Due diligence reimagined: AI’s impact on valuations

There is no question we’re living and operating in a new world. When it comes to merger, acquisition or divestiture opportunities and considerations, today’s deal teams must adopt a structured AI due diligence approach that evaluates not only current capabilities but also how AI may disrupt a company’s ability to compete in the future. An organization may appear highly profitable today but face rapid erosion of its market position as AI renders its business model obsolete, lowers barriers to entry, introduces new AI-native competition, compresses margins, or automates delivery of core value propositions and brand promises.

To help PE firms and deal assessors address these changes, we have developed a comprehensive assessment framework anchored in seven core dimensions of AI maturity.

Setting the foundation: The case for AI-focused due diligence

AI has accelerated the pace of innovation and disruption across nearly every sector and industry. Organizations that successfully harness AI gain substantial advantages in cost efficiency, speed, personalization and product capability, greatly enhancing their value. But there’s another side to this coin: AI enables new competitors to emerge rapidly, often with lighter cost structures and more scalable operating models, elevating the potential for an organization to be disrupted and its business model to become dated and irrelevant.

This dynamic creates a new challenge for deal teams:

Are we paying today’s valuation for a business whose revenue may materially decline in an AI-driven future?

Consider this example: A company generating $1 billion in revenue today may operate in a sector where AI-enabled automation, generative models or decisioning systems could commoditize their offerings. Failing to assess this disruption risk could result in materially overpaying for an asset that is poised for significant revenue decay.

Given these dynamics, AI due diligence must be both holistic and forward-looking, integrating analysis of technology, governance, people, platforms, risk, products, scalability, and perhaps most important, the industry’s AI disruption horizon.

Seven core dimensions of AI maturity

In our view, a comprehensive AI maturity model provides a structured lens for evaluating how well a company can leverage and survive AI transformation. Following are seven dimensions that, for PE firms, serve as the foundation of an effective AI due diligence program.

1. Strategy

A mature AI strategy aligns directly with the company’s long-term business objectives. High-performing organizations articulate clear roadmaps for AI adoption, prioritize use cases based on measurable value, and anticipate shifts in the market that may demand changes to the roadmap and overall AI strategy.

Further, a strong strategy offers valuable insight into management’s vision for sustaining AI’s competitive advantage. It better positions the organization to outpace its rivals and pivot swiftly as technology and market forces evolve. In addition, a strong AI strategy serves as a shield against disruption. Companies lacking a cohesive AI strategy frequently fail to anticipate competitive displacement, elevating exposure to future revenue erosion.

2. Adoption

The depth and breadth of AI use case deployment are strong indicators of organizational capability. AI-mature companies move beyond pilots and embed the technology meaningfully in both operational and customer-facing processes. These organizations have developed an operating rhythm for how to convert AI ideas into production on a consistent basis, which could be a force multiplier.

The maturity of different AI use cases signals whether the company can scale AI advantage fast enough to remain competitive. A company with limited or fragmented use cases is more vulnerable to AI-native challengers. Ad hoc AI implementations are a critical sign of a business at risk.

3. Governance

Governance remains one of the strongest predictors of responsible, trustworthy, secure and scalable AI adoption. AI-mature organizations maintain clear decision rights, model approval processes, ethical AI principles and structured documentation.

Strong AI governance frameworks are essential not only for compliance but also for agility. Companies with weak governance typically cannot deploy AI quickly or safely. The result is slower innovation, increased security risks around data and systems, and greater exposure to AI-driven disruption from more nimble competitors.

4. Risk and compliance

As regulatory scrutiny intensifies around the world (the EU AI Act, for example), companies must be prepared for requirements around transparency, data rights, model explainability and high-risk system classifications.

When it comes to successful AI deployments, regulatory readiness has become a core competitive differentiator and is essential to sustaining trust and market permission. Noncompliance risks can trigger product constraints, legal liabilities and reputational damage — each having a potential impact on revenue. A company unprepared for emerging AI regulation in every jurisdiction in which it operates carries significant future value risk.

5. People

Talent depth across data science, machine learning engineering, technology architecture and responsible AI plays a central role in an organization’s ability to build and maintain AI capabilities. A sufficiently skilled workforce is also essential to realizing the full value proposition of AI initiatives and sustaining organizational readiness. This includes, but is not limited to, key governance functions such as legal, risk, compliance, audit, privacy, security and data teams. The toolsets to build and manage AI are evolving rapidly, meaning a higher percentage of the workforce than ever before will be leveraging AI in their day-to-day tasks.

A deficit in AI talent and the absence of a clear talent management strategy are leading indicators that a company may fail to keep pace with industry changes. In a rapidly evolving landscape, lack of skilled personnel can translate into slower product innovation and revenue vulnerability. A strong return on investment (ROI) from AI is contingent on human capability. Simply purchasing AI tools is not enough; organizations must invest in upskilling, new roles, new approaches to talent management, and a cultural shift where AI is a collaborative co-worker, not merely a tool.

6. Products and services

There must be a clear view of how AI enhances the company’s market offerings, customer experience and competitive differentiation.

Products leveraging AI in defensible, proprietary ways (for example, customer service AI agents) are far more resilient to market disruption. Conversely, organizations with offerings such as an AI “wrapper” on top of a generic application programming interface are easily replicated by emerging models or low-cost AI tools, and they are at high risk of revenue compression. ChatGPT rolled out to the masses in November 2022, yet that is an eternity ago considering the rapidly changing AI landscape. The majority of consumers now expect “GPT-like” capabilities as they engage with the organization and its brand. Determining what that means for the company, its products and the customer experience it delivers are critical areas of differentiation and future valuation.

7. Partners and platforms

A scalable AI ecosystem depends on resilient platforms, aligned vendor partnerships and a well-architected infrastructure.

Platform fragility limits innovation velocity and increases operational risk. Companies relying on opaque, brittle or unscalable third-party solutions expose themselves to disruption not only from competitors but also from their own technology stack and technical debt. Partners with antiquated technology, software or approaches jeopardize longevity and competitive advantage. Strong, rapidly evolving partners and platforms that prioritize continuous improvement and enhancements are vital to longer-term success.

AI and the risk of future revenue disruption

This is the element of AI due diligence that is increasingly mission-critical, yet it is often overlooked.

Here is the central question for PE firms and deal assessors: How will AI reshape the industry’s economics, and what does that mean for the company’s performance revenue and valuation?

To evaluate this, due diligence must include assessments of the following areas:

1. Market disruption horizon: How is AI reshaping the competitive landscape? What new entrants are emerging?

2. Automation and margin compression: Could AI reduce the cost of delivering similar services or products? If so, by how much?

3. Customer behavior shifts: Will customers expect AI-augmented solutions that the company cannot provide?

4. Substitution risk: Could AI or automation replace the company’s offerings entirely?

5. Internal readiness to adapt: Does the company have the strategy, operating model and talent to pivot?

Consider a real-world example: If a target company generates $1 billion today but AI is poised to automate 40% to 60% of its value chain, future revenue may be materially lower, and the target may even be exposed to disintermediation risk. Paying a valuation based on current revenue without proper discounting for future AI disruption is a significant deal-level risk.