Executive summary
Private equity leaders are under pressure to demonstrate AI's value across their portfolios — and that pressure is showing up at the deal table. For sellers, the ability to substantiate their AI claims is increasingly the difference between a deal that closes at the right valuation and one that doesn't close at all. Learn what buyers are testing, where AI claims most commonly break down under scrutiny and how to prepare before buyers start asking.
How sellers can prepare for AI diligence
When one SaaS company came to market, its leaders were convinced that incorporating new AI features would be a key differentiator. They assumed customers would pay a premium for the features embedded in their product, but diligence proved otherwise.
“During the AI diligence process, we found that customers were increasingly viewing AI-enabled functionality as table stakes and were not willing to pay extra unless it demonstrated true value or ROI,” said Chen Liu, Partner at Grant Thornton | Stax. “AI investments alone don’t translate into competitive differentiation or increase deal value. Customers need to see tangible value in areas like productivity, profitability or growth.”
Companies across the market face a similar scenario as they increasingly invest in AI. According to Grant Thornton's 2026 AI Impact Survey, 46% of private equity leaders said they are scaling AI across multiple functions, and another 45% are piloting it in select use cases. Thirty-one percent also cite competitor moves as the top external pressure driving that adoption.
As AI becomes more deeply embedded in how companies operate and compete, it is showing up at the deal table in a bigger way. Where a company’s AI capabilities once took up a single slide in a confidential information memorandum or management presentation, AI now commands dedicated sections requiring detailed evidence. Buyers expect that level of rigor, and they're arriving with sharper questions than ever before.
"Buyers are spending a lot more time thinking about whether AI strengthens their competitive position in a durable manner or if AI will ultimately weaken its competitive moat,” Liu said.
The market's appetite for AI claims has outpaced sellers' readiness to defend them. Sellers who wait until buyers start asking questions are already behind.
What is AI diligence?
Our survey found that just 9% of private equity firms say they're confident they could pass an independent audit of their AI governance within 90 days, far fewer than the 22% who say the same across all industries. That disconnect between the AI claims companies make and what they can actually substantiate is driving the need for AI diligence in the deal process.
"Almost every deal now involves product and tech diligence," Liu said. "Every organization can claim they use AI, but buyers need to validate whether it's actually driving differentiated value, both in the market and inside the business."
AI diligence isn't a separate workstream that gets added when a company happens to have AI. It's now embedded in standard tech and product diligence on nearly all deals.
AI diligence tests the gap between what a company believes its AI adds and what it can actually demonstrate today and over the hold period.
“Many companies are touting strong adoption of AI both internally and in their products, but not all of it translates to value. We call it ‘vaporware,’” Liu continued. “It sounds like you have AI, but what you actually have isn't being used or generating value.”
Undifferentiated or overstated AI claims carry consequences — not just for how a company is perceived, but for deal structure and timing.
"We're seeing the diligence cadence shift," Liu said. "Buyers are doing more pre-LOI work to validate the market and business model — and then going deeper post-LOI on what the business looks like three to five years out and whether it's a fundable exit."
When AI claims can't be substantiated, deals slow down. When they can be, buyers have a clearer line of sight to exit, and that changes valuation.
Questions buyers ask during AI diligence
Buyers test AI across four areas, and sellers who can't produce clear, substantiated answers in each one face valuation risk.
Core offering and competitive position
For companies positioning AI as a core part of their value proposition, buyers want to know whether AI is embedded in the product as part of the core offering in a way that drives pricing power, retention and competitive differentiation, or whether it's a feature customers expect as standard or could easily find elsewhere. That distinction matters because it determines how defensible the revenue is over the hold period.
"The key question is the AI functionality enhances stickiness among customers and reduces churn, or whether they can derive the same functionality from competitors or new entrants," Liu said.
AI’s impact on profitability
From there, buyers examine how AI shows up in the financials and how exposed the business is to AI-driven market shifts. Is AI tied to the company's commercial model in a way that drives measurable revenue growth? As AI capabilities advance across the market, how confident is the company that customers will still be paying the same amount — or more — for what they're getting three to five years from now?
"Buyers want to see evidence that AI has been used to improve the bottom line, either though additional revenue streams or greater operational efficiency," Liu said.
Buyers also examine which models a company is using and why. In regulated industries such as healthcare and financial services where financial reporting and compliance requirements are specific and significant, they need to understand whether models are deterministic — producing consistent, repeatable outputs given the same input — or probabilistic, where outputs can vary.
How AI is moving the business
Buyers want to know whether AI is moving the needle on both sides of the P&L. On the cost side, efficiency claims need to be tied to measurable outcomes, not described in general terms. On the revenue side, they want to know whether AI is opening new business lines, improving customer outcomes or driving expansion across existing customers.
Data infrastructure
Buyers also care whether a company has the underlying data infrastructure to sustain and scale its AI with clean, well-documented data pipelines, clear data ownership and the ability to demonstrate where AI outputs are coming from. Without that foundation, even the most sophisticated AI capabilities are difficult to defend under scrutiny.
Governance and auditability
Few private equity firms have proven, documented governance of their AI initiatives — and this is where many companies are most exposed.
Governance questions cover how data is treated when using third-party models: whether proprietary data is being used to train external models and whether it's properly segregated from other customers. They also cover decision rights, oversight structures and the auditability of AI outputs – all things lenders, insurers and auditors are paying closer attention to.
How we can help you
INDUSTRY
SERVICE
SERVICE
What AI evidence can do for your valuation
Companies that can connect AI investments to concrete business outcomes are the ones that win deals — and command better terms.
"One IT services company we worked with monetized bespoke AI agents, generating a few thousand dollars per agent per month," Liu said. "That level of monetization demonstrated a maturity many firms lacked, and ultimately helped position the company for a successful bid."
Buyers aren't expecting every company to have fully scaled AI operations. They're looking for evidence that the AI use cases presented are generating real returns, whether through top-line growth, margin improvement or operational efficiency. What matters more than the sophistication of the AI tool are the foundations beneath it.
"Buyers value proprietary data that can be used to develop a better AI-enabled offering, coupled withproducts that are deeply embedded in customer workflows and strong customer relationships,” Liu said.
Key takeaways for sellers
In today's deal environment, buyers aren't paying for AI potential. They’re looking for AI proof. The companies that come to market ready to demonstrate that proof are the ones that will protect their valuation. Here's where to start:
- Know what your AI is actually worth to customers. Test pricing assumptions with customers and competitive benchmarks before coming to market.
- Tie AI impact to business outcomes. Buyers will probe every AI claim for documented results.
- Get your data infrastructure in order. Clean, well-documented data pipelines and clear data ownership are best practice and what buyers and their diligence teams will be looking for.
- Build governance before you need it. Decision rights, oversight structures and audit readiness should be in place well before you go to market.
- Plan for the exit from the start of the hold period. AI that looks strong today may look different in three to five years as the landscape shifts. Buyers are already thinking about that, and sellers should be, too.
Content disclaimer
This Grant Thornton Advisors LLC content provides information and comments on current issues and developments. It is not a comprehensive analysis of the subject matter covered. It is not, and should not be construed as, accounting, legal, tax, or professional advice provided by Grant Thornton Advisors LLC. All relevant facts and circumstances, including the pertinent authoritative literature, need to be considered to arrive at conclusions that comply with matters addressed in this content.
Grant Thornton Advisors LLC and its subsidiary entities are not licensed CPA firms.
For additional information on topics covered in this content, contact a Grant Thornton Advisors LLC professional.
Share with your network
Share