Transforming M&A: The Role of Generative AI

Generative AI in M&A Deals

About one in five surveyed companies currently uses Generative AI in Mergers and Acquisitions (M&A) processes, and over half expect to integrate it into their dealmaking soon. Early adopters report that expanding generative AI tools across more M&A stages multiplies the benefits. Traditionally, M&As are strategies companies use for growth. Merging with another company in the same sector can boost market share and revenue, while acquisitions can involve purchasing a business in a different industry.

Generative AI is poised to become a permanent fixture in M&A. Its potential is vast, with a profound impact expected on dealmaking. Companies that master generative AI in M&A over the next five years are likely to identify targets faster, underwrite deal value with increased confidence, conduct diligence and integration more efficiently, and ultimately achieve higher shareholder returns.

Notably, in the latest survey, 36% of the most active acquirers use generative AI for M&A. This matters because long-term M&A studies show that companies making regular deals consistently outperform less active firms in total shareholder returns. Private equity is also an enthusiastic early adopter, with over 60% of surveyed firms leveraging at least one generative AI tool to improve sourcing, screening, or diligence.

Beyond accelerating sourcing, screening, and diligence, early adopters are now experimenting with generative AI for integration, divestiture planning, and program management.

How Generative AI is gaining traction in M&A deals

According to recent surveys, nearly 80% of companies leveraging Generative AI in their M&A processes report reduced manual effort. As a result, employees spend less time on the M&A program and can more easily balance demands from the core business. Key business cases for incorporating Gen AI into M&A deals include.

Advancing the Screening / Sourcing Process

While traditional AI has already proven effective in deal scanning, Gen AI enhances this by analyzing broader sets of both structured and unstructured data, synthesizing results for quantitative and qualitative insights, and highlighting key elements such as strategic, financial, and cultural fit of potential targets. This enables companies to identify and pursue targets that might otherwise go unnoticed.

Expediting Diligence and Negotiations

Generative AI accelerates the diligence and negotiation stages by summarising key documents, surfacing risks, drafting initial memoranda tailored to deal with specifics, sourcing relevant statutes and regulations, and pinpointing useful case law to ease negotiation friction. It can also generate highly accurate first drafts for deal announcements and regulatory filings. Collectively, these capabilities can significantly reduce the time required for various legal tasks associated with deal negotiations, signing, and closing.

Executing Integration or Separation Effectively

Experienced dealmakers understand the importance of quickly capturing deal synergies; delays can lead to missed value. Organizations engaged in significant M&A events are particularly prone to distractions that may result in a decline in organic momentum and an average decrease in excess revenue growth compared to peers.

Enabling Generative AI in M&A deals

74% percent of C-suite executives anticipate increasing their investments in data and generative AI technologies over the next decade, aiming to drive business transformation.

Task Standardization

Standardised tasks that are ideal for automation include designing Transition Service Agreements (TSAs) and integrating systems and infrastructures. Creative tasks benefiting from data augmentation include designing operating models, producing deal communications, and facilitating post-merger performance assessments.

Early adopters are using generative AI to draft integration workplans and TSAs in less than 20% of the time previously required, enabling faster team mobilization with better quality information

Generative AI can enhance internal capabilities by leveraging proprietary data from prior deals to assess performance patterns and identify new opportunities. For instance, AI can evaluate an acquisition portfolio and measure the impact of individual deals.

AI can efficiently analyse large volumes of data, such as reviewing material contracts and identifying deviations from model contracts, saving time and focusing attention on critical issues.

Value Creation

Generative AI tools allow access to specific company data to size realistic cost and revenue synergies and design value creation plans informed by historical acquisition performance.

A key use case is a generative AI “coach” trained on M&A best practices and the organisation’s unique playbook, providing rapid, insightful answers to integration and separation teams

Companies using generative AI can improve due diligence and value realization by identifying cost and revenue synergies and refining plans to achieve them. Tailored AI tools can leverage sales, pricing, customer relations, and catalog data to prioritize cross-selling targets and accelerate post-close value capture.

Leading research organizations recommend applying AI within internal M&A processes first by developing and testing diverse use cases. In particular, contract analytics is an impactful application for using AI right now to improve activities tied to letter of intent (LOI) negotiation, contract due diligence, definitive agreement and TSA negotiation, and contract renewals and integration.

Generative AI reduces manual efforts in M&A, allowing employees to focus on core business priorities. Nearly 80% of businesses report reduced manual work and improved efficiency and retention when using AI in their M&A processes.

Productivity and Efficiency

Generative AI-powered tools automate and accelerate a wide array of time- and resource-intensive tasks. Innovations like virtual data rooms, cloud-based integration tools, and advanced analytics have transformed due diligence, collaboration, risk assessment, and decision-making, resulting in smoother business integrations and improved deal outcomes.

One user described utilizing third-party tools for managing data rooms, including automated filing, advanced search, and streamlined Q&A processes.

Among surveyed companies, 78% report productivity gains from reduced manual effort, 54% saw faster timelines, 42% realized cost savings and better focus, and 85% of early users stated that AI met or exceeded expectations

Together, these post-deal applications of generative AI represent a significant and relatively untapped opportunity for dealmakers aiming to comprehensively reinvent their companies’ M&A functions. By developing and implementing a formal strategy, executives can simultaneously address related priorities, such as talent, technology maturity, and business value validation.

Adoption of Generative AI in M&A deals

Adoption of generative AI in M&A deal processes is currently low at 16%, but this is projected to rise to 80% over the next three years. Notably, 85% of current users report that generative AI meets or exceeds their expectations. Senior leaders should begin by honestly assessing their current M&A capabilities and identifying where technology can meaningfully improve their M&A processes.

Start Adopting Now

New technologies require testing, learning, and time to build expertise, identify valuable use cases, and drive user buy-in. Companies can’t simply purchase a generative AI–enabled solution later and expect to catch up with early adopters, as the learning curve will be steeper. While mastery will take time, businesses can start reducing manual process time within the first week of implementation. Prioritize generative AI use cases with the highest impact. For M&A strategies targeting many small acquisitions, generative AI will add the most value in opportunity scanning and assessment.

Build an AI Portfolio

A basic generative AI tool might be as simple as a well-designed prompt drawing upon high-quality data sources. At a minimum, companies can make substantial progress by securing an enterprise license and using these tools. When bringing prioritized use cases to life, carefully evaluate whether to build in-house or adopt off-the-shelf solutions. Recently, more turnkey solutions have entered the market and additional options are expected in the next one to two years.

Innovate with Purpose

Leading companies will move beyond simple automation, reimagining their end-to-end M&A processes to fully leverage generative AI. They’ll strategically invest in areas offering competitive advantages. As with any build-or-buy decision, leaders should weigh their team’s expertise, investment size, potential returns, and actions of peers.

Evolve the M&A Team

Generative AI is set to assume more of the time-intensive project management. This prompts a reevaluation of future skill needs for M&A. The most successful deals are delivered by professionals focused on strategic value rather than process management. To stay ahead, companies should revisit their talent strategies, aiming to build and sustain long-term value through generative AI.

Managing Risks in M&A deals

Mergers and acquisitions (M&A) can offer substantial benefits to both parties involved, but they may also present challenges, particularly when regulatory issues or cultural clashes arise. As more companies leverage advancements like generative AI, late adopters may struggle in three key areas:

Ensuring proper guardrails are in place. Generative AI differs from many existing technologies in that it amplifies certain risks, such as increased potential for security breaches due to its accessibility, reputational risks from quality control failures, and intellectual property infringement. Legal and regulatory environments are evolving rapidly, even as generative AI progresses.

Increased reliance on advanced AI models can raise the risk of human disengagement, potentially allowing critical issues to go unnoticed. It’s crucial for organizations to keep humans involved, work proactively with legal and technology teams to identify and mitigate risks, and uphold strong ethical standards.

Generative AI can drive significant process efficiencies in M&A, but it also brings risks such as data inaccuracies, privacy concerns, and cybersecurity threats

Generative AI accelerates data summarisation, reducing the time from a week to a day and enabling teams to focus on deeper analysis. This allows firms to make better-informed bids and quickly identify promising opportunities—or know when to walk away.

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