Private Equity Value Creation Through AI

Introduction

For more than two decades, returns were largely driven by a combination of financial engineering, multiple expansion, and cyclical tailwinds. That model is now under pressure. Higher interest rates, extended holding periods, valuation compression, and a constrained exit environment have reduced the effectiveness of leverage and arbitrage. In parallel, limited partners are demanding clearer evidence of operational value creation, faster distribution of capital, and greater transparency.

Artificial intelligence (AI) is emerging as a decisive response to this new environment. It is no longer simply an efficiency tool or a digital add-on. AI has become a distinct and scalable value lever that can materially reshape EBITDA trajectories, improve capital efficiency, and strengthen exit narratives. Across the investment lifecycle—from deal sourcing and diligence to portfolio transformation and exit preparation—AI enables faster decision-making, deeper insight, and structurally higher performance.

This report examines how AI is reshaping value creation in private equity. It combines a macroeconomic and business perspective on AI-driven value, an assessment of deal momentum, an analysis of the current exit environment, and a review of how PE firms are implementing AI today. It concludes with best practices and a forward-looking path for firms seeking to institutionalize AI as a core driver of sustainable returns.

1. AI Impact in Business and the Global Economy

Artificial intelligence is already exerting a measurable impact on productivity, growth, and value creation across the global economy. Unlike prior waves of automation, AI—particularly generative AI and advanced machine learning—extends beyond routine tasks into cognitive, creative, and analytical domains. This dramatically expands the scope of economic activity that can be augmented or transformed.

AI Value by Business Function

AI’s value creation potential is unevenly distributed across business functions. The largest opportunities are concentrated where information intensity, decision frequency, and labor costs intersect. Functions such as operations, marketing and sales, software engineering, and research and development stand out as primary beneficiaries. In these areas, AI enables both cost reduction through automation and revenue uplift through improved decision quality, personalization, and speed to market.

For private equity, this functional view is critical. Portfolio-level value creation plans increasingly focus on identifying two to three high-impact AI use cases per company, rather than broad, unfocused digital transformation programs.

Automation and Productivity Expansion

Generative AI materially increases the technical automation potential of the economy. Tasks that previously resisted automation—such as document analysis, scenario modeling, code generation, and customer interaction—are now partially or fully automatable. In midpoint adoption scenarios, this raises overall automation potential well beyond earlier estimates.

For PE-backed companies, this translates into faster EBITDA expansion. Automation is no longer limited to back-office functions; it extends into commercial, engineering, and analytical roles that directly affect growth and margins.

Contribution to Global Economic Growth

At the macroeconomic level, artificial intelligence represents one of the most significant structural growth catalysts of the next decade. The chart illustrates this impact clearly: starting from a baseline global GDP of approximately $113.8 trillion, AI is expected to contribute between $2.6 trillion in a base case and $4.4 trillion in an upper case of incremental annual value added, lifting total global output to roughly $116.4 trillion–$118.2 trillion. This range is not a one-off stimulus effect; it reflects a sustained productivity uplift driven by automation, decision augmentation, and the creation of AI-native products and services across the economy.

What makes this contribution structurally different from prior technology waves is its breadth. AI impacts not only capital-intensive industries or digitally native sectors, but also traditionally slower-moving segments such as industrial manufacturing, healthcare delivery, logistics, professional services, and financial intermediation. Productivity gains are realized through a combination of labor augmentation, faster cycle times, reduced error rates, and materially improved capital allocation decisions. At the same time, AI enables entirely new revenue pools—from data-driven services and intelligent automation to outcome-based and subscription business models—that expand total addressable markets rather than merely redistributing existing demand.

For private equity investors, the implications are profound. First, the absolute scale of value creation—measured in trillions of dollars annually—means AI is no longer a niche technology theme, but a macro force reshaping industry economics. Second, and more importantly, a significant portion of this incremental value is expected to be captured before companies reach public markets. Unlike earlier technology cycles where the majority of value accrued post-IPO, AI adoption is increasingly occurring within private companies, supported by growth equity and buyout capital that can fund transformation during the ownership period.

This dynamic positions private equity as a primary conduit for monetizing AI-driven economic growth. AI is not merely an internal efficiency lever for sponsors; it is a thematic investment driver that informs sector selection, diligence priorities, and post-acquisition value creation plans. Sectors such as healthcare, industrials, financial services, and software stand out not only because they are data-rich, but because AI can materially alter their cost structures, pricing power, and growth trajectories within a typical holding period.

In practical terms, the chart underscores why AI must be embedded directly into investment theses. Capturing even a small share of the $2.6–$4.4 trillion annual value pool requires sponsors to move beyond opportunistic pilots and toward systematic deployment of AI capabilities at scale. Those firms that succeed will not only benefit from higher EBITDA growth, but also from structurally stronger exit narratives grounded in demonstrable productivity gains and future-proofed business models.

Geographic Differences in Productivity Impact

As noted by McKinsey, the productivity impact of automation—and, critically, the incremental uplift enabled by generative AI—varies meaningfully across geographies, reflecting differences in labor economics, industrial structure, digital maturity, and capital intensity. As shown in the chart, global productivity growth from automation is expected to average approximately 3.4% CAGR between 2022 and 2040, of which roughly 0.6 percentage points are attributable to generative AI. This incremental contribution is not uniform and highlights where AI can most effectively compound value creation.

In developed economies, the total productivity impact consistently ranges between 3.6% and 3.8% CAGR, with generative AI contributing 0.6–0.7 percentage points on top of already high baseline automation levels. The United States, for example, reaches the upper end of this spectrum at 3.8%, with 0.7% directly linked to generative AI, reflecting its advanced digital infrastructure, high labor costs, and concentration of knowledge-intensive work. Japan, Germany, and France follow closely at approximately 3.7%, where AI-driven augmentation of engineering, manufacturing, professional services, and administrative functions materially boosts output per worker.

For private equity investors, this data underscores why AI adoption in developed markets is primarily a margin and productivity story. With labor scarcity, aging workforces, and elevated wage structures, AI delivers value by augmenting existing employees, compressing cycle times, and reducing dependency on incremental headcount. In these markets, AI initiatives tend to translate quickly into EBITDA margin expansion, particularly in sectors such as industrials, healthcare services, financial services, and software-enabled business services.

By contrast, emerging economies exhibit lower—but still meaningful—productivity gains. Total automation-driven productivity growth ranges from 3.3% in Mexico to 2.9% in South Africa, with generative AI contributing 0.4–0.5 percentage points. While the absolute uplift from AI is smaller, its strategic role is different. Lower labor costs reduce the immediate economic case for full automation, but AI enables scalability, consistency, and leapfrogging of legacy processes, particularly in rapidly growing consumer, logistics, and export-oriented sectors.

For PE firms operating in emerging markets, this implies a more selective and growth-oriented AI strategy. Rather than prioritizing headcount reduction, value creation efforts often focus on revenue enablement, pricing intelligence, demand forecasting, and supply-chain optimization—areas where AI can support expansion without disrupting labor-intensive operating models. Over time, as wage inflation and regulatory pressures increase, these markets are likely to converge toward more automation-heavy use cases.

Importantly, the chart also highlights that generative AI acts as a universal productivity accelerator, adding incremental growth across all regions regardless of baseline maturity. For global private equity portfolios, this reinforces the need for region-specific AI playbooks. A one-size-fits-all approach risks underdelivering value. Instead, sponsors must calibrate AI deployment to local economic realities—maximizing margin expansion in developed markets while enabling scalable growth and operational resilience in emerging economies.

Ultimately, the geographic dispersion in productivity impact strengthens the strategic case for AI as a portfolio-wide value creation lever. While the magnitude and pathways differ, AI consistently enhances productivity across regions, reinforcing its role as a foundational driver of long-term value creation in global private equity investing.

The AI Value Creation Curve

According to JP Morgan, AI-driven value creation follows a highly structured and historically consistent curve, but with a critical deviation from prior technology cycles that materially alters where—and by whom—value is ultimately captured. As illustrated in the chart, value creation progresses through four distinct phases: physical infrastructure, digital infrastructure, platform technology, and applications. While early phases are capital-intensive and foundational, the overwhelming majority of economic value is realized at the application layer.

In Phase 1 (Physical Infrastructure)—encompassing chips, compute hardware, and data centers—total value creation remains relatively modest, at well below $1 trillion, and is overwhelmingly captured in public markets. These businesses require massive upfront capital expenditure, benefit from scale economics, and tend to be dominated by a small number of incumbents. While strategically critical to the AI ecosystem, this phase offers limited scope for differentiated private equity value creation beyond infrastructure-style returns.

Phase 2 (Digital Infrastructure)—including cloud platforms, data architectures, and foundational tooling—shows a slight increase in total value, approaching approximately $1 trillion. Again, the majority of value accrues to public market players, reflecting winner-take-most dynamics and high barriers to entry. Private market participation remains limited, largely confined to niche enablers or late-stage growth opportunities.

The inflection point occurs in Phase 3 (Platform Technology). Here, total value creation expands meaningfully to approximately $3–4 trillion, with a visibly larger private-market component emerging. This phase includes foundation models, APIs, orchestration layers, and AI development platforms. While public markets still capture a significant share, private capital increasingly participates by backing category-defining platforms before they reach public scale. For growth equity and technology-focused buyout funds, this phase represents the first opportunity to underwrite AI-driven value creation with both scale and control.

The most consequential shift appears in Phase 4 (Applications). Total estimated value creation in this phase reaches well over $12 trillion, dwarfing all prior phases combined. Crucially, unlike previous technology cycles, a substantial portion of this value is captured in private markets, as reflected by the sizable private-market segment of the bar. These applications span vertical-specific AI solutions, enterprise automation tools, agent-based systems, and AI-native business models embedded directly into operating workflows across industries.

This represents a structural break from the internet and cloud cycles, where application-layer value largely accrued post-IPO. In the AI cycle, companies are scaling revenue, embedding defensible IP, and achieving material EBITDA expansion while still privately held. Longer private lifecycles, abundant private capital, and the operational complexity of AI deployment all favor private ownership during the most value-creative phase.

For private equity, the implications are profound. The chart underscores that AI is not primarily an infrastructure investment thesis—it is an application-led value creation opportunity. Buyout and growth funds are uniquely positioned to capture this value by combining capital with operational control, enabling AI deployment directly within portfolio companies rather than betting solely on standalone AI vendors.

More importantly, this curve reframes AI from a technology exposure question to a portfolio transformation mandate. The largest value pool is unlocked not by owning AI infrastructure, but by applying AI to fundamentally reshape cost structures, pricing models, and revenue engines inside businesses. This is precisely where private equity has comparative advantage: concentrated ownership, long-term capital, and the ability to drive change across fragmented, non-public markets.

In practical terms, the AI value creation curve validates why private equity and growth capital are becoming the primary vehicles for AI value realization. Funds that position themselves downstream—at the application layer—can participate in the largest, fastest-growing, and most defensible segment of the AI economy, while simultaneously enhancing EBITDA, strengthening exit narratives, and reducing dependence on favorable market timing.

Value Opportunity by Segment

AI-driven value creation is not concentrated in a single technology layer but distributed across several distinct segments, each with different risk profiles, scaling dynamics, and implications for private equity ownership. As shown in the chart, agentic AI represents the largest individual value pool, at approximately $6 trillion, while vertical AI, horizontal AI, and industrial AI each account for roughly $3 trillion of potential value creation.

The prominence of agentic AI reflects a shift from AI as a passive analytical tool toward autonomous, action-oriented systems embedded directly into business workflows. These solutions—capable of executing tasks, orchestrating processes, and interacting across systems—have the potential to materially reshape cost structures and productivity at scale. For private equity, agentic AI is particularly relevant as it enables repeatable efficiency gains across portfolio companies, especially in functions such as operations, customer service, finance, and IT.

Vertical AI captures value through deep industry specialization, embedding AI directly into sector-specific workflows such as healthcare, financial services, legal, or industrial processes. These solutions tend to offer stronger defensibility and pricing power, making them attractive both as standalone investments and as value creation levers within portfolio companies operating in fragmented or regulated industries.

Horizontal AI platforms focus on cross-functional enterprise use cases, including sales, marketing, HR, and finance. While often more competitive, these solutions scale rapidly and can be deployed broadly across portfolios, supporting standardized value creation initiatives.

Finally, industrial AI targets automation, predictive maintenance, and process optimization in asset-heavy environments. Although adoption cycles can be longer, the impact on EBITDA and cash flow is often substantial once deployed.

Taken together, the chart highlights that AI value creation is both broad and diversified, reinforcing the need for PE firms to pursue a balanced strategy—combining selective exposure to high-growth AI segments with pragmatic deployment of AI capabilities inside portfolio companies to capture value directly during the holding period.

2. AI Deal Momentum

The acceleration of AI adoption is reflected in deal activity across private markets. AI-related transactions have increased sharply in both value and volume, spanning venture, growth equity, and buyout strategies.

Deal momentum is driven by three factors. First, AI-native companies are scaling faster than traditional software businesses, justifying higher valuations. Second, incumbents across industries are acquiring AI capabilities to defend market positions. Third, PE firms are increasingly underwriting AI-driven value creation at entry, rather than treating it as optional upside.

This momentum underscores AI’s transition from a thematic investment area to a core component of mainstream private equity strategies.

3. The Current Exit Environment and the Imperative to Expand EBITDA

The private equity exit environment remains constrained. While deal activity and fundraising rebounded rapidly after the global financial crisis and again during the post-COVID period, the last several years have exposed the fragility of exit-driven value realization when macro conditions tighten. Higher interest rates, valuation compression, and increased buyer selectivity have fundamentally altered how and when private equity firms can return capital to investors. As a result, value creation during the holding period—rather than exit timing or multiple expansion—has become the dominant determinant of fund performance.

A Volatile and Uneven Exit Market

Global PE-backed exit activity illustrates the magnitude of this shift. After reaching an unprecedented $1.7 trillion in exit value in 2021, fueled by ultra-low interest rates and strong public markets, exits fell sharply to $808 billion in 2022 and further to $751 billion in 2023—a decline of more than 55% from peak to trough. Although 2024 showed signs of stabilization at approximately $887 billion, and 2025 is tracking around $832 billion year-to-date, exit volumes remain well below the 2021 peak.

This volatility underscores a critical reality: exit markets are no longer reliably available on demand. Even when transaction volumes recover, buyers are more disciplined, financing is more expensive, and underwriting standards are materially tighter. Assets without demonstrable earnings growth, pricing power, or operational resilience face extended holding periods or discounted valuations.

Distributions Lag Contributions

The impact of constrained exits is most visible in the widening gap between capital returned to limited partners and new capital deployed. The ratio of distributions to contributions has deteriorated meaningfully, creating liquidity pressure across the LP ecosystem. Many institutional investors are facing denominator effects, reduced re-up capacity, and heightened scrutiny of GP value creation claims.

This dynamic has important second-order effects. LPs are no longer willing to underwrite returns based primarily on financial engineering or exit multiples. Instead, they are demanding clear evidence of operational value creation, EBITDA expansion, and defensible growth trajectories—well before exit.

Fundraising Remains Resilient, Increasing Pressure on Value Creation

Despite slower exits, private equity fundraising has remained structurally strong. Annual capital raised continues to measure in the high hundreds of billions of dollars, reinforcing elevated levels of dry powder across buyout and growth strategies.

This imbalance—ample capital supply combined with constrained exit capacity—has intensified competition for high-quality assets and increased holding periods across portfolios. In this environment, the traditional reliance on leverage and multiple expansion is insufficient. Higher interest rates compress leverage-driven returns, while valuation multiples are increasingly capped by public market comparables and buyer risk aversion.

Continuation Vehicles as a Symptom, Not a Solution

The rapid growth of continuation vehicles further highlights the structural nature of the exit challenge. Continuation funds have become an increasingly common mechanism to provide partial liquidity, reset hold periods, and retain exposure to assets believed to have further upside.

While continuation vehicles can be effective portfolio management tools, they do not create value on their own. They defer exit risk rather than eliminate it. Ultimately, assets must still justify their valuation through stronger cash flows, higher margins, and credible growth narratives. In practice, this places even greater emphasis on operational transformation during the extended holding period.

EBITDA Expansion as the Primary Exit Lever

In this environment, EBITDA growth has become the most reliable and controllable driver of value creation. Buyers—whether strategic acquirers, sponsors, or public market investors—are prioritizing assets with visible earnings momentum, scalable operating models, and data-backed performance improvements.

Artificial intelligence plays a central role in enabling this shift. AI-driven initiatives can accelerate EBITDA expansion through:

• Cost reduction via automation of labor-intensive processes

• Margin expansion through pricing optimization and demand forecasting

• Revenue growth via personalization, cross-selling, and faster product innovation

• Capital efficiency through improved working capital management and predictive maintenance

Crucially, these benefits are increasingly measurable within a typical private equity holding period, allowing sponsors to demonstrate realized value creation rather than aspirational upside.

Implications for Private Equity Strategy

Taken together, the exit data points to a clear conclusion: the path to successful exits now runs through operational excellence at scale, not market timing. AI-enabled value creation is no longer a differentiator—it is rapidly becoming a prerequisite for competitive exits.

Private equity firms that systematically deploy AI across portfolio companies are better positioned to:

• Shorten time-to-value despite longer holding periods

• Support higher exit valuations through defensible EBITDA growth

• Withstand cyclical exit market volatility

• Meet rising LP expectations for transparency and performance

In a world where exits are scarcer, slower, and more selective, AI-driven EBITDA expansion represents the most dependable bridge between capital deployment and capital realization.

4. Current PE Implementation of AI and Emerging Opportunities

Private equity firms are at varying stages of AI adoption. While awareness is high, implementation maturity differs significantly across firms and portfolios.

AI in Portfolio Valuation

An increasing number of firms are explicitly incorporating AI initiatives into valuation assumptions. This includes both cost savings from automation and revenue uplift from improved pricing, sales effectiveness, and product innovation.

Institutionalization Through Value Creation Teams

Leading private equity firms are increasingly formalizing AI and operational transformation capabilities through dedicated value creation teams, signaling a shift from ad hoc initiatives to institutionalized execution. As the chart illustrates, the majority of firms have moved beyond minimal staffing: 33% of PE firms operate teams of 5–9 professionals, while an additional 23% maintain teams of 2–4, indicating that value creation is now resourced as a core function rather than an overlay. Only 5% of firms report having no dedicated value creation team, underscoring how broadly this model has been adopted across the industry.

At the upper end of the spectrum, approximately one-third of firms employ teams of 10 or more professionals, including 18% with 10–19, and nearly 20% with teams exceeding 20 members, reflecting a growing emphasis on in-house operating leverage at scale. These teams increasingly integrate technology, data, and AI expertise alongside traditional operational skill sets, enabling sponsors to identify, prioritize, and execute AI-driven initiatives consistently across portfolios. Importantly, the presence of a dedicated team improves accountability and speed, ensuring that AI use cases are tightly aligned with the original investment thesis and translated into tangible EBITDA impact well before exit.

Technology and Digital Expertise

The scale and sophistication of technology- and digital-focused value creation teams within private equity firms continue to increase, reflecting the growing importance of data, AI, and digital execution in driving portfolio performance. As shown in the chart, 78% of PE firms now maintain at least one dedicated technology or digital value creation professional, with 37% staffed by a single specialist and 31% operating small teams of 2–4 individuals. This distribution suggests that many firms are still in the early stages of formalizing digital capabilities, prioritizing focused expertise over large, centralized teams.

At the same time, a smaller but meaningful subset of firms is moving toward greater scale: 10% report teams of five or more, including 5% with 5–9 members and another 5% with 10–19, indicating a transition from advisory support to hands-on execution capacity. Notably, 22% of firms still report having no dedicated technology or digital value creation team, highlighting a widening gap between digitally enabled sponsors and those at risk of falling behind. For firms further along the maturity curve, the emphasis is increasingly on building repeatable AI and digital playbooks—covering data infrastructure, automation, and advanced analytics—that can be systematically deployed across portfolio companies to accelerate EBITDA impact and support more resilient exit narratives.

Perceived Value Across Business Lines

Perceptions of AI-driven value creation vary meaningfully across industries, reflecting differences in data intensity, process standardization, and regulatory complexity. As shown in the chart, technology stands out as the clear leader, with 68% of respondents rating AI’s value as “very significant” and an additional 15% as “significant,” underscoring the maturity of AI use cases in software-driven environments. Healthcare and legal services also exhibit strong conviction, with 41% of respondents in both sectors viewing AI as very significant, supported by high combined “very significant” and “significant” ratings of 72% and 66%, respectively—highlighting the impact of AI on diagnostics, workflow automation, contract analysis, and compliance.

Financial services–oriented sectors show similarly high perceived value, albeit with slightly more dispersion. In financial services, 70% of respondents rate AI as significant or very significant, while banking and insurance each register over 60% combined high-significance ratings, reflecting AI’s role in risk modeling, fraud detection, underwriting, and customer engagement. By contrast, more asset-heavy or people-intensive industries such as manufacturing, retail, hospitality, and real estate exhibit lower—but still material—levels of conviction. In manufacturing, for example, only 16% view AI as very significant, but nearly 45% rate it as significant or somewhat significant, suggesting growing recognition despite slower adoption cycles. Higher “don’t know” responses—reaching 31% in energy and real estate—signal untapped potential rather than skepticism, often linked to data fragmentation and implementation complexity.

For private equity sponsors, these perception gaps are instructive. They point to where AI-enabled value creation is already being monetized—and where operating partners can generate outsized impact by accelerating adoption. As AI initiatives move from pilots to scaled deployment, perceived value increasingly converges with realized outcomes, particularly in operations, commercial optimization, finance, and strategic decision-making. Sponsors that proactively translate these perceptions into targeted execution roadmaps are better positioned to unlock EBITDA growth and differentiate assets at exit, especially in sectors where AI value is acknowledged but not yet fully operationalized.

5. Best Practices for AI-Driven Value Creation

Successful AI-driven value creation in private equity is characterized by discipline, focus, and integration across the investment lifecycle.

AI Across the Investment Lifecycle

Best-in-class firms apply AI end-to-end: enhancing deal sourcing through data-driven screening, accelerating diligence through automated analysis, improving portfolio performance through operational AI, and strengthening exit narratives with data-backed results.

AI as a Strategic Response to Complexity

AI is increasingly positioned as a response to rising business complexity. Rather than optimizing isolated processes, firms use AI to improve decision-making under uncertainty, enhance resilience, and enable faster strategic pivots.

Key best practices include:

• Use-case-driven deployment focused on EBITDA impact

• Rapid pilots with clear success metrics

• Clean, accessible data foundations

• Strong governance and risk management

• Explicit linkage to exit narratives

6. Conclusion and the Path Forward

Artificial intelligence has become a structural driver of private equity value creation. In a market defined by constrained exits, higher capital costs, and elevated LP expectations, AI offers a scalable and defensible path to EBITDA expansion and multiple resilience.

The firms that succeed will be those that move beyond experimentation to institutionalization. This requires treating AI as a core investment capability, embedding it into value creation teams, and aligning it explicitly with exit outcomes. The path forward is clear: focused use cases, disciplined execution, and a relentless emphasis on measurable value.

AI will not replace sound judgment or operational expertise. However, for private equity firms that master its application, it will decisively enhance both.

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