Excellent article by Marc Vogelsang on AI and due diligence. AI could become a game changer for both buyers and sellers (VDR preparation). Outside in buyer dd could also benefit from AI. AI generated summaries of lengthy docs would also speed up due diligence.
Human and AI working together would also further speed up first dd drafts and make buyers more competitive (think auctions processes or preemptive offers). Marc’s concerns focuses on privacy and hallucinations. Interesting final comments on whether or not AI backed due diligence could become the new normal when assessing “fairly disclosed” condition. Should buyers of legal services now systematically request or at least ask their law firms about use of AI in due diligence and what would be the impact on due diligence speed and pricing?
Artificial intelligence (AI) is expected to significantly transform the mergers and acquisitions (M&A) due diligence landscape. AI-powered tools are constantly improving in automating a wide range of tasks, from data collection and analysis to risk identification and quantification.
In brief:
- In the near future, AI is expected to significantly improve the efficiency and effectiveness of the due diligence process by helping to identify potential problems and to make informed decisions earlier in the M&A transaction.
- This requires a seamless implementation of AI technology into existing M&A due diligence procedures.
- The future impact of AI on M&A due diligence goes beyond just optimizing the process, as legal concepts such as “fairly disclosed” often reference human knowledge, capabilities, and attention.
In 1770, Austrian inventor Wolfgang von Kempelen presented the “Mechanical Turk”, a chess-playing machine in the appearance of a man wearing a traditional Turkish dress. In front of an astonished and fascinated audience of monarchs and nobility, the machine won almost every game – even against seasoned chess players. The “secret” behind the machine was rather profane: the “Mechanical Turk” was steered by a person hidden in the box underneath the mechanical chess player.
More than 250 years later, another invention has captivated the audience: particularly through the development of Large Language Models (LLMs) such as ChatGPT and Bard, (generative) AI became tangible to a broad public and a wide range of businesses. This article outlines the technology’s anticipated uses and limits from an M&A practitioner’s perspective.
The field of play: current skills and limitations of AI in a due diligence context
The main benefit of software algorithms is their ability to mine large amounts of documents, contracts, and financial data. Accordingly, they can be trained to spot patterns, anomalies, or inconsistencies in the data available. These possibilities are neither new nor revolutionary as they have been used for years in simplified forms (e.g., in e-commerce, search engines, and by tax authorities). However, predominantly two areas within AI accelerate the use of this technology for a broader range of tasks:
- Machine learningis a subfield of AI that focuses on developing algorithms that can learn from data without explicitly being programmed for this (mainly through reinforcement learning). Improvements in AI capabilities to learn without human intervention are essential for its efficient and cost-effective use.
- Natural language processing(NLP) deals with the interaction between computers and human language. A subfield of NLP is the area of large language models (LLMs) used to generate and “understand” human language.
From an M&A due diligence perspective, there are still some relevant limitations to the use of AI algorithms, in particular:
- Accessibility of information:data in virtual data rooms (VDRs) is usually well protected and the sell side is often not willing to have their confidential information used for AI training. Also, the VDR provider is generally mandated by the seller. Hence, developing and training algorithms for M&A due diligence may be very limited “on the fly” in running transactions. Instead, this process may be based on a vast amount of public and anonymized data.
- Reliability and data protection:LLMs still tend to bluntly “invent” facts (often referred to as “hallucinations”) or to leak parts of the input data they have been trained on. While this is not a considerable issue if the LLM is being used to plan a family vacation or to compose Christmas cards, it is a showstopper for using sensitive data in a VDR and relying on AI findings.
The opening: analysis of publicly available information and setting up a VDR
For thebuy side, the first step in a due diligence process is often to delve into the public sources available on the target. The advantage of such outside-in due diligence is the (often vast) availability of information. Further, the buy side can perform a first analysis of the target withoutzeitnot(time pressure). At this stage, generative AI can examine public sources such as press releases, ad-hoc announcements, financial reports, prospectuses, and media coverage for ongoing tax and legal disputes. The issues identified in such an AI-generated report may serve as customized input for the information request list and the management interviews in the due diligence process.
On thesell side, setting up a VDR is often time-consuming and burdensome, tying up substantial operational resources. AI algorithms can streamline the process by automatically organizing the uploaded documents. Further, the software may support the team by checking the documents for sensitive information and directly proposing redactions. This capability provides considerable time and efficiency benefits to the sell side, particularly for transactions involving many documents with employee details, competitively sensitive information, and intellectual property.
Read the full article: https://www.ey.com/en_ch/strategy-transactions/how-ai-will-impact-due-diligence-in-m-and-a-transactions
