Rusty Wiley is CEO of Datasite, a leading SaaS technology provider for the M&A industry.
The sudden accessibility to a novel form of artificial intelligence (AI) that produces visual, audio and written content responses to user prompts has captivated the minds of business leaders, academics and the general public alike. Since the November 2022 launch of ChatGPT, the realities of revolutionizing decision making, problem-solving and creative processes across numerous industries through generative AI (GAI) are now coming to fruition. In fact, following their most recent earnings calls, executives at 110 S&P 500 companies discussed AI—a record—further showcasing the massive impact it’s having.
The Investment Opportunity
AI is expected to be a $2 trillion investment opportunity and potentially deliver around $13 trillion in additional global economic activity by 2030. However, some also see this new innovation as “the biggest bubble of all time.” But looking at dealmaking specifically, the value of AI-related deals has increased by 175% in the first quarter of 2023 compared to the same time in 2022.
Yet, whenever an innovation gathers interest, the question for business leaders becomes clear—buy, partner or build? Given the huge computing power and datasets needed to build and adequately train GAI models, there’s been a lot of activity dedicated to the first two options.
Recent deals, including one for $1.3 billion and another for $650 million, indicate that tech companies are eager to acquire GAI companies or take a minority stake in GAI startups. What’s more, this strategy isn’t limited to tech companies. Consulting companies are also making investments and establishing partnerships.
But although the technology offers a great opportunity to transform a broad range of industries by enabling businesses of all sizes to leverage its capabilities and remain competitive, using it isn’t without risks, including privacy, intellectual property rights, security and data quality. For example, in a single day, one leading AI company lost $100 billion when its AI engine shared inaccurate information in a promotional video.
For companies seeking to future-proof their businesses, transformational GAI deals will be top of mind as they look at not only the technology but also the skills needed to make use of it to value potential deal targets. Those who can create purpose-built models on-demand or apply large generative models to specific industries will be the most attractive.
Yet, although GAI deals will be ones to watch into 2024—especially if interest sparks the return of tech’s megadeals—I believe it’s the dealmaking process that generative AI will make its impact in this year.
Transforming Deal Management
AI has impacted nearly every industry, including M&As. Its potential to help dealmakers make more informed decisions faster is already revolutionizing the deal flow, due diligence processes and overall value creation. GAI has the potential to impact every element of the dealmaking journey.
Enhancing the due diligence process with GAI is a key example. Deals are high stakes and tight timeline environments, so quality control is key. An incomplete or incomprehensive document review exposes the deal to hidden risks. Using GAI to interrogate a document for keywords or phrases, potential risk factors, critical clauses and essential obligations can help pinpoint them fast. This process can then be reduced from days or weeks to hours, minutes or even seconds, potentially producing more accurate diligence reviews at a lower cost.
However, this doesn’t necessarily mean the overall due diligence process should take less time. The classification and extraction of information via GAI diligence enable dealmakers to devote more time and resources to what matters most, which includes taking longer to prepare and conduct due diligence to achieve successful outcomes.
Datasite data shows deal prep has lengthened by a median of 24 days since 2021. This presents an opportunity for improved risk management and heightened accuracy. Instead of looking for a needle in a haystack—especially when dealmakers are unsure of what that needle is and its impact—they can use GAI capabilities to reduce the instances of missing something buried that could hold a serious risk. Those red flags might be anything from an expired patent to a noncompete clause. With natural language smart analysis, GAI can better detect these and classify them by category, priority and sentiment.
GAI-based analysis can also be used in support of deal research, as relevant, fresh and actionable data can make or break a deal. That notion begins at inception, meaning identifying and engaging with potential buyers, mapping niche markets, keeping up with changing market conditions, understanding buyer history and even polishing pitch decks. GAI can enhance the speed and accuracy of the analysis needed to begin building a deal team.
Later, GAI analysis also comes in handy for identifying patterns in deal terms. From that insight, future financial performance and market conditions, ranging from volatility and competition to regulatory changes, can be forecast with sensitivity analyses simulating how these changes may impact the calculations, ultimately reducing the risk profiles of potential earn-out scenarios.
Additionally, although contract creation has been automated for many years now, introducing GAI brings a more sophisticated layer to this process with the ability to alert users when a contract departs from specifically agreed-upon or expected terms or uses noncompliant language (and could provide suggested replacements from pre-agreed-upon templates and industry standard wording).
Ultimately, the benefits of GAI are far-reaching. Also, instead of removing humans from the process, dealmakers’ skills are required to ensure the technology’s maximum potential is reached. As this technology is still in its infancy, data security and confidentiality, reliability and contextual understanding must be considered and gauged. But by clearing bottlenecks in the deal process, dealmakers will have the space and insight to focus on the work that gets a deal over the line.
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