How Artificial Intelligence is Disrupting the Dealmaking Process
Louis Lehot and Eric Chow discuss how artificial intelligence and machine learning are affecting the mergers and acquisitions process and what issues may arise with the use of this emerging technology.
The use of artificial intelligence (AI) and machine learning (ML) technologies has exploded across the board. According to EY’s July 2023 CEO Outlook Pulse, almost half of the CEOs who participated are focusing capital allocation on these technologies, with 43% responding they have already fully integrated AI-driven product or service changes into their capital allocation process and are actively investing in AI-driven innovation.
As more businesses adopt these technologies, it has become clear that there are countless business applications for AI and ML. This includes their ability to transform the merger and acquisition process.
Negotiating a deal and running due diligence in a merger or acquisition has always been complex, time-consuming, and resource intensive. However, AI and ML are changing the game and making the process much more efficient on many levels. These transformative technologies can streamline deal processes with their ability to better analyze data and improve outcomes, particularly regarding due diligence and contract analysis.
In any transaction, participants exchange long lists of questions, requests, and spreadsheets demanding documents, data, and information, generally referred to as due diligence request lists or DDRLs. This has traditionally been a cumbersome process, involving mass amounts of digital and paper files and teams of bankers, lawyers, accountants, and analysts who must review the documents, glean information, negotiate agreements, produce disclosure schedules, and allocate risks and rewards.
This largely human and manual process of digesting the information could be more efficient, as well as the source of many budgets exceeded, details missed, questions never asked, and opportunities for synergies lost.
However, with the introduction of AI in the dealmaking landscape, the due diligence process is becoming more efficient as it is able to analyze what can be overwhelming amounts of data in a fraction of the time.
Tools like Robotic Process Automation (RPA) can provide tremendous potential for time and cost savings as they automate data analysis and can quickly flag anything that would require further review. AI tools also provide predictive modeling tools, allowing companies to examine various scenarios and what kinds of risks might be involved.
Companies such as Deloitte are launching products to assist with the due diligence process. Their iDeal product uses AI and ML to organize and tag the huge amounts of data involved. It can also learn from corrections made by humans, making it more accurate the more it is used.
Numerous companies are also launching AI products designed to improve the contract review process. Take, for example, Kira Systems, a machine learning contract and document review software that can scan and analyze vast amounts of data sources and contracts. This kind of software allows users to analyze complex contracts and documents with a much higher level of efficiency, saving time and money and allowing for identifying and mitigating risks.
Spellbook, Syntheia, and IronClad are other examples of companies focused more specifically on contract review. While these tools do not replace human judgment and experience, they help significantly speed up what is traditionally a prolonged process and help identify red flags that can turn into major issues down the road.
The use and benefits of AI in transactions go beyond just due diligence and contract review, extending into target identification and valuation — two areas that can often present difficulties.
Target identification requires extensive research and detailed analysis of, again, what can be mass amounts of data. In order to better match targets and buyers, AI tools can compound numerous data sets to pinpoint patterns that humans cannot otherwise recognize, enhancing the target identification process.
It is important to remember that AI tools do not take into account corporate value compatibilities and visions of the buyer, but they can help narrow down available targets and provide insights. They can also analyze market trends, competitor performances, and customer behavior to identify the impact of a target and a company’s most suitable acquisition targets. An essential part of this process is the technology’s ability to assess the risks of a transaction or the risk indication of a potential target.
Determining a “fair” valuation can be one of the most difficult parts of the transaction process, but the predictive modeling techniques we discussed earlier can also assess the target’s future potential and forecast financial performance more accurately. AI algorithms can analyze historical financial data, market trends, and macroeconomic factors to project potential growth scenarios.
AI also has the capability to analyze thousands of previous valuations and learn from them, putting that knowledge to use in the valuation process. This data-driven valuation approach, combined with the human intuition of dealmakers, helps negotiate better deals and avoid overvaluation or undervaluation of the target company.
Potential issues and concerns
Despite AI’s numerous advantages to the deal landscape, as with any new and developing technology, some issues and concerns must be considered.
Data Privacy and Security: Because of the vast amounts of data processed by AI tools, there are real concerns about data privacy and security breaches when used in the deal context. Sensitive and private information is shared throughout a transaction process, and the last thing parties want is for their data to be compromised. Companies should ensure robust data protection measures to prevent unauthorized access and potential misuse of sensitive information.
Lack of Human Judgment: AI lacks human judgment and intuition. It is important to bear in mind that while it is a valuable tool, AI does not make human involvement obsolete or irrelevant, especially when evaluating qualitative factors and understanding the nuances that can impact a deal’s success.
Regulatory Compliance: Relying heavily on AI may inadvertently lead to non-compliance with various regulatory frameworks governing transactions. Similar to the lack of human judgment, some areas of AI technology still need to be fully developed enough to rely on entirely. Compliance requirements may easily be overlooked by AI technology.
AI is an Investment: AI is still in the very early innings, and everyone may not be able to take advantage of it right away. To some organizations, AI means they may have to go out of their way to invest time and money into technology and possibly even personnel. Companies should be mindful of the time and resources required to implement AI tools and carefully weigh the risks and rewards.
AI will undoubtedly continue to transform the way deals are conducted, and companies are already jumping in to launch new tools to automate this process even more. Its potential benefits are vast, enabling people to make more informed and efficient decisions. However, it is vital to strike a balance between leveraging AI capabilities and incorporating human expertise to address serious concerns that exist.
AI is not yet at a place where the technology can replace all human interactions and procedures within a deal, and AI users should be cognizant of the issues and concerns presented above. But with proper safeguards in place, it can serve as a powerful ally in creating more informed and ultimately more successful deals.
Originally published by Westlaw.
Reshaping of the Biotech M&A Landscape
By Louis Lehot, Eric Chow; While the road ahead might be unpredictable, the core dynamics of the life sciences sector…
Webinar Key Takeaways: The New Biden Executive Order on Foreign Investment
Author(s): Louis Lehot Christopher Swift