Nixon Peabody’s Hot Topics in the Middle-Market series welcomed a panel discussion featuring insights from AI experts to shed light on the utilization of AI, investments, and corporate transactions involving AI technologies, and the risks and concerns of AI that market participants are monitoring. The panel featured Randy Bean of Data & AI Leadership Exchange, Dan Dresner of Houlihan Lokey, Siobhan McCleary of Accenture, Chris Ryan of Clearhaven Partners, and John Werner of Link Ventures, and was moderated by Nixon Peabody corporate partners Amy O’Keefe, and Jason Hirsch.
The panel discussed the current state and future potential of AI in various industries, including investment banking and private equity. The panelists highlighted both the transformative potential (and limitations) of AI, as well as the ethical and legal challenges associated with its adoption. They emphasized the need for a balance between technical skills and ethical decision-making, particularly where AI has the potential to surpass human cognitive abilities in the near future. The importance of understanding the developing regulatory landscape was also featured.
Some panelists still maintain a preference for a people-driven business model over AI-driven deal sourcing and execution. This perspective underscores a fundamental tension in the industry: while AI can offer significant efficiencies and insights, the human element—intuition, experience, and relationship-building—remains invaluable. This human-centric approach highlights the importance of balancing technological advancements with traditional investment practices.
Utilizing AI in the M&A Process
AI is gradually making its way into the M&A space, particularly in areas like deal sourcing and review of potential investment opportunities. The panelists began their discussion on data usage and AI’s significant potential in accelerating growth opportunities. By analyzing vast amounts of data, AI can identify undervalued companies and potential investment opportunities that might otherwise be overlooked. AI helps in identifying target companies by analyzing various data points like employee growth and market trends. AI may be used to enhance market and data analysis, thereby improving the quality of due diligence. This capability is especially useful in a competitive market where timely and informed decisions can make a substantial difference. The use of sentiment analysis to gauge market reactions and potential risks is one example of how AI is beginning to play a more prominent role in dealmaking.
The panelists discussed the early stages of AI usage in legal contexts, such as crafting NDAs and purchase agreements. Although AI has proven useful in tasks like contract review and summarization, skepticism remains in its ability to craft complex legal language and address nuanced legal concerns. While AI can significantly aid in repetitive and “mundane” tasks, it should not be relied upon as a substitute for human judgment—careful integration and oversight is necessary.
Notwithstanding, AI has been gradually transforming the sector. For instance, AI helps in creating detailed and data-driven presentations that highlight a company’s strengths and future potential to investors. This not only has the potential to speed up the diligence process but also ensures that the information presented is comprehensive and compelling.
AI presents numerous transformative opportunities. Portfolio companies are using AI to increase productivity and accelerate competitive differentiation. AI tools can streamline operations, reduce costs, and uncover new revenue streams, positioning companies more favorably in the market.
A major challenge in leveraging AI across portfolio companies is the standardization of data. Without standardized data, it’s difficult to fully utilize AI’s capabilities. Inconsistent data formats and quality issues can hinder AI’s ability to provide accurate and actionable insights. Standardizing data is a prerequisite for effective AI implementation, enabling more consistent and reliable outcomes. Once data is consistent, AI can be leveraged to provide insights into performance and potential areas of improvement. However, this functionality remains a work in progress for many companies and thus the full benefits of AI in portfolio management are yet to be realized.
Investments in AI
AI presents significant investment and development opportunities as industry leaders are increasingly prioritizing investments in generative AI models, which are designed to generate new data that is similar to the training data and is used in applications like content creation, simulations, and data augmentation. Innovators and entrepreneurs are creating new norms, driving the AI moment forward. As one panelist suggests, however, AI adoption will be gradual, with legacy companies adapting more slowly than expected. Only a small percentage of firms have AI in production at scale, indicating a slow adoption rate. This slow pace is attributed to the lack of safeguards and a significant talent gap. Effective AI implementation requires not only advanced technology but also skilled professionals who can manage and optimize these systems. The panelists emphasized the importance of long-term planning and urging companies to stay ahead of the AI curve.
AI Risks and Concerns
Investing in AI involves navigating various risks, including regulatory compliance, intellectual property protection, and governance. The panelists highlighted the developing enforcement landscape, with policies and procedures emerging to mitigate risks. The SEC may target companies that exaggerate their AI capabilities, adding another layer of complexity to AI investment.
Data privacy and ethical considerations are also paramount. The panelists advise caution when sharing proprietary data with third parties and raise concerns about AI models being trained on unethical or illegal data. These issues underscore the need for robust ethical guidelines and rigorous oversight in AI development and deployment.
The panelists note a growing divide between the excitement surrounding AI and its practical applications in M&A and investment management. AI is often misused and overhyped, with large companies occasionally facing setbacks as a result. This discrepancy between expectation and reality calls for a more measured approach to AI adoption, emphasizing practical, incremental improvements over grandiose promises.
What is ahead for AI?
Looking ahead, the panelists agreed that AI will continue to evolve rapidly, with potential transformative impacts across various sectors. However, they emphasized the importance of balancing innovation with caution, ensuring that AI is used responsibly and ethically. While AI holds the promise of enhanced profitability, productivity gains, and competitive differentiation, its integration is fraught with challenges. Data standardization, talent gaps, ethical considerations, and regulatory compliance are just a few of the hurdles that firms must navigate.
AI adoption in the M&A industry is a complex, multifaceted journey. As companies navigate this rapidly changing landscape, the combination of AI and human intelligence will likely drive the most significant advancements. By leveraging a hybrid approach in AI’s efficiency and analytical power, while maintaining human oversight, judgment, and creativity, businesses can achieve a competitive edge in an increasingly data-driven world. As the panelists suggested, the key to success lies in finding the right balance between AI and human intelligence, ensuring that technology serves to augment rather than replace the unique capabilities of humans.