Gen AI in M&A: From theory to practice to high performance
Top line
Generative AI is revolutionizing M&A by enabling faster, more cost-effective deal identification, diligence, and integration, with proactive adoption and expertise development being key for future success.
Summary
Generative AI (Gen AI) is rapidly transitioning from theoretical potential to practical application within Mergers & Acquisitions (M&A), offering significant advantages in cost reduction and deal acceleration. While global Gen AI spending is projected to reach $644 billion in 2025, current adoption by M&A practitioners is moderate, largely due to a lack of expertise and reliance on off-the-shelf chatbots. Gen AI tools are actively being used to enhance target identification, streamline due diligence by processing vast amounts of data and extracting insights, and automate aspects of integration planning and execution. Experts predict increasingly sophisticated end-to-end M&A tools within the next two to five years. To capitalize on this wave of innovation, M&A teams are advised to proactively assess their processes, build AI fluency, secure leadership support, formalize their strategies, and develop clear roadmaps for Gen AI integration, thereby positioning themselves to capture future value.
Highlights
- Worldwide Generative AI (Gen AI) spending is forecast by Gartner to reach $644 billion in 2025.
- In a survey, M&A practitioners using Gen AI reported an average cost reduction of approximately 20 percent.
- Forty percent of survey respondents stated that Gen AI enabled 30 to 50 percent faster deal cycles.
- 42 percent of all respondents believe Gen AI has the potential to transform or bring highly differentiating capabilities to the deal process.
- Despite excitement and reported results, only 30 percent of respondents engage with Gen AI at moderate to high levels.
- The majority of current Gen AI users rely on commercially available chatbots rather than customized, proprietary tools.
- A lack of expertise is identified as the primary challenge for AI adoption across industries and company sizes.
- The article advises M&A teams to proactively engage with existing Gen AI tools and understand their evolution, rather than adopting a 'wait and see' approach.
- Gen AI tools are currently used to identify M&A targets, accelerate diligence, and augment integration planning and execution.
- In target identification, Gen AI-enabled tools combine Large Language Models (LLMs) with machine learning algorithms trained on deal history, using semantic search to cluster and score potential targets.
- A business software company used an AI-powered platform to identify and score over 500 potential targets in less than a day, leading to three acquisitions.
- Future Gen AI tools for target identification are expected to act as strategic partners, analyzing company strategy and identifying top M&A opportunities within two to five years.
- For due diligence, Gen AI tools can search interview transcripts, access virtual data rooms, summarize diligence files, analyze financials, and enrich findings with sentiment data.
- Within two years, Gen AI tools are expected to improve diligence into a continuous, connected part of the deal cycle, feeding insights into screening and integration.
- Gen AI agents can currently automate some integration tasks, producing day-one readiness and integration plans, and generating communication materials, though they require human oversight.
- In two to three years, Gen AI tools are anticipated to automate over half of all integration-related tasks.
- Key steps for M&A teams to adapt include assessing current processes, building AI fluency, securing executive sponsorship, formalizing M&A playbooks, and developing a one- to two-year roadmap for Gen AI integration.
- Gen AI is moving beyond theoretical applications into practical use in M&A, driving efficiency, speed, and innovation in deal-making.
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