The impact of AI on M&A transactions: opportunities, risks and emerging market practices

Manuel Santos Vitor

Abreu Advogados, Lisbon

manuel.s.vitor@abreuadvogados.com

Rita Albuquerque

Abreu Advogados, Lisbon

rita.albuquerque@abreuadvogados.com

Introduction

Artificial intelligence (AI) is already reshaping M&A practice at a pace that few anticipated even a couple of years ago.

The integration of AI into the delivery of legal services represents not a passing trend but a structural change: M&A practice stands at the forefront of this transformative context. Across the full M&A deal cycle, AI tools are altering how deals are executed, how quickly and at what cost.

For dealmakers, this transformation presents both significant opportunities and material risks that demand careful consideration. In 1609, Francis Bacon – widely regarded as a founding figure of the scientific method – observed in The Wisdom of the Ancients that the ‘mechanical arts are of ambiguous use, serving as well for hurt as for remedy’. Over four centuries later, this reflection remains strikingly relevant.

As AI assumes an increasingly central role in legal practice, we must weigh the considerable benefits it offers against the risks it introduces, ensuring that appropriate safeguards are in place.

Main opportunities

AI tools are enabling legal advisers to deliver faster, more comprehensive and more cost-effective services from target identification, due diligence and the negotiation phase to post-merger integration.

Deal analytics and target identification

Thinking about the preliminary steps of a M&A deal, AI tools are increasingly valuable in deal sourcing and target identification. By analysing market data, financial metrics and sector trends, AI platforms can identify potential targets meeting predefined strategic, financial and regulatory criteria. In doing so, additional analysis can be focused on the pre-selected targets after exclusion of those that do not match the criteria.

These tools can also benchmark valuations against comparable transactions, model deal structures and assess likely regulatory implication. This enables better informed strategic recommendations at an earlier stage, improving the quality of client advice and the allocation of resources throughout the transaction.

Due diligence

Due diligence is where AI’s impact is most immediately visible. AI-powered document review platforms already process and analyse thousands of documents at a speed and scale impractical through manual review alone.

These tools can flag material contingencies, non-standard provisions, deviations from market standards, inconsistencies and potential irregularities demonstrably faster and with a lower margin of error than manual review. AI-driven platforms can cross-reference provisions across entire data rooms, identifying conflicts or gaps otherwise possibly overlooked or undetected in a conventional human document-by-document review, categorise findings by risk severity and summarise due diligence reports in minutes, enabling deal teams to focus on the most significant issues.

The result is a qualitative, not merely quantitative, advance in the due diligence process: not only quicker turnaround times, but also greater consistency and more comprehensive coverage.

Transaction documents

Drafting and negotiation support is a further area of rapid development. AI tools already assist in producing first drafts of transaction documents (including sale and purchase agreements, shareholder agreements and ancillary contracts), as well as in reviewing and tracking changes across successive drafts.

AI can also assist in identifying negotiation leverage, flagging, for instance, where a counterparty’s proposed position deviates materially from comparable executed transactions, formulating substantive arguments to oppose counterparty positions, drawing on precedent analysis, and comparable transaction terms. AI tools already allow the incorporation of counterparty comments into working drafts and support the preparation of detailed written responses setting out the grounds for non-acceptance of specific proposals or countering them – thereby strengthening the negotiating posture of all of the teams negotiating.

AI can also be helpful in preparing negotiation playbooks and roadmaps used as internal guidelines to manage contract negotiations efficiently.

These efficiency gains can accelerate deal closings and reduce legal costs, but AI also enables a less obvious and potentially transformative opportunity: the rapid preparation of summaries, step-plans, checklists and other client-facing materials that, while highly valued, would traditionally be too time-consuming to produce (and, consequently, would often not be undertaken at all).

By removing these practical constraints, AI allows deal teams to offer a more transparent, structured and responsive service, converting previously uneconomical tasks into a source of genuine competitive differentiation. This opportunity, if harnessed responsibly, has the potential to be truly distinctive in the lawyer-client relationship, creating proximity and reinforcing trust with the real time involvement of the clients at each phase of the deal and transforming legal workflows.

Post-closing integration planning

AI can play a material role in post-merger integration planning, improving efficiency by mapping deal steps, sequencing closing conditions and deliverables, reducing the risk of errors and delays, and automating responses to common queries.

AI-driven platforms can also facilitate the preparation of integration checklists and timelines, enabling deal teams and clients to monitor each workstream in real time and anticipate potential bottlenecks, contributing to a smoother post-completion transition.

Knowledge management

Working as a valuable knowledge management tool within law firms is another significant opportunity. AI tools can compile internal precedent libraries, past transaction documents and legal research to surface relevant expertise quickly, reducing duplication of effort and allowing firms to leverage institutional knowledge otherwise inaccessible in archived files.

Main risks

The opportunities come with risks that lawyers cannot afford to underestimate.

Accuracy and systemic bias

One of the most fundamental risks associated with AI tools relates to accuracy and hallucination. Generative AI systems can produce plausible-sounding but factually incorrect output – including but not limited to mischaracterised contract terms, fabricated regulatory requirements or thresholds, and inaccurate clause extractions. In a transaction context, reliance on unverified AI output constitutes a serious professional liability exposure.

There is also the risk of systemic bias. AI models trained predominantly on historical transaction data from particular markets or sectors and may generate poorly calibrated outputs for transactions in other jurisdictions or industries. A model trained on United States or United Kingdom precedents may produce ‘market standard’ positions that are inappropriate or inapplicable for transactions governed by civil law systems or where a different business culture applies. Practitioners must be alert to this limitation and avoid treating AI-generated benchmarks as universally applicable or ‘carved in stone’ proposals to be implemented.

Is human review becoming useless or being replaced? Right now, the answer appears to be no. Human review, experience, expertise and consideration remains critical. In 1950 Alan Turing wrote in a seminal paper on artificial intelligence that machines cannot think; this concept does not apply to machines. AI tools do not think like humans, but more and more they can act closer to the way a thinker or human acts.

Homogenisation of market behaviour

The widespread adoption of the same or similar AI platforms across the market increases the risk that deal terms, negotiation strategies, valuations and risk assessments will converge, reducing the diversity of approaches among market participants. This may lead to standardised contractual positions, uniform risk assessments that overlook transaction-specific factors and fewer strategic options for clients. In extreme cases, if several acquirers rely on models with the same limitations, they may all underestimate the same type of risk, creating systemic vulnerabilities.

The decline in independent professional judgement may also raise competition concerns, as it reduces differentiation among legal advisers. To mitigate these risks, firms should critically evaluate AI-generated outputs against independent analysis, ensure that deal teams retain autonomy of judgement, and adopt governance rules that prevent excessive reliance on standardised recommendations.

Emerging market practices

The profession is responding. Law firm AI governance frameworks are becoming standard at leading firms, addressing approved tools, mandatory human review of AI output, client disclosure policies, and data handling protocols specific to AI platforms. Firms are also investing in dedicated AI and innovation functions, tasked with evaluating new tools, monitoring regulatory developments and training practitioners in responsible AI use.

Client awareness of AI’s efficiency benefits is also driving fee model evolution, accelerating the shift from hourly billing towards fixed-fee and value-based arrangements. Firms deploying AI responsibly will be better placed to win and retain mandates, and justify fee levels. At the same time, clients and regulators are beginning to ask questions about transparency – specifically, how AI is being used, with what oversight, and to what effect. Firms must be prepared to answer these questions.

Conclusion

The integration of AI into M&A practice is not a future prospect; it is the reality for dealmakers, already reshaping how transactions are sourced, executed and completed. It is much more than a sophisticated spellchecker.

The opportunities are wide-ranging, compelling and of such magnitude that they cannot responsibly be overlooked at any stage of the M&A deal cycle.

The functional and neutral definition of AI adopted by ISO/IEC 22989:2022 – describing AI as ‘a technical and scientific field devoted to the engineered system that generates outputs such as content, forecasts, recommendations or decisions for a given set of human-defined objectives’ – proves particularly apt when mapped against current M&A practice.

We are dealing with tools generating valuable outputs such as:

• contents: generation of first drafts, marked-up versions, summaries, step-plans, playbooks and several other options;
• forecasts: modelling of deal structures and regulatory outcomes;
• recommendations: target identification, valuation benchmarking, and decisions definition of remedies following due diligence findings; and
• decisions: prioritisation of findings by risk severity in due diligence, outlined for human-defined objectives.

The use of AI does not reduce a lawyer’s obligation to ensure accuracy. If humans define the objectives, it is equally the human’s duty to validate the accuracy of the results. It is key – and cannot be overstated – that firms establish robust verification protocols, continuous evaluation methods and human leadership: AI-generated output must be treated strictly as a preliminary starting point for analysis, and never as a finished work product.

Firms and practitioners that embrace AI thoughtfully – with clear governance structures, proper safeguards and a realistic understanding of its limitations – will be better positioned to deliver superior outcomes and to lead in an increasingly competitive market for legal services.

The lawyers who will thrive are those who embrace AI, engage seriously with AI’s capabilities and limitations, and balance the harm and good (in Francis Bacon’s words of wisdom of over four centuries ago, the hurt and the remedy). The time to act is now and the responsibility to do so wisely rests with each of us – and, for the avoidance of doubt, that responsibility cannot be delegated to a chatbot.

References:

Arjun L Bennett, Artificial Intelligence and the Reinvention of Mergers & Acquisitions: Mechanisms, Risks, and Strategic Pathways (2025).

ISO/IEC 22989:2022 – Information technology – Artificial intelligence – Artificial intelligence concepts and terminology (International Organization for Standardization/ International Electrotechnical Commission, 2022).

Francis Bacon, The Wisdom of the Ancients (De Sapientia Veterum), (1609).

PA Emmi, The Impact of Artificial Intelligence on M&A Deals—Part I (2025).