Eighteen months ago, private equity firms approached artificial intelligence with the caution typically reserved for unproven technologies in frothy markets. The conventional wisdom held that AI investments were better suited for venture capital’s risk appetite than PE’s disciplined, returns-focused approach. The sector’s mantra echoed across Sand Hill Road and Greenwich: “Let the VCs take the risk. We’ll buy the winners when revenues and EBITDA become clear.”
That sentiment has evaporated with remarkable speed. Through the first half of 2025, private equity firms announced or completed 155 AI-related deals—a 49% surge from just 104 deals in H1 2024. This isn’t gradual adoption. It’s a wholesale sector transformation driven by converging pressures that make AI investment no longer optional but existential.
The drivers are stark. PE firms hold more than 30,000 portfolio companies, with 47% acquired since 2020, many now aging past optimal exit windows. Limited partners are demanding liquidity from a generation of funds approaching the end of their lifecycles, with 1,607 funds requiring wind-down by 2026. Traditional exit channels—IPOs and strategic M&A—remain constrained by macroeconomic headwinds. Meanwhile, AI is disrupting industries faster than PE can reposition portfolio companies, creating an imperative to either integrate AI or face obsolescence.
This analysis examines how private equity evolved from AI skeptic to AI evangelist in barely eighteen months, the specific infrastructure and operational plays driving returns, how firms are deploying AI to accelerate portfolio exits, and the emerging playbook for PE firms navigating this transformation.
The Numbers Tell the Story: A 49% Surge in 18 Months
The quantitative evidence of private equity’s AI pivot is unambiguous. Ropes & Gray’s H1 2025 Global AI Report documents 155 PE deals involving AI targets through June 2025, compared with 104 deals in the same period of 2024. This 49% year-over-year increase represents not merely incremental growth but fundamental reallocation of capital deployment strategy.
Deal value metrics reveal even more dramatic shifts. While venture capital deal count for AI rounds is projected to finish 2025 down 12% year-over-year—primarily reflecting broader VC market headwinds—venture dollars invested in AI are on pace to exceed all previous years. Strategic M&A involving AI targets grew 33% in volume and 123% in value through H1 2025 compared to the prior year. Megadeals exemplify this aggressive capital reallocation: OpenAI’s $6.5 billion acquisition, Meta’s $14.3 billion investment in Scale AI, and the landmark $40 billion Aligned Data Centers buyout.
These figures understate PE’s total AI exposure because they measure only direct AI company acquisitions. Private equity’s actual AI investment extends far deeper through infrastructure plays, add-on acquisitions bolstering existing portfolio company capabilities, and operational AI deployment across thousands of portfolio companies.
The geographic concentration is notable. The United States accounted for 47% of AI deal volume across M&A, PE, and VC in H1 2025, but an impressive 83% of total transaction value. This suggests not only that the U.S. leads in AI innovation but that the largest, most capital-intensive AI opportunities remain concentrated in American markets, driven by the robust startup ecosystem and accessible resources for capital-intensive innovation.
The Sectoral Breakdown
Software and services dominated AI deal activity on both volume (54%) and value (68%) bases across M&A, PE, and VC in H1 2025. This concentration is unsurprising; software companies provide the applications layer where AI capabilities translate most directly into revenue and margin expansion.
Healthcare and life sciences ranked second by deal volume but fourth on total value due to prevalence of smaller transactions and deals with undisclosed values. The healthcare AI opportunity remains substantial—drug discovery, diagnostic tools, personalized medicine platforms—but the regulatory complexity, longer commercialization timelines, and scientific validation requirements make these less attractive to PE’s typical holding periods.
Robotics and hardware, despite ranking fourth by deal count, commanded the second-highest total deal value in H1. This reflects the capital intensity of AI infrastructure buildout. Data centers, semiconductor manufacturing, and specialized AI compute hardware require enormous upfront investment but promise steady, long-duration cash flows that align perfectly with PE’s investment thesis.
The sectoral analysis reveals PE’s strategic calculation: invest heavily in infrastructure enabling the AI ecosystem while selectively pursuing software applications with proven revenue models and demonstrated product-market fit.
The Strategic Shift: From Risky Bets to Proven Infrastructure
Private equity’s AI strategy differs fundamentally from venture capital’s approach. Where VCs place high-risk bets on early-stage startups developing novel AI capabilities, PE firms have demonstrated clear selectivity, preferring mature companies with proven use cases and financial performance.
This philosophical divergence manifests in the types of deals PE pursues. Instead of funding foundation model development or speculative AI applications, PE firms are deploying capital into the data infrastructure investments needed to support AI infrastructure buildout. As Marc Lipschultz, Co-CEO of Blue Owl, articulated in June 2025: “We don’t want any of those risks for our investors. If you believe that 10 years from now, AI will be an important part of the fabric of IT or the way we operate this economy, then you want the picks and shovels, you want the infrastructure that goes with it.”
The Infrastructure Play: Data Centers as the New Real Estate
Data centers have emerged as PE’s preferred AI exposure, combining predictable cash flows with explosive demand growth. The $40 billion Aligned Data Centers acquisition—involving BlackRock, NVIDIA, Microsoft, and other consortium members—exemplifies this trend. The deal structure calls for deploying $30 billion of equity capital initially, with potential total value reaching $100 billion including debt.
The investment thesis is straightforward. AI model training and inference require exponentially more compute than traditional workloads. ChatGPT training alone consumed approximately 10,000 NVIDIA GPUs running for weeks. Scaling to multiple foundation models, plus millions of daily inference requests, creates insatiable demand for data center capacity. This demand translates into long-term power purchase agreements, multi-year tenant contracts, and expansion optionality that PE understands deeply from decades investing in infrastructure.
PE investors view data centers as stable, long-term investments generating recurring revenues even amid macroeconomic volatility. Large asset managers with established infrastructure platforms are capitalizing on data center tailwinds. Brookfield Asset Management has already built 2,000 megawatts of data center capacity and positions AI infrastructure buildout as “one of the largest capital formation cycles of this generation.”
The data center play extends beyond traditional rack space. Power generation, cooling systems, and network infrastructure all require massive capital deployment. PE firms with experience in energy infrastructure, utilities, and telecommunications can leverage existing capabilities while accessing a market projected to grow dramatically as AI adoption accelerates.
Add-On Acquisitions: Bolstering Portfolio Companies
PE’s AI strategy isn’t limited to platform investments in AI companies. Firms are aggressively pursuing add-on acquisitions that bolster portfolio company capabilities to compete against AI disruptors. Since 2020, PE firms have steadily increased add-on activity in the AI/ML sector, with the market peaking in 2023 at more than 50 add-on deals.
These acquisitions serve multiple purposes. First, they accelerate portfolio companies’ digital transformation by bringing AI capabilities in-house rather than developing them organically. Second, they enhance competitive positioning against AI-native startups that might otherwise disrupt established markets. Third, they create exit narratives demonstrating to potential buyers or IPO investors that portfolio companies have embraced AI rather than being threatened by it.
The add-on strategy aligns with PE’s operational value creation playbook. Firms can deploy best practices across multiple portfolio companies, creating a repeatable framework for AI integration. Vista Equity Partners exemplifies this approach, requiring each of its 85-plus portfolio companies to submit quantified goals and benefits from generative AI initiatives as part of annual operational planning.
The Mature Company Preference
PE’s preference for mature AI companies with proven use cases reflects risk-adjusted return calculations. Early-stage AI startups face substantial technical, market, and regulatory uncertainties. Will the model architecture prove superior? Can the company acquire sufficient training data? Will customers actually pay for the solution? These questions create volatility incompatible with PE’s return requirements.
Mature AI companies have answered these questions. They have paying customers, demonstrated revenue growth, and established competitive moats through proprietary data, switching costs, or network effects. Their valuations may be higher than early-stage startups, but the probability of achieving projected returns is correspondingly greater.
This selectivity manifests in deal metrics. AI deals command premium valuations—25.8x revenue multiples—but PE firms conducting rigorous due diligence focusing on unit economics, customer retention, and path to profitability. The questions PE asks differ from VC: What’s the customer acquisition cost? How long is the sales cycle? What’s the gross margin after accounting for compute costs? Can margins expand as revenue scales?
The mature company focus also reflects LP expectations. After years of compressed distributions, LPs demand that new investments generate returns within reasonable timeframes. Deploying capital into speculative AI startups that may not generate cash flows for years conflicts with the immediate need to demonstrate progress and build track records supporting next fund raises.
The Aging Portfolio Crisis: 30,000 Companies and Counting
Private equity’s accelerated AI adoption isn’t driven solely by offensive opportunity. It’s equally propelled by defensive necessity arising from an unprecedented portfolio aging problem that threatens the sector’s fundamental business model.
The Numbers Are Stark
By March 2025, PE firms were holding more than 30,000 portfolio companies, with nearly half—47%—acquired since 2020. Even as median exit size hit new highs in Q2 2025, the inventory of PE-backed companies grew to 12,552, equivalent to 8.5 to 9 years of exits at recent rates. This backlog remains a central challenge, pressuring returns and fundraising even as exit activity shows nascent recovery signs.
The holding period statistics tell the story clearly. The median holding period for PE-backed portfolio companies reached 5.8 years in 2025, the longest since Private Equity Info began tracking this metric in 2000. Over 30% of PE-backed companies have been held by fund managers for at least five years, the highest percentage in nearly a decade. The average hold reached 8.5 years in 2024, more than double the 4.1 years observed in 2007.
These aren’t abstract statistics. Each additional year holding a portfolio company creates cascading problems. Funds approaching their contractual termination dates face pressure to exit at potentially suboptimal valuations. LPs demanding distributions to fund new commitments grow increasingly impatient. Management teams at portfolio companies, promised liquidity events that continue postponing, become demoralized or leave for opportunities offering clearer paths to exit.
The COVID Hangover
The current backlog stems substantially from the 2020-2021 deal vintage. PE firms deployed record capital during this period, often at peak valuations, believing that post-COVID economic recovery would create robust exit markets by 2024-2025. Reality has proven different.
The macro environment that enabled those deals—near-zero interest rates, quantitative easing, exuberant public markets—evaporated. The Federal Reserve’s aggressive rate hiking campaign to combat inflation made leveraged buyouts expensive and widened bid-ask spreads between sellers’ valuation expectations and buyers’ willingness to pay. IPO markets, a preferred exit route historically, remain well below pre-pandemic levels due to weak investor appetite, volatile public markets, and new regulatory hurdles.
Historical patterns suggest recovery timelines stretch longer than optimists hoped. Following major economic shocks—the dot-com bust, the Great Recession, COVID—PE firms typically require 4 to 6 years to normalize holding periods. These events historically increased median holding periods by 1.0 to 1.5 years. Portfolio companies acquired at peak valuations just before economic shocks face particularly extended holds, sometimes 8 to 10 years, as sponsors work to grow into stretched entry multiples.
The 2025 exits showing a median hold of approximately 6.0 years reflect companies acquired just before COVID in 2019. This implies the full inventory of 2020-2021 vintage companies—bought at even higher valuations—has yet to flow through the exit pipeline. The backlog problem will worsen before it improves unless PE firms find creative solutions.
The LP Liquidity Pressure
The portfolio aging problem intersects with fundraising challenges to create intense pressure on GPs. Institutional LPs—pension funds, endowments, foundations, insurance companies—commit capital to PE funds expecting distributions within 10 to 12 years. Extended holding periods delay those distributions, leaving LPs over-allocated to private equity as a percentage of total portfolios and unable to make new commitments to next-generation funds.
This dynamic has tangible consequences. Private equity fundraising in H1 2025 fell as a percentage of overall U.S. private capital raised. While overall PE fundraising was down compared to H1 2024, growth equity capital raising increased, suggesting LPs are prioritizing strategies offering quicker liquidity over traditional buyouts with uncertain exit timelines.
GPs face a paradox. They need to exit portfolio companies to raise new funds, but market conditions make attractive exits difficult. Traditional mechanisms—IPOs, strategic sales—remain constrained. The alternative—accepting lower returns through distressed sales—damages track records and makes future fundraising even harder.
The pressure manifests in GP behavior. According to Bain’s 2025 Global Private Equity Report, contributions from LPs have equaled or outweighed fund distributions in five of the last six years. The industry broke even on cash flow in 2024, but the cumulative deficit created by years of delayed exits creates urgent need for liquidity solutions.
The AI Disruption Threat
Overlaying the portfolio aging and LP pressure problems is the accelerating threat of AI disruption rendering portfolio companies obsolete. A PwC CEO survey found that 40% of respondents believe their companies won’t survive the next decade without charting new paths amid looming existential change driven by AI.
This isn’t hyperbole. AI is compressing competitive timelines across industries. Software companies face pressure from AI-native startups building products in weeks rather than months. Professional services firms confront automation threatening billing models. Manufacturing companies must integrate AI for operational efficiency or lose ground to competitors achieving superior productivity.
For PE-backed companies, many already held for 4, 5, or 6 years, the AI disruption accelerates urgency. A retail software company acquired in 2019 might have been best-in-class then. By 2025, it may lack AI-powered inventory optimization, personalized recommendations, or automated customer service that newer competitors offer as table stakes. Waiting another 2 to 3 years for market conditions to improve could mean exiting a company that’s fallen from market leader to laggard.
This dynamic explains why PE firms can’t afford to remain AI skeptics. The choice isn’t whether to invest in AI capabilities for portfolio companies but how quickly to deploy them and at what scale. Inaction creates value destruction as portfolio companies lose competitive position to AI-enabled rivals.
AI as the Exit Accelerator: Deploying Technology to Solve the Portfolio Problem
Faced with aging portfolios, demanding LPs, and AI disruption threats, PE firms are deploying artificial intelligence not just as a thematic investment but as an operational tool to accelerate portfolio company exits.
The logic is straightforward. AI can compress value creation timelines by automating operational improvements that traditionally required years of manual effort. AI can enhance exit multiples by demonstrating to buyers that portfolio companies have integrated cutting-edge capabilities. AI can improve exit timing by providing real-time market intelligence about buyer appetite and competitive positioning.
The Operational Value Creation Playbook
Leading PE firms have developed structured approaches to AI-driven value creation that directly support exit readiness. The playbook operates across three dimensions: transforming core business models, centralizing AI orchestration at the firm level, and building AI narratives into buy-side diligence and sell-side positioning.
Transforming Core Business Models
FTI Consulting’s 2024 AI Radar for Private Equity survey found that an increasing number of PE executives are shifting AI focus from incremental improvements toward business model evolution. This requires adjusting the AI lens to wider, multidimensional focus examining three key elements: how portfolio companies sell, what they sell, and how they create products and services.
Vista Equity Partners exemplifies this comprehensive approach. The firm has arrayed an internal army of professionals dedicated to helping its 85-plus portfolio companies apply AI across product innovation, R&D, go-to-market, talent, and operations. Vista regularly screens and triages its portfolio to determine where opportunities and risks lie, then partners with management teams to either move out of harm’s way or seize potential to enhance value. Company executives share learnings at a GenAI CEO Council organized so small companies can learn from large ones and vice versa.
The results are quantifiable. Vista requires each portfolio company to submit goals and quantified benefits from generative AI initiatives as part of annual operational planning. This discipline ensures AI deployment isn’t experimental but targeted toward measurable outcomes supporting higher exit valuations.
Centralizing AI Orchestration
PE firms are discovering that decentralized approaches to AI—allowing each portfolio company to pursue independent initiatives—miss opportunities for scale and knowledge transfer. FTI Consulting found that 40% of PE firms manage AI investments at the portfolio company level, a “Decentralized AI Operating” model that may prove insufficient given industry challenges formulating AI-focused strategies, battling for sparse talent, and deploying tools in acceptable timeframes.
Leading firms are moving toward centralized models efficiently scaling and exploiting AI learnings across portfolios. Hg’s approach illustrates this strategy. The firm uses generative AI across its software portfolio to “refactor” code from outdated languages to modern ones, extending popular portfolio company product life. This solution, once developed, applies broadly across Hg’s holdings, creating economies of scale impossible in decentralized models.
Hg also leverages generative AI to help management teams mine massive databases for sales prospects and M&A targets with specific characteristics. By design, Hg targets companies in high-cost labor markets where software spending is highest, then identifies opportunities to improve workflows making highly paid employees more efficient. This centralized intelligence capability benefits all portfolio companies simultaneously.
Building AI Into Exit Narratives
Perhaps most critically, sophisticated PE firms are embedding AI into sell-side narratives from day one, not as afterthought during exit processes. Buyers—whether strategic corporates or other PE firms—increasingly evaluate targets on AI maturity, recognizing that companies without AI integration face competitive disadvantage.
This shifts PE’s operational priorities. Previously, operational improvements focused on gross margin expansion, sales force optimization, geographic expansion—traditional levers driving EBITDA growth. Now, AI capability demonstration has become its own value driver. Portfolio companies can command premium multiples by showcasing:
- Proprietary datasets enabling competitive advantages
- AI-powered products generating higher customer lifetime value
- Automated operational processes reducing dependency on scarce labor
- Real-time business intelligence enabling faster decision-making
- Demonstrated ROI from AI investments proving ongoing value creation potential
BDO’s 2025 Private Equity Survey found that 84% of fund managers report longer holding periods, creating urgency around value creation plans. AI provides tangible evidence of value creation progress even when revenue growth or margin expansion may be constrained by macroeconomic conditions. A portfolio company that has implemented AI-driven customer acquisition, automated back-office operations, and AI-enhanced products tells a compelling story to buyers about sustainable competitive advantage.
Specific AI Applications Driving Exit Readiness
The operational playbook translates into specific AI applications that directly support exit preparation:
Revenue Optimization: AI-powered demand generation helps portfolio companies enhance customer acquisition by analyzing CRM data and identifying opportunities in overlooked markets or new distribution channels. This addresses a key buyer concern—proof that revenue growth can continue post-acquisition.
Operational Efficiency: Automating routine administrative tasks like legal compliance reporting, invoice processing, HR onboarding, and customer support reduces SG&A costs. Research from The Hackett Group shows generative AI can drive up to 40% reductions in SG&A costs for billion-dollar companies. These efficiency gains directly translate to EBITDA expansion supporting higher valuations.
Competitive Intelligence: AI-based tools track market developments, customer sentiment, and industry trends in real-time, enabling portfolio companies to benchmark against competitors and adjust strategies. During sell-side diligence, demonstrating sophisticated competitive intelligence capabilities signals management quality to buyers.
Digital Transformation: Many PE-backed companies must modernize outdated processes to remain competitive. AI accelerates digital transformation by automating legacy systems and identifying innovation areas like cloud platform integration or digital-first business models. This unlocks new revenue streams and scales operations more efficiently, creating optionality for buyers.
Exit Timing Optimization: AI tools analyze market cycles and buyer behavior to pinpoint optimal sale timing, uncovering windows of opportunity that would be invisible through traditional analysis. While PE firms have long relied on investment bankers for market timing advice, AI enables continuous monitoring rather than episodic assessments.
Real-World Results
The impact of AI on PE portfolio performance is beginning to show in empirical data. Bain’s 2025 survey of private investors representing $3.2 trillion in assets under management found that a majority of portfolio companies were in some phase of generative AI testing and development, with nearly 20% having operationalized generative AI use cases and seeing concrete results.
These aren’t marginal improvements. Vista Equity Partners, as noted, expects AI’s impact on software companies’ top and bottom lines to rewrite the Rule of 40—the long-standing yardstick investors use to evaluate SaaS businesses. When such fundamental metrics change, exit valuations change correspondingly.
Apollo Global Management’s Operating Partner Vikram Mahidhar notes that while results have been mixed and few firms report significant returns on generative AI investments so far, structured approaches are improving outcomes. Firms that secured executive buy-in, brought in relevant talent including operating partners understanding both business and implementation, and assessed AI’s industry impact during diligence are better positioned to align AI initiatives with investment goals.
The timeline matters enormously. PE’s typical 5 to 7-year hold period means AI implementations begun today must show measurable results within 2 to 3 years to support exit valuations. This compressed timeline drives preference for proven AI applications with demonstrated ROI rather than experimental approaches with uncertain payoffs.
Secondary Market Innovations: Continuation Funds as Liquidity Solution
While PE firms deploy AI to accelerate portfolio company exits, they’re simultaneously leveraging financial engineering to address the liquidity crisis through continuation funds and secondary transactions.
The Rise of Continuation Funds
Continuation funds have rapidly moved from niche strategy to mainstream exit mechanism. In 2024, 96 continuation fund vehicles were recorded, up 12.9% year-over-year, representing 14% of all PE exits. Analysts at Greenhill & Co. predict continuation funds could account for 20% of PE exits in coming years, driven by maturing secondary markets and challenging exit environments.
A continuation fund is a GP-led secondary transaction where a sponsor approaching fund expiration sells one or more assets from an existing fund into a new vehicle. Existing LPs can either exit for immediate liquidity or roll their investment into the new vehicle, while new investors gain exposure to proven assets with lower blind-pool risk than traditional fund commitments.
The scale of these transactions is growing dramatically. Single-asset continuation funds like the $3 billion Alterra Mountain Company deal and New Mountain Capital’s Real Chemistry transaction underscore how continuation vehicles now handle trophy assets that sponsors want to retain beyond original fund terms.
Why Continuation Funds Are Proliferating
Multiple structural forces drive continuation fund adoption:
Maturity Wall: More than 50% of PE funds are now six years or older, with 1,607 funds requiring wind-down in 2025 or 2026. Continuation funds allow firms to extend value creation without forced sales at inopportune valuations.
Exit Channel Constraints: PE-backed IPOs remain well below pre-pandemic levels. Strategic M&A, while showing signs of recovery, faces headwinds from trade policy uncertainty, elevated borrowing costs, and regulatory scrutiny. With traditional exit options constrained, continuation funds provide essential liquidity alternatives.
High-Conviction Asset Retention: Rising financing costs have constrained leveraged buyouts and widened bid-ask gaps in M&A deals. Continuation funds allow managers to retain high-conviction assets and provide investors with liquidity options simultaneously. Portfolio companies approaching inflection points—scaling operations, achieving profitability, capitalizing on strategic growth opportunities—benefit from extended hold periods while original fund LPs gain exits.
Investor Demand for Flexibility: LPs can exit for immediate liquidity or roll over to chase future upside. New investors gain exposure to proven assets with established revenue and operational track records, offering better risk-adjusted returns than blind pool commitments. Morgan Stanley research found upper-quartile continuation funds achieved 1.8x MOIC compared with 1.6x for comparable buyout funds, with continuation funds showing 9% loss ratios versus 19% for buyouts.
The Secondary Market Ecosystem
The broader secondaries market has expanded in lockstep with continuation funds. Global secondary deal volume hit $162 billion in 2024, with GP-led deals accounting for 44% of that total. Secondary market activity jumped to $180 billion in 2023, a 15% increase from the prior year, reflecting both LP needs for liquidity and GPs seeking exit flexibility.
These transactions now involve diverse buyers beyond traditional secondary funds: insurance companies, sovereign wealth vehicles, pension funds, and family offices all participate in secondary markets, creating competition that supports valuations. The buyer sophistication has increased correspondingly, with demands for credible marketing information, pipeline visibility, and independent NAV reviews.
The pricing dynamics are notable. Buyers in secondary markets typically seek discounts of 20-30% to previous round prices, especially if growth has been slower than expected. The choice between portfolio-plus-fund interests (“stapled” transactions) and continuation vehicles can shift deal pricing by 10-20%. These valuation adjustments reflect that LPs facing liquidity needs may accept discounts to accelerate distributions.
Governance Concerns and Best Practices
Despite their benefits, continuation funds raise legitimate governance and valuation concerns. When GPs act as both seller and buyer, conflicts of interest are inherent. Critics liken these to circular financing structures if not carefully governed. In weak markets, GPs might be motivated to offload assets from older funds at artificially low prices to new funds they control, capturing upside in new vehicles.
Several mechanisms mitigate these risks:
Full Disclosure to LPs: Transparent communication about deal rationale, valuation methodology, and alternatives considered helps LPs make informed decisions about whether to roll or exit.
Competitive Processes: Running auctions with multiple secondary buyers establishes market clearing prices rather than relying solely on GP valuations. This discipline ensures LPs aren’t leaving money on the table.
Independent Fairness Opinions: Third-party valuation experts providing fairness opinions protect GPs from potential disputes and give LPs confidence in pricing integrity.
LP Advisory Committee Approval: Requiring approval by LP advisory committees or majorities of non-conflicted LPs ensures that continuation transactions have support from those whose interests might conflict with GP preferences.
When IPO windows are closed and strategics hesitant to acquire, arriving at fair market values is challenging. Under these conditions, independent third-party valuations and fairness opinions become critical, not optional.
Continuation Funds and AI Integration
Interestingly, continuation funds create opportunities for AI-focused value creation that traditional exit timelines might preclude. If a PE firm wants to implement comprehensive AI transformation at a portfolio company but the original fund is approaching its term, a continuation vehicle provides runway to execute the strategy fully and capture resulting value creation.
Consider a software company in a PE portfolio where the firm has identified significant AI-driven opportunities—implementing AI-powered features, automating customer support, refactoring code bases—but the original fund has only 18 months remaining. Executing the AI roadmap requires 2 to 3 years. A continuation fund solves this timing mismatch, allowing the firm to retain the asset while providing liquidity to LPs preferring immediate exit.
This dynamic may partly explain why continuation funds and AI investment are accelerating simultaneously. Both address the portfolio aging problem from different angles—continuation funds by extending timelines, AI by accelerating value creation—and the combination is particularly powerful when deploying AI requires longer implementation periods than original fund terms allow.
The PE Playbook for Entering the AI Market
For PE firms seeking to capitalize on AI opportunities while navigating the sector’s complexities, a clear playbook is emerging from first movers and market leaders. The framework addresses deal sourcing, due diligence, value creation, and exit preparation.
Stage 1: Building AI Conviction and Capabilities
Secure Executive Buy-In
AI initiatives require sustained investment and patience as implementations mature. Firms need buy-in not just from deal partners but from operating partners, portfolio company CEOs, and most critically, LPs who must understand that AI deployment represents strategic necessity rather than speculative distraction.
Apollo, Vista, Hg, and other leaders begin by educating stakeholders on AI’s transformative potential and existential threats to companies lacking AI capabilities. This creates organizational alignment supporting multi-year AI programs across portfolios.
Assess Firm-Wide AI Readiness
Before pursuing AI deals or implementing AI across portfolios, firms should audit existing capabilities:
- Data infrastructure: Can the firm and its portfolio companies access, clean, and analyze data at scale?
- Talent: Does the firm have internal AI expertise or relationships with consultants and full-stack engineers who can implement solutions?
- Technology stack: Are portfolio companies’ systems architected to integrate AI, or do legacy platforms require modernization first?
- LP expectations: Do LPs understand and support AI investments, or do they view these as risky distractions from core value creation?
This assessment identifies gaps requiring investment before large-scale AI deployment begins.
Decide on Operating Model
FTI Consulting’s framework identifies four AI operating models ranging from fully decentralized (each portfolio company manages independently) to fully centralized (firm level ownership of AI strategy, talent, and execution).
The optimal model depends on portfolio composition, firm size, and sector focus. Specialist firms with concentrated portfolios in similar industries (like Vista in software or Hg in business software) can centralize effectively, developing repeatable playbooks and sharing learnings across holdings. Generalist firms with diverse portfolios may need hybrid approaches allowing some central coordination while respecting that AI opportunities in healthcare differ from those in manufacturing or retail.
The key insight: firms should make deliberate choices about operating models rather than allowing decentralized approaches to emerge by default.
Stage 2: Deal Sourcing and Investment Selection
Prioritize Infrastructure Over Applications
As Marc Lipschultz articulated, PE’s competitive advantage lies in “picks and shovels” infrastructure rather than high-risk application bets. Focus on:
- Data centers and colocation facilities serving AI compute demand
- Power generation and distribution infrastructure supporting data center buildout
- Networking and connectivity enabling distributed AI workloads
- Semiconductor manufacturing and specialized AI hardware
- Data management and cloud infrastructure platforms
These businesses generate recurring revenue from multi-year contracts, align with PE’s infrastructure expertise, and face less technology obsolescence risk than application-layer AI companies.
Pursue Mature AI Companies with Proven Use Cases
When investing directly in AI companies, prioritize:
- Demonstrated revenue growth from paying customers
- Clear unit economics with paths to profitability
- Proprietary data or switching costs creating competitive moats
- Management teams with track records successfully scaling technology companies
- Large addressable markets supporting growth even if company loses market share
Avoid:
- Pre-revenue companies where product-market fit remains unproven
- Companies dependent on single customers or nascent markets
- Business models requiring quantum leaps in AI capability to succeed
- Highly capital-intensive R&D with uncertain commercialization timelines
Conduct AI-Focused Due Diligence
Standard PE due diligence must expand to address AI-specific considerations:
Technology Assessment: Evaluate the AI architecture, model quality, training data sources, and technical debt. Engage AI experts who can assess whether claimed capabilities are genuine or marketing hype.
Data Rights and Quality: Examine data ownership, privacy compliance, and data quality. The best AI models require clean, comprehensive, properly labeled data. Verify targets own or control necessary data and comply with GDPR, CCPA, and other privacy regulations.
Compute Economics: Analyze ongoing compute costs and whether margins can sustain them. Some AI applications consume so much compute that gross margins become negative at scale. Model the unit economics carefully.
Competitive Positioning: Assess defensibility against AI-native startups and tech giants. Why can’t Google, Microsoft, or Amazon replicate this capability? What prevents customer switching to competitors?
Talent Retention: Identify key technical personnel and structure retention packages. AI talent is scarce and mobile. Losing the core engineering team post-acquisition can destroy value.
Regulatory Risk: Evaluate AI governance, bias testing, and compliance frameworks. As AI regulation increases globally, companies with robust governance frameworks will have competitive advantages.
Stage 3: Value Creation Through AI Implementation
Develop AI Transformation Roadmaps
For each portfolio company, create detailed roadmaps addressing:
- Current AI maturity assessment
- Highest-impact use cases for AI deployment
- Technical infrastructure requirements
- Talent acquisition or partnership strategies
- Financial projections showing ROI timelines
- Key performance indicators measuring progress
These roadmaps should be living documents updated quarterly as implementations progress and new AI capabilities emerge.
Implement Pilot Programs
Rather than attempting comprehensive AI transformations immediately, launch targeted pilots proving ROI and building organizational confidence:
Back-Office Automation: Start with routine administrative tasks—invoice processing, expense auditing, compliance reporting. These deliver quick wins with limited downside risk.
Customer Acquisition: Deploy AI for demand generation, analyzing CRM data to identify high-value prospects and optimize marketing spend.
Operational Efficiency: Implement AI-driven procurement optimization, inventory management, or supply chain forecasting where measurable cost savings justify investments.
Successful pilots generate momentum and stakeholder buy-in supporting more ambitious implementations.
Build or Buy Decisions
PE firms face recurring decisions about whether to build custom AI solutions, buy commercial off-the-shelf products, or partner with AI vendors:
Build when:
- The use case creates competitive differentiation
- The firm has necessary technical talent
- Commercial solutions don’t address specific needs
- Data sensitivity precludes external partnerships
Buy when:
- Commercial solutions exist with proven track records
- The use case is common across industries
- Speed to market is critical
- Building would require prohibitive investment
Partner when:
- Expertise resides outside the organization
- The solution requires ongoing innovation
- Risk sharing benefits all parties
- The partnership creates strategic optionality
Vista’s approach of building internal AI capabilities while selectively partnering with vendors exemplifies this balanced strategy.
Centralize Knowledge Sharing
Establish mechanisms for sharing AI learnings across portfolios:
- Regular AI councils where portfolio company leaders share successes and failures
- Centralized repositories documenting use cases, implementation guides, and vendor evaluations
- Operating partner networks connecting portfolio companies with similar AI challenges
- Quarterly reviews tracking AI initiatives across the portfolio
This knowledge sharing accelerates value creation by preventing each portfolio company from reinventing solutions to common problems.
Stage 4: Exit Preparation and Execution
Build AI Into Investment Memos
From initial acquisition, embed AI considerations into investment theses:
- How will AI disrupt this industry?
- What AI capabilities must the company develop to maintain competitive position?
- How can AI accelerate revenue growth or margin expansion?
- What AI story will resonate with buyers at exit?
This forward-looking perspective ensures AI initiatives align with exit objectives from day one.
Track AI Metrics for Exit Narratives
Document and quantify AI impact on business performance:
- Revenue from AI-powered products or features
- Cost savings from AI-driven automation
- Customer acquisition cost reductions through AI marketing
- Employee productivity gains from AI tools
- Competitive wins attributed to AI capabilities
These metrics provide concrete evidence of AI value creation during sell-side processes.
Timing Exit to AI Maturity
Coordinate exit timing with AI implementation milestones. Exiting before AI initiatives mature means leaving value on the table. Waiting too long after implementation risks buyers viewing AI capabilities as expected rather than differentiating.
The sweet spot typically occurs 6 to 18 months after AI implementations demonstrate measurable results but before they become commoditized. This timing allows firms to sell growth stories (AI capabilities continuing to drive improvement) rather than just historical results.
Educate Buyers on AI Value
During sell-side processes, proactively educate potential buyers on:
- Proprietary datasets and their strategic value
- AI talent quality and retention strategies
- Roadmap of AI capabilities in development pipeline
- Comparison to competitors’ AI maturity
- Total addressable market expansion enabled by AI
This education prevents buyers from discounting AI capabilities they don’t fully understand.
Stage 5: Measuring Success and Iteration
Establish Firm-Wide AI KPIs
Track AI impact at portfolio and firm levels:
- Percentage of portfolio companies with operationalized AI use cases
- Revenue contribution from AI-powered capabilities across portfolio
- Cost reduction attributed to AI implementations
- Exit multiple premiums for companies with strong AI narratives
- Fund performance correlation with AI integration intensity
These metrics inform future AI investment strategies and demonstrate ROI to LPs.
Conduct Post-Exit Reviews
After each exit, analyze AI’s contribution to returns:
- Did AI capabilities drive valuation premiums?
- Which AI implementations delivered highest ROI?
- Where did AI initiatives fail to create expected value?
- How did buyers perceive AI capabilities during diligence?
These insights refine the playbook for subsequent deals.
Adapt to Evolving AI Landscape
AI technology evolves rapidly. What constitutes cutting-edge capability today may be commoditized in 18 months. Firms must continuously monitor:
- Emerging AI capabilities and business applications
- Changes in AI regulation and governance requirements
- Shifts in AI talent markets and compensation
- New entrants disrupting portfolio company industries
- Opportunities for AI-enabled market expansion
This environmental scanning ensures AI strategies remain current rather than becoming outdated.
The Path Forward: Challenges and Opportunities
Private equity’s transformation from AI skeptic to AI evangelist in 18 months represents genuine strategic evolution rather than temporary trend-chasing. The combination of aging portfolio pressures, LP liquidity demands, AI disruption threats, and emerging opportunities in AI infrastructure creates conditions where AI investment becomes necessity rather than option.
However, significant challenges remain:
Implementation Risk: Despite growing investment, MIT research found that 95% of companies have seen little to no P&L impact from GenAI despite $30-40 billion in enterprise investment. The gap between AI deployment and value realization remains substantial. PE firms must develop disciplined implementation approaches that avoid investing in AI for its own sake while focusing on use cases delivering measurable ROI.
Talent Scarcity: AI talent remains scarce and expensive. PE firms compete with tech giants, well-funded startups, and other PE firms for limited pools of AI engineers, data scientists, and ML experts. Firms relying exclusively on internal talent may struggle to scale. Partnerships with consulting firms, fractional talent models, and systematic training of existing personnel offer alternatives.
Valuation Discipline: AI deals command premium valuations—25.8x revenue multiples—creating risk of overpaying if growth assumptions don’t materialize. PE firms must maintain valuation discipline even as competition for attractive AI assets intensifies. This requires sophisticated financial modeling accounting for AI’s impact on customer acquisition costs, churn rates, gross margins, and scalability.
Integration Complexity: Deploying AI across diverse portfolio companies with varying technical maturity, data readiness, and organizational cultures creates integration challenges. One-size-fits-all approaches fail. Firms need flexible frameworks allowing customization while maintaining some standardization and knowledge sharing.
Regulatory Uncertainty: AI regulation is evolving rapidly globally. The EU AI Act, potential U.S. federal legislation, and sector-specific rules create compliance complexity. PE firms must build governance frameworks ensuring portfolio companies can adapt to regulatory changes without business model disruption.
Technology Obsolescence: AI capabilities that create competitive advantages today may become commoditized as foundation models improve and off-the-shelf solutions proliferate. PE firms must distinguish between temporary technological leads and sustainable competitive moats when underwriting AI investments.
Despite these challenges, the opportunity set is substantial. The firms that successfully navigate AI integration—deploying capital into infrastructure plays while selectively pursuing proven AI applications and systematically implementing AI across portfolios to accelerate exits—will generate superior returns and establish competitive advantages that persist for years.
The next 18 months will prove critical. As continuation funds become increasingly mainstream, providing liquidity mechanisms for aging portfolios, and as AI implementations mature from pilot to production across thousands of portfolio companies, the PE industry’s transformation from AI skeptic to AI native will complete. The firms executing this transformation most effectively will shape the industry’s next decade, while those clinging to traditional approaches risk obsolescence in an AI-accelerated world.
For LPs, the implications are clear. AI capability and implementation discipline should feature prominently in GP evaluation. Questions to ask include: How is the firm thinking about AI strategically? What AI operating model has it adopted? Can it demonstrate measurable AI value creation in existing portfolio companies? How does its AI approach differ from competitors?
For GPs, the message is equally direct. The 18-month transformation from AI skeptic to AI believer represents only the beginning. Building systematic AI capabilities, deploying them effectively across portfolios, and leveraging AI to solve the industry’s most pressing challenge—the aging portfolio crisis—will separate winners from laggards over the coming years.
The private equity industry’s AI playbook is being written in real-time. The firms contributing chapters grounded in operational discipline, financial prudence, and measurable results will create the most value. Those treating AI as marketing narrative rather than operational reality will face consequences in returns, fundraising, and competitive positioning.
The verdict on private equity’s AI transformation won’t arrive for several more years, when the current generation of AI-integrated portfolio companies exit and return data demonstrating whether AI implementations delivered promised value creation. But the trajectory is clear: AI has moved from peripheral consideration to central investment thesis, and there’s no going back.
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