AI Is Changing Mergers and Acquisitions—But Not Without Humans

The Bottom Line

• Artificial intelligence is changing the mergers and acquisitions landscape by giving acquirers a more comprehensive understanding of potential targets.
• Humans are still needed to prompt and interpret the data produced by natural language processers to avoid inherent biases in the programs.
• More efficient and successful deals using AI come at a cost, as companies will be required to comply with new rules and regulations governing such technology.

Identifying suitable acquisition targets is a critical component of successful mergers and acquisitions strategy. In today’s data-rich business environment, artificial intelligence has emerged as a transformative tool that enables companies to identify targets with unprecedented precision and efficiency.

AI technologies can process vast amounts of data, uncover hidden patterns, and predict promising acquisition opportunities that might otherwise be overlooked using traditional methods. This article examines how companies can best leverage AI for acquisition target identification, exploring key technologies, methodologies, and implementation strategies.

AI-Driven Target Discovery

AI has fundamentally transformed how companies identify potential acquisition targets by enabling the analysis of enormous datasets that would be almost impossible to process manually. Traditional deal sourcing relied heavily on manual research, industry expertise, and financial modeling, which limited the scope of potential targets that could be evaluated and thus failed to shine light on all possibilities within the acquisition universe.

AI-driven analytics now allow companies to process vast amounts of structured and unstructured data simultaneously, uncovering hidden opportunities and connections across markets, industries, and geographies.

These AI systems analyze and integrate diverse data sources such as historical M&A transactions, market trends, financial statements, and alternative data sources to predict potential acquisition targets with strong synergies, financial health, and strategic fit.

Alternative data sources such as customer reviews and online forums offer valuable real-time intelligence that traditional financial data might not capture, as they can better account for evolving market conditions, regulatory and technological changes, and shifts in consumer behavior. This comprehensive data analysis provides a significant competitive advantage by identifying promising acquisition candidates before competitors can recognize the opportunity.

Private equity firms, investment banks, and corporate acquirers are increasingly adopting AI to streamline deal origination by leveraging predictive analytics to rank acquisition targets based on predefined parameters such as revenue growth, industry positioning, and innovation potential. AI’s vast data processing capabilities offer such a high degree of efficiency in target identification that companies can invest more time in the most high-potential targets, leading to a higher acquisition success rate and accelerated deal cycles.

Natural Language Processing

Natural Language Processing has emerged as a particularly valuable AI technology for target identification, as it improves the ability to capture qualitative factors that are important to the search. NLP enhances deal sourcing by scanning news articles, financial reports, and social media to detect early signs of a company’s growth potential or distress that might make it an attractive acquisition target.

This technology can identify subtle indicators in text data that human analysts might miss, such as changing sentiment around a company, new product announcements, or emerging partnerships. In other words, corporate development teams that turn to NLP-driven insights can develop more forward-looking M&A strategies, instead of waiting to respond to obvious inflection points.

Coupled with AI-driven analytics of quantitative variables, NLP helps to eliminate common blind spots in the traditional screening process.

Key Predictive Features

Analysis of AI inputs and outputs reveals critical factors that influence successful acquisition target identification. The most significant predictors include revenue growth rate, market cap/EBITDA ratio, and debt to equity ratio.

Machine learning models can also identify patterns in historical acquisition data to determine which characteristics make a company more likely to be acquired. This approach helps identify companies with similar profiles to those that have been successfully acquired in the past, creating a more targeted acquisition strategy based on empirical evidence rather than intuition alone.

Humans Implementing AI

Boston Consulting Group suggests that its AI-driven target search tools can help bring a list of targets numbering in the thousands to a much more manageable range of 50–500. As the search process moves forward, this list is then refined to 20–30 and then 10 through various stages of evaluation and secondary deep dives.

Of course, whittling from 10 potential targets down to five and then to the final one still calls for executive expertise, alongside a healthy dose of human aptitude and preference. Target identification is thus arguably more art than science.

Though the initial steps may rely on large sets of data points with variable factors, as the process moves forward, more nuances arise and the pure logic that AI offers isn’t sufficient. For example, most selection processes assign more value to industry knowledge than to any quantitative metric. And the data points AI systems can identify and extract still benefit from a gut-check for relevance.

While AI can help spot strong target candidates, a human should still confirm their alignment with an acquirer’s value creation objectives. Accordingly, companies should view AI as an enhancement to human judgment rather than a replacement for it.

The most effective approach combines AI-generated insights with traditional fundamental analysis, considering factors such as company ownership structure, management quality, business model sustainability, and industry-specific metrics. This hybrid approach leverages AI’s data processing capabilities while incorporating the contextual understanding and strategic vision that human experts provide.

Specialized AI Platforms

An emerging ecosystem of specialized AI tools is making sophisticated target identification capabilities more accessible to companies of all sizes.

Leading consulting firms have developed platforms that leverage global data sources, cognitive technologies, and transaction experience to scan thousands of metrics across millions of global public and private companies. This approach enables the identification of targets with a high propensity to transact, providing insights ahead of competitors.

Cyndx Finder, an AI-powered deal search and discovery engine, uses a combination of machine learning and NLP to identify companies for deal sourcing. Cyndx and other platforms like it also monitor targets on an ongoing basis to help companies identify the right time to kick off acquisition discussions.

Triggers for these discussions may be contingent on revenue thresholds, product releases, leadership transitions, or competitor acquisitions, among other things. AI tools such as sc0red can even generate visual representations that enable a company to digest the results of a target screening exercise and easily make sense of how shortlisted targets match key priorities.

Deal teams can adjust the visuals to simulate different market conditions and courses of action, giving companies the ability to understand how results can change based on new variables. These tools go beyond providing data to their users; they deliver actionable insights to support timely, strategic decision-making.

Comprehensive Competitive Analysis

Beyond identifying individual targets, AI tools can map complete competitive landscapes to provide strategic context for acquisition decisions.

For example, AI tools can reveal a complete picture of competitors’ offerings and overall funding performance, which helps to map the flow of opportunity in the market and uncover new ways to capture deals. This broader perspective helps companies identify not just individual acquisition targets but entire market segments that may represent strategic opportunities.

Turning to this approach is especially practical when quality targets are scarce or when an industry is undergoing rapid change.

Future Trends, Considerations

As AI is both a nascent and rapidly evolving technology, it’s expected that AI models will continue to fine-tune their approach to soft factors that can be critical to target screening, such as cultural fit and management compatibility. Although NLP offers a good starting place from which to approach such issues, future iterations of AI will likely provide responses that show greater contextual awareness.

Efforts are already underway to improve AI’s understanding of nuances that may be industry- or jurisdiction-specific. And as AI becomes more advanced, models may develop stronger verification mechanisms to vet the credibility and authenticity of available data.

Though AI models’ results and quality still rely on human validation, they are continuously being refined to offer greater accuracy. Future iterations of these technologies are likely to reduce the degree of human oversight required. Companies should monitor for these developments when evaluating how and when to introduce AI as a component in their M&A strategy.

There are also regulatory and ethical questions to consider for companies that rely on AI for M&A target identification. Because the process can involve sensitive or confidential information that may encompass everything from financial data to customer information, AI algorithms must abide by country- and state-specific regulations relating to data privacy and security.

As government concerns about AI intensify, companies should clearly document how an AI model works, how it was trained, what data inputs are leveraged and for which purposes.

Companies should also be prepared to address the potential for algorithmic bias, which can occur when AI systems perpetuate prejudices that exist in the data. This bias can distort assessments of potential targets. For instance, if historical data disproportionately favors companies in the US, the AI may recommend US targets without considering the unique context of a situation where an acquirer should be pursuing cross-border opportunities.

As another example, an AI model that has been trained with only recent data may skew too heavily toward targets with capabilities in newly buzzworthy fields, such as cloud computing or renewable energy. It’s crucial for companies to regularly assess their AI systems to detect and mitigate biases that may be present, ensuring that decision-making remains objective.

The EU AI Act, enacted in March 2024, is one example of how governments are playing an increasing role in AI oversight. The Act introduces stringent guidelines for specific AI algorithms, with penalties for non-compliance reaching as high as 35 million euros or 7% of a company’s global turnover.

Within the US, no state has seen more AI regulation than California, where legislators approved 17 AI-related bills directed across a wide range of industries in the last year alone. Companies must ensure their AI systems comply with these regulations, particularly when they influence significant business decisions such as acquisitions.

Transparency is important not just to satisfy regulatory frameworks. Executives and boards of directors will certainly want to know the analytical basis for an AI tool’s filtering process, how sound the AI is as a reference point, and what mechanisms are behind its recommendations. Because deal stakeholders require clear justification for target selection decisions, a “black box” AI model would likely struggle to gain traction with executives and key decisionmakers. As a result, AI interpretability offers benefits far beyond check-the-box compliance. Models that can provide plain-English reasoning for their recommendations would help drive use among M&A advisers and management teams alike.

While target identification is a critical first step, AI’s value extends throughout the entire M&A lifecycle. Gartner and Deloitte both expect that AI will have a significant effect on M&A processes generally by improving deal teams’ speed, efficiency, and performance.

Forward-thinking companies are developing integrated AI solutions that support target identification as well as due diligence, valuation, negotiation, and post-merger integration.

Companies that develop comprehensive AI capabilities across the full M&A spectrum will be best positioned to create value through acquisitions.

Conclusion

Artificial intelligence has fundamentally transformed how companies identify acquisition targets, enabling more precise, data-driven, and efficient approaches to M&A strategy. By leveraging AI-driven analytics, machine learning models, and NLP, companies can uncover under-the-radar opportunities, predict promising targets, and make more informed acquisition decisions.

The most effective AI implementations for target identification integrate diverse data sources, apply disciplined search parameters, and combine sophisticated technology with human expertise. Leading companies and consulting firms have developed specialized AI platforms that make these capabilities more accessible and powerful.

As AI technology continues to evolve and improve, companies that strategically implement these tools for acquisition target identification in compliance with ethical and regulatory frameworks will gain significant competitive advantages in the M&A landscape. By thoughtfully integrating AI throughout the M&A lifecycle, companies can not only identify better acquisition targets but also create more value from their M&A activities overall.

This article does not necessarily reflect the opinion of Bloomberg Industry Group, Inc., the publisher of Bloomberg Law, Bloomberg Tax, and Bloomberg Government, or its owners.

Author Information

Frank Aquila is partner at Sullivan & Cromwell who has advised deals totaling more than $1 trillion in value.

Catherine Yuh is an associate at Sullivan & Cromwell.

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