Artificial intelligence has surfaced as a major consideration in due diligence as both private equity and corporate M&A buyers are investing more time and effort to uncover the technology’s potential impact on target companies. Most acquirers tell us that an AI diligence has convinced them to walk away from a deal, yet the smartest dealmakers aren’t just turning down deals; they’re using diligence to uncover how AI could unlock new efficiencies, growth levers, and even entirely new business models in a target. It has become a key component for a full potential diligence.

When a financial sponsor performed diligence on an AI-native healthcare company it hoped to acquire, the Bain diligence team was able to build and test a prototype tool “outside in,”  which convinced the sponsor that the target’s technology might more easily be challenged by incumbents or new entrants.

On the other hand, during the due diligence for a leading specialty workflow software company, the team determined that AI was more of an opportunity than a risk. The product was entrenched in customer workflows with high switching costs, and it had loyal customers. It held defensible data/workflow moats. Moreover, the management team treated AI as a board-level priority and had a track record of adding AI features that customers are starting to pay for, and its strong brand and competitive position reinforced these advantages. The team concluded that the company was well positioned to defend its core and create new value as AI adoption accelerated.

What type of AI risk and opportunity do we see in diligence?

The best financial investors and corporate dealmakers use diligence to establish the level of technology-inspired disruption risk and opportunity for each business based on the target’s value proposition, market, product offering, and cost structure. As a critical starting point, they set out to identify whether the target (or the business unit or even a particular product) falls into one of three categories based on the level of impact that AI could have on the business: revolution, transformation, and augmentation.

Revolution: These are targets in areas such as translation services and outsourced customer support, where AI tools or platforms can put a fundamental business model at risk. For most, survival means reinventing products or services.

Transformation: For targets in this category, the business model will need substantial changes. For example, AI can both lead to new revenue streams and operational efficiencies while also requiring a significant overhaul of existing processes and strategies. In healthcare, AI tools can analyze medical images and patient data faster and more accurately than doctors, speeding diagnosis and enabling personalized treatments. But capturing this value requires substantial investment in technology and training. Above all, targets in this category need to move quickly to embrace the new technology. Transformation companies that wait on the sidelines could see market share erosion and increasingly be left behind. Each quarter of delay can add up to a full year of catch-up.

The fate of these companies as it relates to AI heavily depends on their ability to execute against product- or service-related opportunities while also changing their cost structure. But adopting AI is not just about using tools. It is much more about utilizing the right technology in the right places, changing processes and roles, enhancing proprietary data assets that larger language models utilize, and enabling broader change management and training throughout the organization.

Augmentation: Our analysis found that about half of all companies fall into this category. For them, AI isn’t about redefining the business from the ground up; it’s about unlocking measurable value without the disruption of reinvention.

Companies here can capture meaningful cost savings and efficiency gains while also tapping into fresh opportunities across the customer journey. For most, AI becomes a powerful catalyst for streamlining operations, lowering expenses, and elevating customer support. It opens the door to enhanced products and services and new revenue streams—all without forcing a fundamental shift in the core business model.

Five key questions you should use to assess AI’s impact on a target

The good news is that most companies have substantial upside from AI. In the course of our work with clients, we have evaluated the impact of AI on more than 1,000 companies in diligence. There aren’t too many revolution targets, and they are relatively easy to spot in diligence. Among the more than 300 companies we analyzed for a formal study, less than 10% fall into this category. However, the technology’s impact can vary significantly based on where the target plays within the industry. Even within subsectors, there’s variation depending on the target’s specific product offerings and value proposition. Generally speaking, most software companies reside at least in transformation, and most industrial companies reside mainly in augmentation. The majority of healthcare companies straddle transformation and augmentation, with some in revolution.

That’s why it’s important for buyers to dig deeper to uncover AI’s impact. This can include investigating competitor moves, extensive research with customers, and even building prototypes to replicate a target’s core functionality. This can help investors fine-tune their investment theses or value creation plans before pursuing a deal.

The most effective private equity firms and corporate dealmakers use diligence to answer five key questions that evaluate the AI risk and opportunities of a potential target.

Question No. 1: Will the business model be upended? As a first step, it’s critical to understand whether AI will fundamentally disrupt a target’s business model. For example, as AI becomes more adept at generating high-quality text, graphics, and video, companies that are primarily involved in content creation likely will fall into the revolution category.

Question No. 2: Will market volumes be affected? Will pricing be impacted? Buyers must assess how market volumes and pricing structures might change for a potential acquisition. As AI reduces the need for human workers, per-seat or cost-plus pricing models could become risky. For example, if AI reduces the need for paralegals, a target that produces software for law firms to track hours and prices per seat could suffer from a significant drop in revenue. For services companies in general, the big question to understand is: Who will benefit the most, company or customers, regarding efficiencies gained from AI?

Question No. 3: Will the basis of competition change? Buyers need to determine if AI will lower the moat that the target has built up. Many data companies turn messy, hard-to-parse information (e.g., regulatory filings) into easy-to-use data sets through automated or manual workflows. These processing steps are made much easier with AI. So, the next question is whether competition will come from incumbents or start-ups. In many cases, incumbents have the right to win because they already have the data, go-to-market system, and other processes in place. But that won’t be true in every market. Some of the profit pool in a specific subsector could flow toward model providers or providers of AI tooling. For example, a services company could start giving up more and more of its margin to providers of the tooling that its frontline workers will be using to deliver their services.

Question No. 4: What improvements to the product offering are possible? This is how AI can help a business achieve its full potential. AI presents opportunities to enhance the product or service offerings in exciting ways. For example, a software business might investigate how its solutions could become more relevant to more users as it becomes more agentic in nature and addresses more services. By expanding the number of users at a business who can get value from the product, a company can offset some pricing issues. Similarly, AI can help make recommendations by the software more personalized and actionable. But exactly how will the target capture the upside from these features? Will they charge for them as an extra, use them to justify price increases, or use them as a differentiator to win share? All are valid options.

Question No. 5: Will there be meaningful cost savings for the business? Finally, it’s crucial to use diligence to evaluate the potential for cost savings from AI. These savings could come from automating routine tasks, reducing labor costs, or finding other ways to create operational efficiencies. The key questions are whether there are a lot of people in the target’s business doing knowledge work and how similar the tasks are among different people. That’s the situation with call center workers, developers, sales representatives, recruiters, creatives, case managers, among others. There’s probably some opportunity to have AI augment them to boost productivity.

These five questions help financial sponsors and corporate dealmakers assess the upside and downside impact of AI on a target. In addition, it’s critical to assess the AI readiness of the management team and the broader enterprise. That means evaluating the company’s AI strategy, its existing tech and data infrastructure, its use cases, talent, and operating model. In today’s market, the edge belongs to those who master AI diligence and know when to bet big and when to walk away.

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