, though actual results will vary by transaction complexity and tooling. Adoption of generative AI for pre-deal M&A activity is reportedly rising, according to Accenture, though precise figures should be treated with caution given how rapidly the market is moving.
From Keyword Search to Semantic Insight: The Technical Leap in Legal AI
Traditional document review relied on keyword searches and rule-based systems that struggled with the nuance of legal language. The current generation of AI tools uses natural language processing and machine learning models, increasingly combined with generative AI, to move well beyond pattern matching. Litera‘s Kira Systems, for example, uses a hybrid approach combining generative AI with proprietary models trained on over one million contracts and more than 40,000 lawyer-hours, with reported accuracy rates above 90% on standard due diligence provision extraction.
Generative AI extends these capabilities further. These tools can read contract portfolios at scale, structure unstructured data, compare clauses, surface deviations and generate natural-language summaries or answers to complex questions. Every output is traceable to its source. AI can also classify documents, recognise over 100 languages, identify agreement types and flag identically drafted clauses across entire document sets in seconds. The result is a shift from unstructured legal data to structured, actionable intelligence.
Case Studies: Real-World Efficiencies and Cost Compression
The business case for AI in due diligence is no longer theoretical. Gallagher, a global insurance brokerage and risk management firm, has deployed Microsoft AI tools to accelerate its M&A integration process, using AI to interrogate acquisition documents faster and conduct deeper due diligence on newly acquired firms. For a firm where M&A is central to its growth strategy, the ability to rapidly process large volumes of unstructured data, including contracts, policy records and financial documentation, has direct strategic value.
Other documented examples reinforce the pattern. A global pharmaceutical company used AI to review individual clauses and identify contracts requiring consents, amendments or other actions in days rather than months, while working under strict data-governance requirements. A global media company used generative AI to isolate 40,000 relevant agreements from a fragmented contract estate for a litigation claim, extracting key obligations and metadata at a pace and cost that manual methods could not match, fast enough to meet court deadlines.
These cases point to a structural shift in legal operations. The cost of reviewing large document sets is falling sharply, making complete audits across entire repositories economically viable for the first time. Law firms including Jalsovszky in Hungary and Houthoff Buruma in the Netherlands have reported faster transaction work after deploying Luminance’s AI, with lawyers redirected toward higher-value advisory work. Luminance reports a reduction in contract review time of around 60%, while Kira Systems reports time savings of around half on contract review, though both figures come from the vendors themselves and should be assessed accordingly. This rapid evolution in legal AI tooling mirrors broader trends in enterprise agent deployment.
Beyond Extraction: The Emergence of Autonomous Negotiation and Agentic AI
AI’s role in M&A is moving beyond extraction and analysis toward something more consequential: autonomous negotiation. Luminance is developing technology that would allow competing AI systems to negotiate contracts against each other. According to Mergermarket, Luminance CEO Eleanor Lightbody has said the company aims to enable AI on both sides of a negotiation to handle routine discussions, flag risks and surface key deal terms in real time, freeing human users to focus on high-value decisions.
This aligns with a broader shift in legal technology toward agentic AI systems capable of executing structured, multi-step legal tasks under human supervision, rather than simply answering questions. These agents are designed to integrate into existing workflows within email, document editors and collaboration tools, allowing in-house teams to review large contract volumes, track regulatory changes and answer business questions at scale. The operational implications are significant, and the governance challenges are equally so.
Litera’s Kira Systems is expanding its generative AI capabilities along similar lines, with features including natural language querying across review data and custom contextual fields grounded in verified extraction data. The direction across the market is consistent: AI shifting from a passive search tool to an active participant in legal workflows, surfacing context, drafting suggestions and recommending next steps throughout the deal process. For a broader view of how agentic workflows are reshaping enterprise operations, see our coverage of autonomous agent hubs cutting operational costs.
Navigating the Minefield: Risks, Limitations and Ethical Imperatives
The risks accompanying this technology are substantial and should not be minimised. Generative AI hallucinations, where models produce inaccurate or fabricated information, are a documented problem in legal contexts. Researchers had tracked over 120 court cases worldwide involving AI hallucinations by late 2025, underscoring that responsibility for accuracy remains with human professionals regardless of what the AI outputs.
A further risk specific to deals involving AI assets is the common assumption that code defects can be addressed post-closing. Analysis from Quarles & Brady attorneys highlights that the loss or impairment of an AI asset itself can represent a material deal risk, and that traditional monetary risk allocation tools are often inadequate. Buyers are increasingly seeking detailed records of every dataset and fine-tuning weight used in training, treating AI assets as operationally material technology that requires validation before a deal closes.
The M&A Attorney’s Evolving Role: From Document Reviewer to Strategic Architect
AI is not replacing M&A attorneys. It is reallocating their time. Historically, a significant share of a lawyer’s hours during due diligence was spent manually sifting through thousands of contracts to identify critical clauses, risks and anomalies. AI now handles much of that extraction work, freeing attorneys to focus on interpretation, strategy and client counsel.
This means attorneys can move from reactive document review to proactive deal architecture. With AI providing rapid, comprehensive analysis across entire document sets, lawyers gain an earlier and more complete picture of risk. That shifts the value they provide upstream: interpreting the implications of AI-surfaced findings, negotiating complex terms and advising on deal structure rather than cataloguing what documents say.
The shift demands new skills. Attorneys need to understand what AI tools can and cannot do, how to frame effective queries and how to validate outputs before they inform decisions or are shared with clients. The human layer, including supervising AI outputs, ensuring compliance with professional obligations and applying judgment where context matters, remains non-negotiable. The role is less document reviewer and more informed overseer and strategic adviser, with AI handling the volume work that previously consumed most of the available time.
Comparison to Traditional Methods: Speed, Scope and Cost Dynamics
The contrast between AI-powered due diligence and traditional manual review is sharpest across three dimensions: speed, scope and cost. In the traditional model, teams of junior lawyers spent weeks or months reading through data rooms, annotating contracts and producing summaries. The process was slow, error-prone and almost always constrained by budget, leading to risk-based sampling rather than comprehensive review.
AI changes all three at once. Speed is the most immediate gain: review timelines that once stretched to months can now be measured in days, with AI processing large document volumes far faster than any human team. Scope expands because it becomes economically viable to review every contract rather than a representative sample. AI can identify hundreds of contract provisions across dozens of substantive areas and detect subtle clause-level differences a manual reviewer might miss.
On cost, the economics shift structurally rather than incrementally. The marginal cost of reviewing additional documents falls sharply, making complete repository audits affordable where they previously were not. The pharmaceutical company example illustrates this: a comprehensive M&A review completed in days rather than months represents a meaningful reduction in both time and legal spend. The business case for AI-driven due diligence is no longer about finding efficiencies at the margin. It is about replacing a fundamentally more expensive operating model.
Deep Dive: Building a Defensible AI Operating Model in M&A
Deploying individual AI tools is no longer sufficient for legal teams operating in high-stakes M&A environments. The more sophisticated question is how to build an AI operating model that is defensible, one with governance, technical rigour and an auditable evidence trail. This means moving from buying tools to constructing an integrated framework of specialised AI agents that operate continuously across large document volumes with clear accountability at every step.
Data governance sits at the centre of this. Buyers in deals involving significant AI assets are increasingly demanding detailed records covering every dataset used in training, every fine-tuning weight and a verifiable history of model development. Without that lineage, an AI asset may be considered impaired before the deal closes. Legal teams must work alongside data scientists and engineers to establish clear protocols for data collection, annotation, storage and model versioning, ensuring that AI-driven processes can withstand scrutiny in litigation or regulatory review. This connects directly to the due diligence considerations explored in our coverage of AI-accelerated M&A diligence.
Domain-specific models are equally important. General-purpose large language models carry meaningful hallucination risk in legal contexts and lack the specialised grounding that high-stakes work demands. Legal tech providers are increasingly offering models trained on curated legal datasets, including filings, judgments, statutes, discovery records and contract data, that perform with greater precision on specific legal use cases. In-house teams are beginning to build on these focused models to create proprietary AI solutions for particular practice areas. The goal is not just speed, but demonstrable accuracy, which in legal practice is the only standard that matters.
What To Watch: The Next Frontier in AI-Powered M&A
The next 12 to 24 months will determine which platforms and operating models become standard infrastructure for M&A legal teams. Four developments are worth tracking closely.
Integrated workflow platforms with agentic capabilities. Standalone AI tools will give way to platforms embedded directly in lawyers’ existing environments, including email, document editors and collaboration tools, with agentic AI that can interpret intent and execute multi-step legal tasks. Litera’s expansion of Kira’s generative AI features and Luminance’s push toward autonomous negotiation are early signals of where the market is heading.
Mandatory AI governance frameworks. Regulatory pressure, particularly from the EU AI Act’s classification of legal AI as high-risk, will compel firms to adopt formal internal governance policies covering transparency, human oversight and risk management. In M&A deals involving AI assets, verifiable model lineage and data provenance will become standard diligence requirements, not optional extras.
Hyper-specialised legal language models. The market will continue moving away from general-purpose models toward domain-specific AI trained on large, curated legal datasets. Higher accuracy, reduced hallucination risk and deeper understanding of legal nuance will be the competitive differentiators, enabling firms to build proprietary AI solutions matched to their specific practice areas.
Cross-border multilingual capability. As M&A deals increasingly span jurisdictions, the ability to analyse documents across multiple languages without sacrificing accuracy or speed will separate leading platforms from the rest. Reducing dependence on manual translation and cross-border review teams is both an efficiency play and a risk management one. For more analysis on enterprise AI strategy, visit our Enterprise AI section.
