Table of Contents
01
Private equity’s dual reality on AI
02
The PE advantage and the untapped half
03
Where AI is taking hold
04
Conclusion: 
From adoption 
to advantage
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AI in PE: Ahead of the market, behind the curve

Leveraging the ramp AI index
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01

Private equity’s dual reality on AI

AI adoption in PE: 

The half-full, half-empty story

AI adoption among private equity-backed companies is accelerating, but half the market is still on the sidelines.

AI adoption among venture capital vs. private equity vs. all other firms

Share of U.S. businesses with paid subscriptions to AI models, platforms, and tools

VC-Backed: 77%, PE-Backed: 59%, All other: 41%, Source: Ramp proprietary data

PE-backed companies are embracing artificial intelligence faster than most, but not fast enough.

According to the Ramp AI Index, nearly three-fifths (58.8%) of PE-backed businesses have paid subscriptions to AI models, platforms, and tools. That’s meaningfully higher than the 40.8% adoption rate across all other businesses, yet still behind the 77.4% among venture-backed companies.

VC-backed companies lead on AI adoption
+18% Adoption gap - All other companies, +18% Adoption gap - PE-Backed Companies, VC-Backed Companies

That ~18-point gap is not just a difference in tech maturity; it’s a difference in mindset.

Venture-backed firms are born in innovation cycles where AI is foundational. By contrast, PE-backed businesses often operate within established finance functions, legacy systems, and layered governance. Those structures bring rigor and stability, but they can also slow transformation.

For PE, the story is half-full and half-empty. Nearly three-fifths of the portfolio has already made the leap into AI-enabled operations; they are on the path to exploring the next frontier of value creation. That remaining 41% is also significant. These are companies with the capital and urgency to modernize, but which haven’t yet embedded AI into their core finance and operational processes.

For sponsors, that’s not just a statistic; it’s a playbook. The firms that close this gap among their portfolio first will gain a measurable edge in valuation, exit readiness, and fund performance.

02

The PE advantage and the untapped half

PE has a built-in advantage when it comes to AI adoption. Portfolio governance naturally creates accountability. Sponsors can direct change with urgency. And unlike most sectors, there’s capital available to invest in transformative technologies.

But according to Accordion’s 2025 AI in the Finance Function survey, a clear tension has emerged. Ninety-eight percent of sponsors say they’ve told their portfolio CFOs to prioritize AI, but fewer than one in three CFOs have done so, at least in any meaningful way.

And let’s be clear: sponsors see AI adoption as critical to value creation. They know that within the finance function alone, AI can accelerate month-end close cycles by up to 30%, reduce days sales outstanding (DSO) by as much as 15%, and enhance EBITDA performance through faster analysis and better forecasting. Yet CFOs remain cautious. In many cases, it’s not resistance but uncertainty holding them back: 68% say they simply don’t know where to begin or who to turn to for help.

59%
AI adoption rate 
among PE-backed companies
Sponsors are frustrated, but optimistic.
  1. 98% of sponsors have mandated AI adoption
  2. <1 in 3 CFOs have implemented it
  3. 68% of CFOs don’t know where to start

 

The finance function is no longer just the scorekeeper, it’s the decision intelligence hub.

And in many cases, that uncertainty around adoption stems from a more foundational issue: data readiness and governance. PE-backed companies often operate with fragmented ERP environments, inconsistent master data, and varying levels of process discipline. Without strong data governance (clear ownership, standardized definitions, and controls over data quality) AI initiatives stall or produce unreliable outputs.

03

Where AI is taking hold

PE-backed AI adoption by sector in 2025

Tech/Media/Telco: 76% +30 Pts YOY, Business Services: 70% +28 Pts YOY, Financial Services: 60% +20 Pts YOY, Manufacturing: 52% +25 Pts YOY, Healthcare & Life Sciences: 48% +25 Pts YOY, Retail & Consumer: 39% +13 Pts YOY, SOURCE: RAMP AI INDEX

Ramp’s sector-level data reveals that AI adoption among PE-backed firms is spreading rapidly, but unevenly.

Technology, media, and telecommunications predictably lead at 76%. But the most striking story isn’t at the top of the chart; it’s in the middle.

Manufacturing and healthcare, both traditionally slower moving and capital constrained sectors, have shown some of the sharpest acceleration. Over the past year, adoption in manufacturing has climbed from 27% to 52%, while healthcare and life sciences rose from 23% to 48%.

These jumps suggest that AI is no longer the sole domain of digital-first industries. It’s becoming a foundational capability among every portfolio company that needs to do more with less, especially in sectors where efficiency and precision drive value.

At the other end of the spectrum, retail and consumer products remain laggards, with the lowest adoption (39%) and slowest growth. But even there, adoption is rising steadily, a signal that AI has crossed from experimentation into mainstream operational strategy.

THE BOTTOM LINE

AI is now a proxy for operational readiness. The best-performing sponsors no longer see it as a separate technology investment; they see it as a prerequisite for value creation.

The question is no longer whether portfolio companies will adopt AI, but how fast, and how effectively, they will operationalize it.

AI adoption across PE-backed companies is uneven by design. Each sector’s economics, regulatory constraints, and operating cadence shape both the pace of adoption and the type of value AI can unlock.

What unites them is the role of the finance function. While AI adoption spans the enterprise – from operations and commercial teams to IT and HR – the Office of the CFO is emerging as the point where AI-enabled insights are translated into margin, cash flow, and risk outcomes.

Equally important is how AI is adopted, not just where. Successful implementations pair AI deployment with deliberate change management (clear ownership, role clarity, and human-in-the-loop design) to build trust and accelerate adoption.

Increasingly, this includes ESG and sustainability reporting. AI is enabling finance teams to automate ESG data aggregation, improve auditability, and reduce manual compliance effort across portfolio companies, transforming ESG from a reporting burden into a governed, scalable process.

Where ai is taking hold:
Manufacturing
Tech/Media/Telco
Business Services
Financial Services
Healthcare & Life Sciences
Retail & Consumer
Where ai is taking hold:
Manufacturing
Tech/Media/Telco
Business Services
Financial Services
Healthcare & Life Sciences
Retail & Consumer

Manufacturing

Manufacturing has long been considered a technology laggard: heavy on capital, light on digital transformation. That narrative is changing fast.

Ramp’s data shows that AI adoption among PE-backed manufacturers has grown from 27% to 52% in the past year. That puts manufacturing among the fastest movers.

The reasons are clear: supply chain volatility, skilled labor shortages, and global cost pressures are forcing companies to rethink how they plan, forecast, and operate. AI offers an answer: an intelligent layer that brings agility to even the most rigid processes.

For PE sponsors, the value proposition is straightforward. AI-native manufacturers make better, faster, and more defensible decisions. They forecast demand more accurately, price more dynamically, and plan more efficiently. And they do it all with leaner teams.

In short, manufacturing has gone from the slowest adopter to one of AI’s most aggressive accelerators.

52%

of PE-backed manufacturers have adopted AI

+25 pts YOY

Source: Ramp AI Index 

Why it matters

Manufacturing companies are operational engines. Every marginal efficiency gained through forecasting, procurement, or scheduling has a direct impact on EBITDA. For sponsors, that makes AI adoption a pure value creation lever.

The role of the Office of the CFO in this evolution is critical – not as the sole owner of AI, but as the function that aligns operational initiatives to financial outcomes. The finance function is no longer just the scorekeeper. It is, instead, the decision intelligence hub. In AI-enabled manufacturers, finance sits at the center of a feedback loop between operations, procurement, and sales.

By deploying AI-enabled forecasting, cost modeling, and risk analysis, CFOs are creating a new kind of visibility: not just what happened last quarter, but what will happen next…and what levers can change it.

How AI is reshaping the finance function in manufacturing

Sales & operations planning (S&OP):

Intelligent agents now automate forecasting and production planning, shortening planning cycles and improving accuracy.

Financial close acceleration:

Automated reconciliations and anomaly detection reduce manual intervention, compressing month-end close by up to 30%.

Procurement and supplier intelligence:

Algorithms scan and score suppliers, monitor tariff news, and model cost scenarios in real time.

Conversational data analytics:

“Chat with your data” tools: think ChatGPT or Claude-style natural language interfaces applied to enterprise data – allow CFOs to query plant-level performance, margins, and cost variance in seconds, eliminating static reporting lags. These capabilities move finance from reporting results to actively shaping them.

Making it real

In one recent engagement, Accordion worked with a PE-backed industrial company whose finance organization was struggling to support increasingly complex operations. Reporting processes varied by entity, close cycles required extensive manual review, and leadership lacked confidence in consolidated outputs as the business scaled.

Accordion helped the company deploy an AI-enhanced S&OP workflow integrated directly with its finance systems. By standardizing data flows and embedding AI-assisted review into core finance processes, the team improved forecast accuracy by 35%, reduced working capital by 8%, and cut reporting time in half within a single quarter.

Importantly, this transformation didn’t come from robots on the shop floor; it came from automation in the finance suite. By pairing financial process expertise with AI enablement, Accordion helped convert a traditional manufacturing finance team into a predictive command center for the business, strengthening sponsor oversight and improving exit readiness without compromising control.

35% Forecast accuracy improvement, 8% Working capital reduction 
35% Forecast accuracy improvement, 8% Working capital reduction 
35% Forecast accuracy improvement, 8% Working capital reduction 

Where to start

For CFOs in manufacturing, AI adoption doesn’t require a wholesale reinvention. It requires focus.

Start with data readiness: clean, structured, centralized data across systems. Pilot high-impact workflows first: forecasting, close acceleration, or procurement analytics. Align each initiative with sponsor goals. Then measure relentlessly against time savings, error rates, and margin impact.

The companies that take this pragmatic approach today will be the ones sponsors cite tomorrow as AI-native success stories.

Technology, Media & Telecommunications

Technology, media, and telecommunications companies are operating at the leading edge of AI transformation. These businesses were built on digital infrastructure long before AI became mainstream, making them both early adopters and early beneficiaries.

Ramp’s data shows AI adoption in PE-backed TMT companies has surged from 46% to 76% in just one year. This is not experimentation. It’s a structural shift in how these companies operate.

The drivers are clear: massive shared-service footprints, subscription-based revenue models, and constant pressure to improve speed and customer experience. AI fits naturally into that environment.

76%

of PE-backed TMT companies have adopted AI

+30 pts YOY

Highest adoption across all sectors

Source: Ramp AI Index

Why it matters

TMT companies are information engines. Every interaction (customer support, billing, usage, content delivery) creates data. AI allows that data to be processed, interpreted, and acted on in real time.

For PE sponsors, AI adoption in TMT directly supports margin expansion and scalability. Automating high-volume workflows reduces cost-to-serve, accelerates reporting, and shortens decision cycles. More importantly, AI-native TMT operators set a new baseline for performance. Firms that lag risk permanent disadvantage.

The finance function sits at the center of this shift. In AI-enabled TMT organizations, finance is no longer just consolidating results; it is actively shaping operating decisions.

How AI is reshaping the finance function in TMT 

Across PE-backed TMT portfolios, AI is being deployed to:

  • Automate financial commentary and variance analysis, reducing manual narrative preparation for monthly and board reporting
  • Accelerate the close process through intelligent reconciliations and anomaly detection
  • Enable conversational analytics allowing leaders to query subscription metrics, churn, and profitability on demand
  • Improve revenue and churn forecasting by modeling usage patterns and customer behavior
  • Analyze cost-to-serve across support, billing, and shared services functions

Making it real

In a recent engagement, Accordion partnered with a PE-backed technology and services company whose finance team was struggling to keep pace with close-period reporting demands. Commentary preparation was highly manual, variance analysis required significant effort, and reporting timelines were stretching as the business scaled.

Accordion implemented an integrated, agentic AI workflow that pulled data directly from core finance systems, performed variance analysis, and automatically drafted executive-ready financial commentary. Finance leaders remained firmly in control, reviewing and approving outputs through a human-in-the-loop interface embedded in the reporting process.

By automating up to 80% of the reporting workflow (from data extraction through commentary draft and deck assembly), the finance team reduced manual effort, accelerated close timelines, and delivered more consistent, executive-ready insights to management and the board.

As in other sectors, the impact came from removing friction in the finance workflow rather than replacing judgment. By embedding AI directly into the close process, Accordion helped shift the TMT finance function from assembling reports to interpreting results, increasing bandwidth for forward-looking analysis and decision support.

Where to start

For CFOs in TMT, AI adoption should begin with frequency and scale. Start with workflows that occur every month, or every day, such as commentary, reconciliations, and KPI analysis. Ensure financial and operational data is centralized, then expand into forecasting and decision support once early wins are proven.

Business Services

Business services companies scale through repeatability. Their value creation depends on standardized workflows, shared services, and disciplined execution across large client bases.

Ramp’s data shows adoption rising sharply in a year (from 42% to 70%), reflecting a shift away from labor-intensive models toward AI-augmented service delivery. In a sector where margins are driven by utilization and pricing discipline, AI has become a core operating capability.

70%

of PE-backed business services companies have adopted AI

+28 pts YOY

Adoption rose sharply, shifting away from labor intensive models

Source: Ramp AI Index

Why it matters

In business services, small inefficiencies compound quickly. Slight improvements in utilization or pricing discipline can materially affect EBITDA.

AI enables sponsors to break the traditional trade-off between growth and overhead. Firms can scale output without scaling headcount, while maintaining visibility into performance at the client, project, and contract level.

Finance plays a central role in this transformation, acting as the lens through which service economics are optimized.

How AI is reshaping the finance function in business services

PE-backed services firms are using AI to:

  • Automate utilization and margin reporting across clients and service lines
  • Support pricing and proposal decisions by analyzing historical performance and win rates
  • Reduce billing leakage through automated validation and reconciliation
  • Improve resource and capacity forecasting to align staffing with demand
  • Accelerate close and reporting cycles freeing finance teams for higher-value analysis

AI turns finance into a real-time control tower for service delivery economics. 

Making it real

In a recent engagement, Accordion partnered with a PE-backed business services provider that was experiencing rising customer churn and limited visibility into emerging risk. Customer signals were spread across operational systems and unstructured communications, leaving finance leaders with little ability to intervene before churn began to impact revenue.

Accordion deployed an integrated GenAI and machine learning workflow that analyzed customer sentiment from unstructured communications and combined those insights with operational and account-level data. By embedding this intelligence directly into the finance and commercial reporting environment, leadership gained an earlier, more actionable view of account health and underlying service issues.

This approach enabled teams to prioritize retention efforts on the highest-risk accounts and intervene before issues escalated. As a result, the organization achieved meaningful churn reduction, retained recurring revenue across the platform, and strengthened margin durability.

The impact came from equipping frontline teams with better signal to drive retention, rather than simply replacing them. By translating customer sentiment into actionable financial and operational insight, Accordion helped shift the business services organization from reactive churn management to proactive, data-driven retention, improving forecast confidence and sponsor visibility.

Where to start

CFOs should begin with billing, utilization, and margin workflows: areas that are highly repeatable, data-rich, and tightly linked to sponsor value creation. Early success builds confidence for broader AI deployment.

Financial Services

Financial services firms operate in environments defined by regulation, and risk. AI adoption in the sector has been measured rather than explosive, reflecting the need for accuracy, explainability, and control.

Ramp’s data shows steady momentum as firms move beyond experimentation and begin embedding AI into core finance and risk processes.

60%

of PE-backed financial services companies have adopted AI

+20 pts YOY

AI adoption gaining momentum

Source: Ramp AI Index

Why it matters

For PE sponsors, AI adoption in financial services is about risk-adjusted returns. Improvements in fraud detection, reconciliation accuracy, and regulatory reporting reliability directly protect value. AI also reduces dependence on manual oversight in complex environments, improving scalability and resilience — key attributes for exit readiness.

The finance function is the natural home for this transformation, balancing innovation with governance.

How AI is reshaping the finance function in financial services

Across PE-backed financial services firms, AI is being applied to:

  • Automate regulatory and compliance reporting workflows
  • Detect anomalies and potential fraud across high-volume transactions
  • Accelerate close processes through intelligent reconciliations
  • Support risk and scenario modeling for credit and liquidity planning

These tools allow finance teams to focus on oversight and decision-making rather than manual review. 

Making it real

In a recent engagement, Accordion partnered with a PE-backed business services platform experiencing rising customer churn and limited visibility into emerging risk. While leadership suspected retention issues were developing, finance teams lacked early warning signals because customer risk indicators were dispersed across operational systems and unstructured communications.

Accordion implemented an integrated GenAI and machine learning solution that analyzed customer sentiment from unstructured communications and combined those insights with utilization and account-level performance data. By consolidating this information into the finance and commercial reporting environment, the organization created a single, actionable view of customer health that could be monitored consistently across the platform.

This enhanced visibility allowed teams to identify at-risk customers earlier, prioritize retention efforts based on financial impact, and intervene before churn translated into lost revenue. As a result, the business strengthened margin durability, retained recurring revenue, and improved forecast confidence for sponsors, shifting from reactive churn management to proactive, data-driven retention.

Where to start

Begin with rules-based, high-volume processes where AI can improve speed and accuracy without increasing risk. Strong data governance is a prerequisite for scaling adoption.

Healthcare & Life Sciences

Healthcare and life sciences organizations are accelerating AI adoption under mounting pressure from labor shortages, reimbursement complexity, and regulatory scrutiny. What was once viewed as a clinical or IT innovation is now being pulled decisively into the finance function.

Ramp’s data shows one of the sharpest year-over-year increases in AI adoption across all sectors. That acceleration is not driven by experimentation, but by necessity (particularly in revenue cycle management, where inefficiencies translate directly into cash flow risk).

48%

of PE-backed healthcare and life sciences companies have adopted AI

+25 pts YOY

one of the sharpest increases in AI adoption across all sectors

Source: Ramp AI Index

Why it matters

For PE sponsors, healthcare value creation is inseparable from revenue cycle performance. Delays in coding, claim submission, denial management, and collections can materially erode EBITDA, extend working capital cycles, and introduce volatility into otherwise stable businesses.

AI changes the economics of RCM by bringing predictive visibility to processes that have historically been reactive and labor-intensive. Rather than identifying problems after cash is delayed, AI allows finance teams to anticipate where revenue will stall and intervene earlier.

The Office of the CFO sits at the center of this shift, working in concert with operations, commercial leadership, and technology teams. In AI-enabled healthcare organizations, finance is no longer downstream of clinical operations; it becomes the connective tissue between patient activity, payer behavior, and cash realization.

How AI is reshaping the finance function in healthcare

Denial prediction and prevention:

Models analyze historical claims, payer behavior, and documentation patterns to flag high-risk claims before submission, allowing teams to correct issues proactively rather than chase denials after the fact.

Intelligent claims prioritization:

AI dynamically ranks claims based on dollar value, denial probability, and payer responsiveness, ensuring staff focus on the work that accelerates cash most effectively.

Root-cause analysis of revenue leakage:

Algorithms identify systemic issues in coding, authorization, or documentation that drive recurring denials across sites or service lines.

Cash forecasting and DSO modeling:

AI improves visibility into expected collections by payer and service line, supporting more accurate liquidity planning and sponsor reporting.

Conversational RCM analytics:

CFOs and revenue leaders can query denial trends, payer performance, and aging in real time, without waiting for static reports.

Together, these capabilities move healthcare finance from backlog management to forward-looking control.

Making it real

In a recent engagement, Accordion partnered with a PE-backed healthcare services organization operating across a multi-entity platform and facing growing pressure on revenue cycle performance. Fragmented clinical, billing, and finance data limited visibility into denial risk and cash flow timing, making it difficult for leadership to anticipate issues before they impacted liquidity and sponsor reporting.

Accordion deployed AI-enabled revenue cycle intelligence that integrated data across entities and applied denial prediction, intelligent work prioritization, and automated root-cause analysis. By embedding these capabilities directly into finance workflows, the organization reduced manual review effort, accelerated cash conversion, and improved forecast accuracy, while maintaining auditability and compliance in a highly regulated environment.

The result was a shift from reactive denial management to proactive revenue control, giving finance leaders and sponsors clearer, earlier insight into cash flow trends and strengthening confidence in exit readiness.

Where to start

For healthcare CFOs, AI adoption should begin squarely in the revenue cycle.

Start by integrating clinical, billing, and finance data into a single, analyzable layer. Pilot AI models focused on denial prediction, claims prioritization, or cash forecasting. These are use cases with fast, measurable ROI. Once visibility improves, extend automation into close and consolidation workflows.

Retail & Consumer

Retail and consumer companies operate in a margin-sensitive environment shaped by demand volatility and inventory risk. Despite these pressures, adoption lags other sectors, creating both risk and opportunity.

39%

of PE-backed financial services companies have adopted AI

+13 pts YOY

AI adoption lagging 
behind other sectors

Source: Ramp AI Index

Why it matters

In retail, small forecasting errors cascade into stockouts, markdowns, and working capital drag. AI enables more accurate demand planning, pricing discipline, and inventory management.

For sponsors, these improvements compound quickly across large store and SKU footprints.

How AI is reshaping the finance function in retail

Retail finance teams are deploying AI to:

  • Automate demand and sales forecasting
  • Perform margin bridge analysis across products and channels
  • Optimize inventory positioning and reorder points
  • Forecast labor needs based on expected demand
  • Generate financial commentary at scale for large store networks

Finance becomes a forward-looking partner to merchandising and operations.

Making it real

In a recent engagement, Accordion partnered with a PE-backed retail and consumer company seeking to improve promotional effectiveness and inventory planning in a highly margin-sensitive environment. The company deployed a machine learning–driven sales forecasting and promotion optimization model that combined historical sales, promotion calendars, product attributes, seasonality, and inventory data to forecast daily demand and simulate promotion outcomes before execution.

With more accurate demand forecasting and promotion scenario analysis, the retailer was able to identify underperforming campaigns, refine discount strategies, and improve inventory positioning. Finance and merchandising teams gained a shared, data-driven view of how promotions impacted revenue, margin, and working capital, reducing excess inventory while mitigating stockout risk.

As in other sectors, the transformation was not about replacing decision-makers, but about improving the quality of decisions. By embedding predictive forecasting and promotion analytics directly into the planning process, Accordion helped shift the retail organization from reactive promotion management to proactive, margin-aware execution, strengthening cash flow discipline and improving the return on promotional investment.

Where to start

Begin with demand forecasting and margin analysis, then expand into inventory and labor modeling once data foundations are in place.

Healthcare organizations that take this approach don’t just improve RCM efficiency, they stabilize cash flow, reduce operational risk, and materially strengthen exit readiness.

04

Conclusion:
From adoption to advantage

Ramp AI Index makes one thing clear: 
AI has crossed the threshold from experimentation to expectation. Across every sector, adoption is accelerating, and the gap between leaders and laggards continues to widen. The firms moving fastest are not simply deploying new tools; they are rethinking how decisions get made.

What separates the winners is not technology alone. It is operationalization: pairing technology deployment with disciplined change management, clear ownership, human-in-the-loop design, and accountability for adoption at the workflow level.

In practice, the same pattern emerges across manufacturing, TMT, business services, financial services, healthcare, and retail. The highest-impact AI use cases are enterprise-wide, but the value they create is most clearly realized, governed, and scaled through the finance function. Forecasting, close, revenue cycle management, margin analysis, and risk modeling shape cash flow, EBITDA, and exit readiness – areas where AI delivers the most meaningful value.

In other words: AI is a core value-creation lever that must be driven with the same rigor as any other operational transformation. This requires more than just software. It requires clean data, disciplined processes, governance, and a clear roadmap from use case to outcome. Most CFOs understand where they need to go. Many simply do not know how to begin.

That is where Accordion comes in.

The bottom line:

Accordion partners with PE sponsors and portfolio company finance teams to turn AI ambition into measurable results. The focus is embedding AI into the workflows that matter most, aligning initiatives to sponsor priorities, and delivering impact that shows up in cash flow, margins, and valuations.

The question for sponsors is no longer whether to act. It is how quickly they can move from AI adoption to AI advantage.

Methodology

AI in PEAccordion and Ramp partnered to extend Ramp’s existing AI Index, which tracks adoption across all businesses, into a version focused specifically on private equity–backed companies. This PE-backed Ramp AI Index measures adoption using observed transaction data, rather than surveys. The sample pulls from billions of dollars in corporate spend using data from Ramp’s corporate card and bill pay platform. It cross-references Ramp’s dataset of tens of thousands of U.S. businesses with ownership sources to identify PE-backed firms.

The index builds on previous work by Bonney et al. (2024) by providing a new dataset to measure firm adoption of artificial intelligence. Previous work has relied almost entirely on surveys that ask businesses if they use AI, but surveys may lead to underreporting of actual AI adoption, particularly when questions are unclear or when adoption of a new technology is rising very quickly. Using contract and transaction data from corporate spend with AI companies, Ramp produced a more timely and accurate measurement of AI adoption by U.S. businesses.

AI in the Finance FunctionThis report also includes statistics from Accordion’s AI in the Finance Function survey, which was conducted in conjunction with Wakefield Research, among 400 total participants—including 200 private equity (PE) sponsors (senior executives) and 200 chief financial officers (CFOs) at private equity-backed companies with $50 million or more in annual revenue. The CFO and PE sponsor samples were collected in Q4 2025, using an email invitation and an online survey.

If you’re ready to close that gap, now is the time to start a conversation with Accordion.

Reach out at AI@accordion.com.

If you want to learn more about how the most efficient businesses are utilizing AI, reach out to Ramp at jschneider@ramp.com.

Tell us where you need help

FAQs

How does AI adoption in private equity compare to other markets?

Private equity is ahead of the broader market but still lags behind venture capital. About 59% of PE-backed companies have adopted AI, compared to 77% of VC-backed companies and 41% of others. This gap isn’t driven by technology limitations—it reflects a difference in mindset. VC firms tend to move quickly and experiment, while PE firms are more deliberate and operationally focused.

Why is there a gap between AI mandates and actual implementation in PE portfolios?

While 98% of PE sponsors have mandated AI adoption, only about half of their portfolio companies are actively implementing it. The gap is even more pronounced in finance: fewer than one in three PE-backed CFOs have meaningfully implemented AI, and 68% report not knowing where to start. This disconnect is a major barrier to realizing value—but it is solvable with the right approach and guidance.

Where should companies start to capture real value from AI?

The finance function—specifically the Office of the CFO—is the highest-leverage starting point for AI. This is where AI can directly drive EBITDA, improve cash flow, and enhance exit readiness. However, many companies are starting elsewhere, missing the opportunity to create immediate, measurable impact.

Which industries are seeing the fastest acceleration in AI adoption?

Some of the biggest gains are coming from traditionally non-digital sectors. Manufacturing increased AI adoption from 27% to 52% in one year, and healthcare from 23% to 48%. These industries are accelerating not because they are tech-forward, but because economic pressures are forcing rapid adoption.

Where can I find the Accordion and Ramp white paper on AI in private equity?

The Accordion × Ramp white paper and report — AI in PE: Ahead of the Market, Behind the Curve — is available in full on this page. The report draws on the Ramp AI Index, which tracks real AI adoption across PE-backed companies using observed transaction data, and pairs it with findings from Accordion’s 2025 AI in the Finance Function survey of 400 PE sponsors and CFOs.