Merger and acquisition activity is being reshaped by the need to understand how artificial intelligence will affect a target company’s future value, moving AI risk assessment from a niche concern to a critical phase in comprehensive due diligence. This shift affects both private equity investors and corporate acquirers who are spending increased time and resources to uncover the technology’s potential impact on business models, growth trajectories, and cost structures. The ultimate goal is to move beyond simple risk identification to unearthing how AI could unlock significant new efficiencies, growth levers, and entirely new business models. This rigorous, AI focused diligence has become a non negotiable component of a full potential assessment.
The experience of leading financial sponsors illustrates the dual nature of AI in M&A. In one case, a sponsor considered acquiring an AI native healthcare company. The diligence team built and tested a prototype tool which revealed that the target’s core technology was potentially too vulnerable to replication by either established industry incumbents or nimble new market entrants. This finding led the sponsor to abandon the deal, underscoring how AI diligence can be a deal breaker. Conversely, a similar process for a specialty workflow software company concluded that AI presented a greater opportunity than a threat. The company benefited from high switching costs, loyal customers, and defensible moats around its proprietary data and workflows. The management team had prioritized AI at the board level, demonstrating a proven track record of integrating features that customers were willing to pay for. This strong brand and competitive position reinforced the conclusion that the company was exceptionally well positioned to defend its core business while simultaneously leveraging AI for new value creation.
Analytical Framework for AI’s Business Impact
The most sophisticated financial investors and corporate dealmakers utilize a framework to categorize the level of technology induced disruption or opportunity. This initial assessment classifies a target based on the potential magnitude of AI’s effect on its business model, value proposition, market position, and cost structure. Companies or specific product lines are typically sorted into one of three distinct categories.
Revolutionary AI Impact
This category applies to businesses where AI tools or platforms pose a fundamental threat to the existing business model, often necessitating a complete reinvention of products or services for survival. Translation services and outsourced customer support, for example, face the risk of having their core value proposition severely undermined by highly capable and accessible AI systems. While this category currently accounts for less than 10% of the companies formally analyzed in major studies, its impact is the most immediate and profound. Identifying these targets is a critical first step in diligence, as they are often the easiest to spot and present the highest binary risk.
Transformational AI Imperative
For companies in this category, the business model requires substantial, though not necessarily existential, changes. AI adoption can unlock new revenue streams and significant operational efficiencies, but capturing this value requires a major overhaul of existing processes and strategies. In sectors like healthcare, AI tools are already analyzing medical images and patient data with superior speed and accuracy, accelerating diagnosis and enabling personalized treatments. Success in this category requires substantial investment in new technology, comprehensive training, and organizational restructuring. Companies that delay their strategic pivot risk significant market share erosion. The race to embrace transformation is crucial, as each quarter of inaction can create an obstacle requiring a full year of catch up. The fate of these organizations hinges on their ability to execute product and service enhancements while simultaneously restructuring their cost base. The shift is not merely about adopting tools, but about strategically deploying the right technology, optimizing data assets that feed large language models, and enabling broad change management across the enterprise.
Augmentative AI Value Creation
Approximately half of all companies fall into this category, where AI acts as a powerful catalyst for efficiency and incremental value rather than a disruptive force demanding reinvention. For these businesses, AI unlocks measurable value by streamlining operations, lowering expenses, and enhancing customer support without fundamentally altering the core business model. This enables the introduction of enhanced products and services and new revenue streams. Companies in industrial sectors are more likely to reside here, while many healthcare companies are found straddling the transformation and augmentation categories.
Five Pivotal Questions Guiding AI Due Diligence
Given the varying impact of AI across industries, it is vital for buyers to move beyond general assessment and conduct deep, targeted investigation. This often includes intensive research into competitor moves, detailed engagement with customers, and even building technical prototypes to simulate a target’s core functionality. The most effective private equity firms and corporate acquirers use diligence to answer five key questions that systematically evaluate the AI risk and opportunity.
Will the Business Model Be Fundamentally Upset
The initial step is to determine if AI will cause a foundational disruption to the target’s value proposition. As AI rapidly advances in generating high quality text, graphics, and video, companies primarily focused on content creation, for example, are highly susceptible to falling into the revolution category. This question establishes the baseline risk.
How Will Market Volumes and Pricing Structures Be Altered
Acquirers must assess the potential evolution of market demand and pricing mechanisms. As AI reduces the reliance on human labor, traditional cost plus or per seat pricing models can become precarious. For example, if AI diminishes the need for human paralegals, a software company that prices its product based on the number of seats for law firm employees faces a potentially significant drop in revenue. For services businesses, the core concern is determining whether the company or the customer will be the primary beneficiary of the AI driven efficiency gains.
Is the Basis of Competitive Advantage Shifting
A crucial question is whether AI will erode the competitive moat a target has built. Many data companies traditionally create value by transforming messy, complex information into structured, usable datasets through laborious manual or automated workflows. AI streamlines these processing steps, potentially lowering barriers to entry. Diligence must determine if competition will primarily emerge from incumbents, who often possess superior data, distribution, and go to market systems, or from startups. Furthermore, some of the profit pool in a subsector may begin to flow upstream to the model providers or AI tooling companies, potentially squeezing the margins of a services company that relies on such tools for its frontline workers.
What Product Enhancements Are Now Possible
This line of questioning focuses on how AI can help the business achieve its full potential. AI offers exciting avenues to significantly enhance product or service offerings. For a software company, AI can make its solutions more agentic, thereby increasing their relevance and value to a wider user base within a client organization. This expansion of users can help offset potential pricing pressure elsewhere. Similarly, AI can facilitate more personalized and actionable recommendations within the software. Crucially, the diligence must determine the strategy for capturing this upside: will these features be sold as premium add ons, used to justify broader price increases, or utilized as a key differentiator to win market share.
Can Significant Operational Cost Savings Be Realized
Finally, it is essential to evaluate the concrete potential for cost savings resulting from AI implementation, which can stem from automating routine knowledge work, reducing labor costs, and boosting overall operational efficiency. The key indicators for this opportunity are the presence of large groups of employees engaged in similar knowledge intensive tasks, such as call center operators, developers, sales representatives, or case managers. In these areas, AI augmentation presents a clear path to meaningful productivity gains.
These five questions provide a comprehensive framework for assessing the multifaceted impact of AI on a potential acquisition. Additionally, a thorough diligence process must evaluate the management team’s AI readiness, including their strategic vision, existing technology and data infrastructure, use cases, talent pool, and operating model. In the modern M&A landscape, the decisive advantage belongs to those who master AI diligence, using it to confidently determine when to place a large bet or when to prudently step away from a deal.
