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Every target is now subject to two forces: acceleration—AI as a growth multiplier—and erosion—AI as a margin compressor or existential threat. Whether you’re a PE sponsor evaluating a platform acquisition or a corporate acquirer assessing strategic fit, it’s critical to assess AI’s impact to your business case and overall investment thesis.
“The People Problem,” the first piece in our five-part series, “Deals in the Age of AI,” established the foundational thesis: The binding constraint on capturing AI’s potential in deals is people, not technology. In this second piece, we provide a framework for evaluating targets through an AI lens—how to assess acceleration versus erosion, what determines which force wins, and how to translate that analysis into valuation posture.
Acceleration and erosion
Consider an example in which two businesses in the same sector have identical EBITDA. One has proprietary data assets that become more valuable with AI, a product embedded in customer workflows, and leadership that has redesigned operations around AI-augmented processes. The other has a labor-intensive delivery model, features that AI tools can increasingly replicate, and an executive team that views AI as an IT initiative.
These businesses should not trade for the same valuation. The first has captured acceleration while the second is exposed to change and erosion. The investor who projects forward using pre-AI assumptions about growth rates and defensibility will likely miss the divergence already underway.
Acceleration: AI as a margin and growth accelerant
Most conversations about AI in business default to cost. How much can we take out with automation? Automation efficiency is table stakes, the larger valuation impact is often on the revenue side.
AI can accelerate growth in multiple ways.
- Faster product development cycles that increase competitive responsiveness.
- Improved conversions and customer experiences that drive retention and expansion.
- Personalization at scale that helps boost demand.
- New revenue streams from AI-enabled services and outcome-based pricing.
- Productivity improvements across sales, service, and administrative functions.
These gains aren’t independent. Faster product cycles improve customer experience. Better customer experience improves retention. Higher retention justifies further investment such as in personalization initiatives. PwC’s research on the path to generative AI value calls this the flywheel effect: Momentum compounds and each success reduces the cost of the next.
Businesses positioned for acceleration can grow faster and more efficiently than their pre-AI trajectory would have allowed. That’s a multiple expansion story, driven by both margin and revenue growth.
Erosion: AI as margin compressor or existential threat
AI also can create erosion risk through:
- New entrants who build faster and cheaper, thanks to reduced development and acquisition costs.
- Platform players who bundle AI capabilities and disintermediate point solutions.
- Price erosion which eventually leads to margin compression as competitors use AI to deliver the same output at lower cost.
- Capital inefficiencies and cost creep from AI infrastructure resulting in eroding capital returns.
But the deepest erosion risk is structural. Moats are being redefined. Traditional competitive advantages like scale, process complexity, labor intensity, and information asymmetry are weakening. AI lowers barriers to entry. It commoditizes features and narrows differentiation windows. It makes “good enough” alternatives easier to build and adopt.
Durable advantage is shifting. The moats that hold in an AI environment are different—distribution control, permissioned data rights that are contractually renewable and usable for training. Other moats include deep operational integration (where switching creates real friction) and trust competence (the governance, security, and compliance capabilities that PwC’s research on Responsible AI identifies as foundational to scaling AI safely and a source of differentiation). A business that looks defensible through a traditional lens may be quietly exposed through an AI lens. And that exposure may not show up in current financials. It shows up in where the business will be in three to five years.
Four questions help predict a target’s trajectory and determine whether acceleration can create value or if erosion will limit, or even destroy, value.
| Determinant | The question |
|---|---|
| Proprietary advantages | What does this business have that AI makes more valuable? |
| Pricing power | When AI improves productivity, do margins expand—or do gains flow to customers? Do the target’s products/services continue to be differentiated? |
| Competitive position | Where is this business vs. competitors, and is the window to establish advantage closing? |
| Execution capability | Can this organization absorb AI at the pace required? |
In this evaluation framework, the first three tell you whether the opportunity exists. The fourth tells you whether it will be captured, and that comes down to the people problem.
Proprietary advantages. Businesses with proprietary advantages in their data, processes, distribution, or products become harder to compete with as AI improves because AI acts as a multiplier on something a competitor cannot easily replicate. Businesses competing primarily on the efficient execution of tasks, or built on commoditized products and services, face the opposite dynamic. AI lowers the cost of replication, compresses margins, and accelerates the substitution risk that was already latent in their model. The diligence imperative is to distinguish, rigorously, between the two because a business on the accelerator side deserves a premium, and one on the disruptor side warrants a material discount to any valuation that doesn’t account for the structural erosion already in motion.
Pricing power. When AI makes a business more productive, do margins expand or does competition force those gains to customers? The answer depends less on the pricing model and more on whether customers value the output enough to pay for it regardless of how it’s produced. If the customer’s response to AI-driven efficiency is “that should cost me less,” pricing power is weak. If the response is “I’m paying for results and the results are better,” pricing power holds. The businesses most exposed are those where customers can see the cost of delivery compressing and have the leverage to demand the benefits flow through to them.
Competitive position. AI reshapes competitive dynamics by lowering barriers to entry, compressing differentiation windows, and making “good enough” alternatives easier to build. The questions that matter: Who are the AI-native entrants and what’s their wedge? Is AI making it easier for competitors to replicate what this company does? A strong position today can erode quickly if the advantage relies on something AI is undermining, factors like labor arbitrage, information asymmetry, or process complexity.
Execution capability. Can your organization absorb AI at the required pace? This is a leadership question. The framework from The People Problem applies: Leaders should give their organizations clarity on which use cases matter, capacity and permission to learn, and incentives aligned to new ways of working. The diligence should help determine who owns AI value delivery with P&L accountability. It also needs to assess whether success is measured or anecdotal. Finally, is capability embedded in process and governance, or is it dependent on one or two individuals?
Translating assessment to valuation posture
| High erosion risk | Low erosion risk | |
|---|---|---|
| High acceleration potential | Execution bet. Upside is real but so is risk. Underwrite conservatively; build execution milestones into value creation plan. | Premium. AI is a tailwind. Strong advantages, defensible position, proven execution. Pay up. |
| Low acceleration potential | Discount or pass. AI is a headwind. Moats eroding, entrants emerging. Price for risk or walk away. | Stable, not exciting. AI doesn't help much but doesn't hurt. Traditional valuation applies. |
The diligence work is figuring out which quadrant the target actually sits in, and whether your view differs from the market’s.
Valuation model implications
Our evaluation framework should change how valuation models are built. Dealmakers need to model both forces. Acceleration flows through to growth—faster product development, improved conversion, new offerings. Erosion flows through to pressure—margin compression, higher churn, AI cost creep. A business that uses AI to improve productivity but faces pricing pressure from AI-enabled competitors may see gains offset by concessions.
Dealmakers should conduct scenario modeling. Start with a base case of conservative workflow-specific gains plus real implementation costs and assume that generic AI features will diffuse quickly. The upside scenario should assume scaled deployment, improved retention, and successful operating model transformation. Downside assumptions should include price compression, higher churn, and governance friction. The sensitivity tests that usually matter include retention rates, price realization, AI infrastructure cost per unit, and time to scale from pilot to deployment.
Don’t confuse table stakes with advantage. If a capability will be matched by competitors within 12 to 18 months, it’s not a durable growth driver. Only credit growth drivers if they’re safeguarded by proprietary advantages.
Our PwC deals perspective
Evaluating a business in the age of AI requires new analytical muscle. To determine how much a business is worth in diligence, dealmakers should understand which side of the AI-driven dispersion the company is on. The decisive question is the same one we identified in The People Problem: Can this organization absorb AI at the pace required? That’s what determinant 4 is really asking. The other three determinants (proprietary advantages, pricing power, and competitive position) tell you whether the opportunity exists. Execution capability tells you whether it will be captured. And execution capability comes down to leadership pulling the three levers: Use case clarity, capacity to learn, and incentive alignment.
Dealmakers who build this lens into their diligence process—assessing targets on acceleration potential and erosion risk, evaluating the determinants that predict which side wins, and distinguishing credible evidence from AI overstatements—can be better positioned to price deals more accurately and avoid costly misjudgments.
But evaluating AI capability in targets raises a question: Have you built it in your own deal process? We’ll address that in Part 3 of this series.
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