From identifying potential opportunities to forecasting company metrics and revenue, AI has the potential for unparalleled impact in the M&A process. While traditionally, AI facilitated data analysis and risk mitigation, the emergence of recent technology – specifically large language models – has exponentially expanded its impact. These models can process vast amounts of textual data, enabling a nuanced understanding of market dynamics, potential M&A opportunities, and competitive landscapes.
And that’s just the beginning.
AI impacts M&A across all stages, from strategy formation to sourcing and pipeline management. Here’s what we’re seeing and what investors and dealmakers need to keep an eye on going forward.
#1. Integrating large language models
Using large language models (LLMs), firms can better understand emerging market conditions and competitive dynamics, identifying M&A, partnership, and organic opportunities with precision, efficiency, and speed – a key competitive advantage.
In deep neural networks, LLMs can process information based on billions or even hundreds of billions of parameters, trained by going through countless inputs while attempting to complete key tasks – predicting the next sentence, for example, or responding to a question. Given this “training,” LLMs can instantly interpret contextual relationships, providing insights, analysis, and recommendations. Current models have an 85%-90% accuracy.
#2. Analyzing deal terms
AI can also be used to analyze deal terms. This is key during active negotiations or auction processes, which may hinge on speed to action. Dealmakers can rapidly analyze positions using predictive AI to understand proposed terms’ impact on a specific opportunity. A good example: ChatGPT could model the impact of several earn-out scenarios on both shareholders and employees. Deal professionals could use this model to spot trends and patterns, forecast financial performance, and run various scenarios to mimic market shifts.
#3. Keeping an eye on what comes next
Predictive capabilities are among the most transformative AI applications in M&A. AI can be used to predict company metrics, deal outcomes, and ideal timing for M&A processes. It can also enable continuous monitoring of target companies, providing real-time insights that inform strategic decision-making.
AI’s predictive capabilities can also be leveraged in creating synergy plans. With a digital twin of the target’s operations, AI can simulate and assess potential integration scenarios, offering insights that would be otherwise inaccessible. The digital twin becomes a valuable tool for integration planning.
The benefits of a digital twin extend to the organization and navigation of due diligence information. It offers a clear, comprehensive, and accessible view of the target’s operations, contributing to an informed and strategic M&A decision-making process.
#4. Streamlining communications and collateral
AI also plays a crucial role in outreach and communications. It can craft personalized outreach messages and draft emails that resonate with potential acquisition targets, ensuring effective communication.
AI can also be instrumental in business case development and value creation planning. It aids in formulating the business case for acquisition, identifying opportunities, and assessing the strategic fit. AI tools, for example, may be used to draft deal summary decks and post-merger integration project plans, minimizing human effort and maximizing productivity.
Understanding the drawbacks of AI in M&A
Granted, AI isn’t without its challenges. Machine learning models are based on statistical probability and rely on significant amounts of data. Predictions, insights, and content are based solely on inputs, meaning bias and interpretation can be an issue. This is less the case with numerical inputs, but research, textual insights, and analysis may not be as reliable or actionable.
Beyond that, AI models don’t have the same ability to understand or interpret questions as humans. There’s no tacit knowledge or prior experience level layered into understanding or responses – again, all outputs are based on immediate inputs. This can challenge very nuanced questions, requests, considerations, or more subjective asks.
Integrating AI in M&A
AI is moving from strategic advantage to critical necessity in the age of serial acquisitions. With AI’s transformative capabilities, businesses can navigate the complexities of M&A, transforming challenges into opportunities for sustained growth and success.
Leveraging AI in M&A represents a massive leap forward. From enhancing market understanding to facilitating efficient data management and driving predictive capabilities, AI is setting new standards for M&A strategies. As competition intensifies in the acquisition market, businesses that leverage AI’s immense potential stand to gain the upper hand, unlocking unprecedented growth opportunities.
