Artificial intelligence is already improving the efficiency of many of the data evaluation tasks of dealmaking. But will AI totally transform the M&A process? Probably not in the immediate future – especially in the realm of small to mid-sized privately held businesses.
Selling a privately held business requires trust, transparency, and integrity. The process stirs up some unexpected emotions when the owner and his or her family has been part of the business for decades. Some deals fall apart for reasons that aren’t predictable or logical.
The Impact of ChatGPT
Although predictive AI has been making its way into data analytics for years now, the ongoing development and refinement of natural-language processing AI tools such as ChatGPT will further improve M&A deal making efficiency.
OpenAI’s launch of ChatGPT in November 2022 enabled us to see AI in action. It gave us a sense of how powerful AI might eventually become
ChatGPT can be prompted to automatically generate business, legal, educational, or creative content such as essays, blog posts, emails, contracts, computer code, video scripts, and more.
In M&A, Its large-language-learning model has already been adapted for specific tasks such as legal research, contract development, and financial analysis. For example, the BloombergGPT has been developed specially for the the language used in financial data and analysis.
Observations from my Own Testing
After experimenting with ChatGPT myself, I wondered how AI might be used to improve the process of buying and selling businesses.
First I considered how financially sophisticated private-equity groups had already reshaped the M&A landscape for small to mid-sized label, packaging, and graphics companies. The growth of virtual data rooms and other technologies during the COVID shutdowns made it easier to exchange and review confidential documents in the cloud and make plant visits remotely.
Next, I read some of the other AI-related articles that other M&A thought leaders had posted this year.
Finally, I asked ChatGBT to compose a 500-word blog post on how AI might be used in the mergers and acquisitions process.
In less than 90 seconds, ChatGBT delivered a straightforward, seemingly factual blog post. Wow!
But when I read the post closely, it seemed like a superficial summary of content that was already published online. Using ChatGBT is like using a super search engine on steroids.
The ChatGBT-generated post didn’t contain any of the experience-based insights that the M&A thought leaders had included in their posts. It lacked specific examples and neglected to mention that M&A processes involving big, publicly traded companies are often much different than the larger number of smaller M&A transactions that involve family-run, privately held businesses.
So I have adapted the ChatGBT’s draft to incorporate my experience-based observations related to the specific interests of my intended audience.
Potential Uses of AI in M&A
Without knowing how AI usage will be regulated, it’s impossible to predict all of the ways it could be used. Right now, AI could quicken the pace of M&A deals by improving the thoroughness of document analysis during the due diligence and contract development phases. It could also reduce risks created by overlooked red flags.
Deal Sourcing. Machine learning models can analyze vast datasets to identify potential acquisition targets based on specific criteria, such as financial performance, market share, or product alignment. AI can also help buyers discover more useful data about privately held companies before the target companies are identified.
Valuations. AI can quickly analyze relevant data from a larger volume of comparable deals and analyze dozens of documents to estimate a reasonable value for a targeted business.
AI-driven predictive analytics can assess factors that influence valuation, including market trends, competitive landscapes, and financial health.
Due Diligence and Reverse Due Diligence. Staff members won’t have to sift through mountains of financial and operational data manually. Instead, AI-powered algorithms can automate data collection and analysis. By rapidly identifying potential red flags and opportunities, AI enables more informed decision-making during the early stages of an M&A deal. It can reduce the risk of overlooking critical information.
In reverse due diligence, AI can help sellers gather better insights into the buyer, such as their track record on previous acquisitions. Was the post-sale integration process successful? Did the merger lead to a stronger, more profitable enterprise?
Choosing the right buyer matters to business owners who want to do what’s best for the future of their employees, partners, and legacies.
Contract Development and Review. As generative AI becomes more advanced, it will likely be used to draft both simple and complex contracts.
Both sell-side and buy-side lawyers can review these drafts and use AI to analyze the effects of each proposed change in terms. For example, ChatGPT might be used to evaluate and compare the effects of various earn-out scenarios.
Risk Assessment: AI-powered tools can flag potential issues related to regulatory compliance and outstanding litigation.
Post-Sale Integration: AI can be used to determine which elements of businesses could be combined to reduce costs and achieve greater profitability.
AI-driven project management tools can track progress, identify bottlenecks, and recommend solutions for a more efficient integration. To assist in aligning cultures and workforces, AI can speed up data migration and identify potential conflicts in terms of human resource policies and practices.
Risks
Data confidentiality. Confidential and privileged information should not be entered into publicly available AI platforms. All partners must respect the requirements of an NDA and restrict access to the secure virtual data rooms that are set up to facilitate deals.
Inaccurate information. Not all content published online is accurate. Likewise, not all of the content generated by AI will be accurate. Industry experts will still be needed to identify details in AI-generated content that aren’t quite right.
Industry Differences. Because generative AI relies on data that is available online, it may not have a full understanding of the different jargon, technologies, and best practices used within various industries.
“Human creativity and interpretation is still needed to stay competitive in M&A,” writes Tanguy Lesselin, CEO of Finquest, in Forbes. “Without the human touch, the solutions provided by generative AI will most likely be marginally better than the average response from the past.”
I agree. AI tools for natural-language processing should be viewed as assistants to content developers – not replacements.
Like the internet, AI tools will help everyone get up to speed on any topic extremely quickly. . When humans skillfully use AI to complement their own work, AI can empower each party in an M&A deal to get a clearer, fuller picture of the other participants in the deal.
But M&A deal making will continue to be driven by human relationships and reasoning.
While AI handles more mundane tasks, dealmakers can focus on understanding the unique value propositions of a company, finding synergies, negotiating the purchase agreement, and forging post-sale integration strategies for the benefit of all stakeholders.
Ideally, if buyers and sellers can focus on more of the human elements of an ownership transition, they will be able to reduce some of the tension, conflicts, and emotional turbulence that occurs during most small- to mid-sized M&A transactions today.
If you are a newcomer to mergers and acquisitions, the LaManna Consulting Group has published a helpful glossary of terms. Download it here: Demystifying M&A Jargon.