Target evaluation, value creation, integration are evolving
Executive summary
AI is reshaping every stage of M&A, from deal sourcing through post-close integration. Grant Thornton’s 2026 Q2 CFO survey found that 52% of finance leaders use AI for diligence analysis, while 60% cite technology and AI-driven transformation as their top value creation priority for the next 12 to 24 months. Finance leaders who have built AI into their M&A approach are gaining an edge in how they evaluate targets, create value and integrate acquisitions. Those who treat it as an afterthought risk overpaying and missing gains that competitors are already capturing.
Introduction
M&A activity is picking up. Forty-two percent of finance leaders in Grant Thornton’s 2026 Q2 CFO survey expect their organization's M&A activity to increase over the next 12 months, while just 6% expect a decline. But more activity doesn’t mean more confidence. Buyers and sellers alike are recalibrating how they evaluate, price and execute transactions — and AI is at the center of that recalibration.
Three years of market disruptions, from banking failures to tariff uncertainty, have trained dealmakers to be selective. AI has added another layer of complexity. It’s changing how buyers assess whether an acquisition target will hold its value, how sellers demonstrate operational upside and how acquirers plan for integration.
“Executives on the buy side and sell side need to get comfortable with evaluating where technology can create real enterprise value post-close,” said Grant Thornton | Stax Global Practice Lead Paul Edwards. “AI enablement is a means for you to bridge gaps in effectiveness, efficiency and potential returns within businesses.”
Recognizing where AI is transforming M&A deals helps CFOs gain ground.
Buyers are cautious about assets AI could disrupt
The M&A market has shifted toward disciplined organic growth and away from new geographies and product lines that can cause dramatic multiple expansion. Private equity firms and corporate acquirers are underwriting value creation around what a business already does well — optimizing prices, expanding customer relationships and cross-selling.
AI has accelerated that shift. Buyers are wary of paying premium multiples for assets that AI could fundamentally reshape. A technology platform that commands strong margins today could face an AI-native competitor within months. That uncertainty is steering capital toward businesses where AI enhances operations rather than threatens the core business model.
“Good deals are getting done and they’re being heavily chased by private equity firms,” Edwards said. “Those tend to over-index toward services companies, where there’s a people component and where you can look at AI as an optimizer for a business rather than something to protect against.”
Building conviction around targets where AI serves as an operational accelerator can help finance leaders close deals with confidence.
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CFOs need to own the AI KPI conversation
A year ago, CFOs at some companies could address AI in a single slide during a board presentation or lender meeting. That era is over. AI now requires its own chapter — one that covers what the organization has done, what it has learned, how it is scaling and what returns it’s generating.
“Every C-suite leader needs to become a CFO right now,” Edwards said. “They need to know the KPIs they’re trying to achieve for business improvement, growth and AI adoption.”
This shift has direct M&A implications. Sellers who can quantify their AI investments with defined ROI, proven use cases and measurable outcomes are commanding attention from buyers. Buyers who can evaluate those claims are making smarter bids. Boards are asking sharper questions about how AI fits into every acquisition thesis.
“A business running 25 AI pilots doesn’t have a strategy,” Edwards said. “A business running two or three does. CFOs are moving away from spreading AI thin and favoring proven use cases with a defined ROI and defined KPIs. That’s where the real value shows up.”
Finance leaders who can connect their AI initiatives to business outcomes — and explain those connections to a board — are positioning their organizations as stronger acquirers and more attractive acquisition targets. That ability is becoming a differentiator in every deal process.
AI is reshaping how deals get done
AI in M&A serves as a filter and an enabler. AI is handling the labor-intensive work of sourcing, screening and diligence, allowing finance leaders to zero in on the strategic questions that determine whether a deal creates value.
Grant Thornton’s survey data demonstrates AI’s impact. Fifty-two percent of finance leaders say the top use for AI in M&A is diligence analysis, including quality of earnings analysis, forecasting and anomaly detection. Forty-eight percent cite value creation planning. And 43% say they're using AI for target screening or deal sourcing.
”We’re seeing CFOs use AI to source and filter the right assets and better understand the drivers of business performance,” Edwards said. "That allows leadership teams to focus diligence on questions that matter most and make more informed investment decisions.”
On the buy side, AI is making deal evaluation more precise. Finance leaders are defining key investment criteria — customer acquisition cost, retention rates, churn and same-customer revenue growth — and deploying AI tools that can scan an entire data trove against those metrics in minutes.
“You can define eight or 10 KPIs and your AI solution will analyze volumes of data and tell you how a business is doing against those metrics in minutes,” Edwards said. ”We’re dealing with far more informed clients who say, ‘I don’t need to boil the ocean. I need to answer three questions.’”
On the sell side, AI is becoming a tool for demonstrating future value. AI pilots give sellers an opportunity to show buyers the potential for growth that exists through scaling AI applications.
CASE STUDY
M&A sellers use AI to show growth capabilities
A regional dental service organization (DSO) with about 200 locations preparing for sale piloted AI optimization in just two or three locations, focusing on:
- Enhancing client communication to reduce missed appointments
- Managing professionals’ time and calendars for better utilization
Within three months, practitioner utilization rose by 12% and missed appointments dropped by 22%. Those proof points became part of the investment case — showing buyers the operational upside still available across the remaining locations, which was estimated at $200 million in revenue and $80 million in cost savings.
“Sellers are putting this in front of buyers as part of the investment case,” Edwards said. “They’re saying, ‘Look at all the juice left to squeeze on AI optimization.’ If you’re a CFO buying that business, you’d better understand whether you can replicate what they've demonstrated.”
Value creation runs through AI
Value creation has become the primary source of returns in private equity, and AI is central to that strategy. Sixty percent of finance leaders in Grant Thornton’s survey say technology and AI-driven transformation is their top value creation priority over the next 12 to 24 months — far exceeding any other priority.
“Private equity is no longer underwriting multiple expansion the way it used to,” Edwards said. “It’s underwriting value creation around organic growth. The businesses growing faster than their competition organically are the ones getting rewarded at exit.”
Private equity firms are driving AI improvements through their portfolio companies with a focused playbook: Identify two or three high-value use cases, pilot them quickly and scale what works. The emphasis is on measurable operational gains with clear proof points that translate into higher valuations.
“You don't have to build out AI thoroughly before you sell,” Edwards said. “But being able to quantify it is essential.”
When CFOs can demonstrate how AI investments translate into margin improvement, revenue acceleration or operational efficiency, they command premium valuations and stronger buyer interest.
AI can reduce integration friction
Integration challenges remain the top area where value creation falls short after acquisitions, identified by 53% of finance leaders in Grant Thornton's survey who participate in M&A. The problem is familiar, but AI is opening new approaches to solving it.
“Integration issues are typically a failure of diligence,” Edwards said. “You need to know enough about the business to understand how to own it and how to optimize it. Too often, people decide to buy without thinking through those next two pieces — and the integration is practically dead on arrival.”
Traditionally, integration has required pulling disparate systems, processes and teams into a single operating model. That is expensive, time-consuming and often disruptive. AI offers an alternative. Rather than forcing full integration, organizations can deploy an AI layer between acquired and existing systems that pulls information consistently across both.
“AI is going to do a lot for integration ,” Edwards said. ”Rather than pulling everything together with all the complexity that comes with that, you can manage systems side by side while an AI layer pulls information more consistently. That makes it far less painful, from people and culture through to systems.”
For acquirers who have struggled with integration, this is a meaningful shift. AI can reduce the friction, cost and timeline that have historically eroded post-deal value.
Key takeaways
Finance leaders who want to capture AI's advantages across the M&A lifecycle can start with these five actions:
Evaluate every target through an AI lens: Assess whether AI enhances or threatens each acquisition target’s core business model. Steer capital toward businesses with strong organic growth where AI serves as an operational accelerator — particularly services-oriented companies with a people component that AI can optimize rather than replace.
Build a board-ready AI narrative with defined KPIs: Move beyond slide-level AI summaries and develop a full chapter for board and lender presentations that covers what the organization has done with AI, what it has learned, how it is scaling and what returns it is generating.
Deploy AI across the deal process to sharpen evaluation: Define key investment criteria and use AI tools to scan data rooms against those metrics in minutes. On the sell side, pilot AI optimization in a small number of operations, capture the proof points and present the remaining upside as part of the investment case.
Make AI the centerpiece of your value creation plan: Identify two or three high-value AI use cases within portfolio companies or acquisition targets, pilot them quickly and scale what works. Quantify AI-driven gains in margin improvement, revenue acceleration or operational efficiency — and present those proof points to buyers or boards as part of the value story.
Use AI to simplify post-close integration: Rather than forcing full system integration, deploy an AI layer between acquired and existing operations that pulls information consistently across both. This approach reduces the cost, complexity and timeline of integration — the area where 53% of finance leaders who participate in M&A say value creation falls short.
AI has moved from a secondary factor in M&A to a defining one. It's shaping how buyers evaluate risk, how sellers present opportunity and how acquirers plan for integration. Grant Thornton’s survey data shows that finance leaders who are embedding AI across the deal lifecycle are gaining advantages their competitors can't replicate. The M&A playbook has changed. Recognizing that shift — and acting on it — helps finance leaders capture the value ahead.
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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.
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