How embracing AI in M&A can drive exponential value

Private equity and strategics are increasingly buying AI and GenAI capabilities. According to Crunchbase, GenAI and AI startups raised almost $50 billion in 2023, a 9% increase over 2022 levels.¹ Furthermore, we see AI and GenAI deal activity across nearly all sectors:

● Technology: Acquisition of AI based capabilities to enhance AI Business offerings to improve customer experience and productivity.

● Life sciences: Acquiring products from a clinical-stage drug discovery firm, which uses AI for a proprietary drug discovery engine.

● Insurance tech: Acquisition of AI-driven cyber risk analytics and GenAI-enhanced underwriting and quoting.

● Oil and gas: M&A and investment in AI-enabled digital models that can increase operational efficiency by enhancing reservoir characterization.

Learn more about the rising trends of increasing AI and GenAI footprints and divesting vulnerable businesses.

With its ability to digest large quantities of data, synthesize and summarize findings quickly, develop quantitative and qualitative analyses, provide recommendations and predictions based upon pattern recognition, and refine outputs through deep learning, AI and GenAI’s impact can span the full M&A life cycle.

To date, much of the focus has been earlier in the life cycle. This is likely driven by companies starting to apply it where they are most comfortable and feel the least risk. Key examples include using AI and GenAI to evaluate markets, products, and technologies to inform strategies, identify gaps or vulnerabilities in product portfolios, and prioritize targets that fill those gaps. In fact, several private equity funds are already engaged in exercises to understand how GenAI could have an impact on their portfolio of investments.

Discover how AI is also helping to identify targets in a novel way.

While we anticipate acquisitions of AI- and GenAI-augmented business will continue to be a focus, we also see that early experimentation with AI is uncovering opportunities to improve top-line growth, reduce costs, and minimize execution risk. In fact, a recent Deloitte survey² found that 79% of CEOs believe AI will increase efficiencies, and 52% believe AI will drive revenue growth for their enterprises.

As the survey signals, top-line growth is not the only consideration coming into focus. The associated cost opportunities and operational benefits are becoming clearer as well. Some buyers are already incorporating modest cost savings associated with more well-founded use cases such as deploying advanced chatbots to reduce customer service costs, automating coding and documentation tasks to lower software development costs, or even personalizing marketing content while trimming associated spend.

Discover how some buyers are incorporating AI-fueled cost savings.

The question is not whether GenAI will affect M&A, but rather at what pace? The technology’s potential to recast the look and feel of dealmaking is significant, but several challenging headwinds must be navigated to bring that potential to fruition.

GenAI suffers from hallucinations: making incorrect inferences from its source data that may seem correct. As with any tool, results and quality must be validated. GenAI is likely to open a gap and lag in understanding and development for the average or early career employee. Additionally, regulatory and ethical complexities continue to evolve and at a seemingly slower pace than AI. We also see access to or ownership of large, high-quality, proprietary data increasing in importance as a source of advantage.

Perhaps GenAI will come to differentiate M&A winners from laggards. On the other hand, AI technologies may simply become mission-critical capabilities that all companies adopt equally—tomorrow’s analog to the internet or electricity.

In a world of increasing “unknown unknowns” and accelerating rate of advancement, companies can benefit by formalizing their approach to sensing.

For example, companies should examine sources of new potential threats that may arise from GenAI disruption. Such sources may expand the definition of competitors to include smaller, nontraditional entrants and markets beyond existing products and offerings.

Companies should reexamine their industry structures and reimagine their business models through an AI lens. The first consideration is to understand and challenge existing assumptions that the industry will operate in the future as it does today.

The second consideration involves disaggregating the value chain and pushing on it: Where could AI both disrupt the chain and create shifts in power? Lastly, given these dynamics, companies should make deliberate choices in a “where to play and how to win” strategic choice framework.

As companies pursue AI acquisitions, they will have to identify internal talent or access external expertise, or both, to assess AI and GenAI targets. That expertise will be critical to evaluating the quality of the underlying technology and impact to existing assets. Increasing the level of understanding across business leaders, including commercial and R&D will be critical to identifying new internal diligence leads and sourcing deals.

Planning can only take you so far, and we believe that “learning by testing” is critical in this early stage of new technology adoption. Companies should leverage a cross-functional team’s perspective to assist with prioritization of AI and GenAI use cases. Regarding prioritization, teams should consider evaluation of use cases based on their customer value, business impact, feasibility, and investment needs.

Dive deeper into these four strategic actions.