Tanguy Lesselin, Co-Founder & CEO of Finquest, a fintech that sources off-market investment opportunities for Private Equity & Corporates.
As an industry, finance tends to be an early adopter of new technologies. The very nature of the industry, with its heavy reliance on data, speed and accuracy, makes it particularly receptive to technological innovations that bring the promise of improved efficiency and increased competitiveness. From electronic and algorithmic trading platforms to financial data analytics platforms, digital payments and blockchain/crypto, it’s clear the industry embraces new tech. So it stands to reason that AI—and, more recently, generative AI (GAI)—such as ChatGPT is the next frontier. But as generative AI’s capabilities expand, it begs the question: How is this going to transform things in M&A?
You’d have to be living under a rock to not have heard about ChatGPT, so I won’t rehash the basics of what it is. The only points I will mention are that it’s based on large language models (LLMs) and we’re only scratching the surface of how we can apply it. It can be a valuable tool in any scenario that relies on natural language data.
But AI is not new; rather, it has evolved over the last few decades. Dramatically. And it is not without its limitations.
What do we need to consider?
The first hurdle: AI is only as good as the underlying data. LLMs are essentially statistical models that learn from the data they are exposed to, which may contain biases, errors or inconsistencies. Even if your data is perfect, AI “hallucinations” are still a possibility (when the AI invents plausible-sounding answers based on what should statistically come up next, but which are completely, factually incorrect). An enormously dangerous game to play when the stakes are high.
The second hurdle is that of creativity. While LLMs such as ChatGPT have emerged as powerful advancements in natural language processing, nothing that comes out of LLMs is truly original (and by original, I mean creative). They do not have any inherent understanding of the meaning or context of the text they generate or process. Human creativity and interpretation is still needed to stay competitive in M&A; without the human touch, the solutions provided by GAI will most likely be a marginally better-than-average response from the past.
What are the benefits?
The most compelling way that we see GAI making waves for the end-user is through being another great leveler, in the same way that the internet was. It can help you get up to speed on anything, extremely quickly and with low or no barrier to entry—educating in a way that is often reserved for those who can pay for it. The reality of this seismic evolution was felt almost immediately by Chegg, a leading online training platform provider, who saw a massive decrease in its share price shortly after it communicated the potential revenue threat posed by ChatGPT in this sphere.
Overall, this means that you can quickly understand the landscape of any scenario, leaving you the time and space to focus on the value-added parts of your job: the creative solutions, understanding the unique value proposition of a company, finding potential synergies, the negotiation, the relationship, etc. Indeed, further enhancements in things like AutoGPT (a process-driven AI) mean the possibility of even more efficiencies in mundane tasks for the end user.
How can you utilize AI in M&A today?
Broadly, AI tools generally fall into two camps: those that improve efficiency, and those that aren’t intended for the end user. Easy wins on the productivity side are in target management (e.g., CRM tools that will assist in your communication and relationship management) and in due diligence, where you can leverage AI’s superior ability in data analysis and pattern recognition to call out the more obvious points. I believe the rise of virtual data rooms paved the way for AI assistance in this process.
When it comes to valuation, AI can perform some basic work in finding comparables, gathering data and estimating enterprise value, essentially doing the grunt work and freeing up resources for human-centric work. BloombergGPT (Bloomberg’s recently-launched chat feature) promises great things in this arena and is, no doubt, the first of many. Similarly, when it comes to post-merger integration, many labor-intensive or manual processes (e.g., contract management and data migration work) can be outsourced to the machines.
One of the most exciting ways AI is being leveraged in M&A is in deal sourcing, which has historically been heavily relationship-driven. AI can be used to augment the usually scarce private company data to increase the discoverability of targets. Generative AI can be used to transform the user experience of the interaction with rich and complex data sets, particularly for deal sourcing, assuming the algorithms are trained with a proper set of private company data.
But a word of caution with any and all of these tools: The data (or the input) remains crucial. As the old adage goes, rubbish in equals rubbish out—which, in the private company space, is a hard maxim to break.
The Bottom Line
It’s important to remember that AI is only a tool—it doesn’t possess the nuanced understanding of human relationships that are vital in, say, deal sourcing. Building connections, fostering trust and navigating complex negotiations are skills that remain firmly in the realm of human expertise.
So, AI should be used as a complement, not as a substitute. We should embrace it as a helpful ally, not as a threat to our jobs. By leveraging its strengths in data analysis and pattern recognition, we can enhance our own capabilities at a faster pace, be more productive as individuals and organizations and make better-informed decisions.
Ultimately, the fusion of AI and human intuition can be a driving force behind the future of deal sourcing in private equity and corporate M&A. So let’s welcome it as a valuable partner in our journey toward more efficient and effective deal-making.
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