Artificial intelligence and M&A

Artificial intelligence (AI) is a transformative technology, and the mergers and acquisitions (M&A) space is no exception. 

Investment bankers have access to several databases using AI and machine learning to extract insights and trends from extensive data set containing information on company financials, M&A activity, and other data such as funding history. Law firms use predictive AI already now in due diligence and drafting of contracts. 

With recent focus on the advancements in generative AI, it is worthwhile to differentiate generative AI from the AI used in predictive analytics and other current generation AI tools.

Generative AI focuses on creating new data or content, such as text, images, or even business ideas, based on patterns it has learned from existing data. Generative AI can for instance assist in generating novel approaches to integrating acquired companies or unearthing potential synergies. Generative AI applications are only coming to M&A market, but the speed of development is fast.

Both approaches have their own strengths and can complement each other in the M&A process. Predictive AI is more focused on analyzing historical data to make informed predictions, while generative AI is more about creating new possibilities and content based on patterns it has learned. Combining predictive and generative AI can provide a more comprehensive view of potential M&A outcomes, helping companies make more informed and innovative decisions. 

Here are some ways in which AI revolutionizes M&A process:

Target Identification and Screening: AI can help analyze vast amounts of data to identify potential M&A targets that align with a company’s strategic goals and criteria. Machine learning algorithms can sift through financial reports, news articles, social media, and other sources to identify potential targets, saving time and effort in the early stages of M&A.

Due Diligence: AI-powered tools can enhance due diligence processes by automating data extraction and analysis. Natural language processing (NLP) algorithms can review contracts, legal documents, and financial statements to identify potential risks or opportunities. This speeds up the due diligence process and improves accuracy.

Valuation and Pricing: AI can assist in valuing companies by analyzing historical financial data, market trends, and industry benchmarks. Predictive modeling can help estimate potential future performance, aiding in determining a fair purchase price.

Negotiation phase: AI can help predict deal outcomes and optimize negotiation strategies based on historical data and market conditions. It can also assist in simulating various scenarios and their potential impact on the merged entity.

Risk Assessment: AI can assess and quantify risks associated with a potential merger or acquisition by analyzing various factors, such as market volatility, regulatory changes, and financial indicators. This helps companies make more informed decisions about whether to proceed with a deal.

Post-Merger Integration: AI can facilitate the integration of two companies by analyzing their systems, processes, and cultures. It can identify areas where efficiencies can be gained, help in resource allocation, and streamline workflows to ensure a smoother integration process.

Regulatory Compliance: AI can assist in identifying potential regulatory hurdles and compliance issues that may arise during an M&A. This proactive approach helps companies address compliance challenges early in the process.

Data Analysis and Visualization: AI-powered analytics tools can process and visualize large datasets, enabling stakeholders to gain insights and make data-driven decisions throughout the M&A lifecycle.

Talent Management: AI can help assess the talent landscape of both companies, identifying key personnel and potential overlaps. This aids in retaining critical employees and optimizing the workforce post-acquisition.

Market Insights and Competitive Analysis: AI can provide insights into market trends, customer behavior, and competitive landscapes, helping companies understand the potential impact of an M&A on their industry position.

While AI is on the cusp of disrupting the tried-and-true M&A playbook, users of AI should also be cognizant of potential risks.

I recently used ChatGPT in target company search to find out leading industry players in a narrow field in certain country. The AI was “hallucinating” and came up with a short-list of five companies with very credible looking business descriptions – three out of five were product of pure AI hallucination.  

Hallucination can occur when an artificial intelligence system, often a machine learning model like a neural network, generates information or data that is not based on real input but rather reflects patterns it has learned during training. 

There is another, real-life example where a licensed lawyer in New York used ChatGPT to conduct research and did not confirm the validity of the cases ChatGPT quoted. The citing at least six fictitious cases in a filed brief, which were hallucinated by generative AI, led to a sanction hearing for counsel.

Conclusions

AI offers organizations the ability to make informed decisions based on data-driven insights, potentially leading to improved efficiency, reduced risks, and better outcomes. Hence. M&A processes of future will be faster and less risky. Transaction costs will be lower too, and the customers are likely to reap most benefits. 

AI may have the ability to transform the way M&A is conducted, but does it change the competitive landscape of various service providers to the industry? Could AI accelerate global consolidation of law firms or investment banks, as only the largest players can afford to develop AI systems in-house? I think not. 

A race is ongoing to develop novel AI solutions for global M&A industry worth more than USD 3 trillion on annual basis. All the previous battles for M&A data base supremacy have been won by independent software providers with flying colors, which have an interest to maximize their own revenue by keeping the long tail alive by providing affordable solutions for all of their customers.

While AI offers numerous benefits to the M&A process, it’s important to note that human expertise and judgment will remain crucial also in the future. Professional, agile, and client-centric corporate finance firms are likely to flourish in the foreseeable future too.