Large-cap Private Equity: The next frontier

Thomas Ferguson

Introduction - from buzzword to boardroom imperative

Artificial intelligence (AI) has rapidly evolved from a futuristic concept into a mainstream business tool. In industries from retail to healthcare, it is already reshaping how organisations operate, compete, and grow. For large-cap private equity (PE) firms that manage multibillion-dollar funds and operate globally scaled portfolio companies, the implications are particularly profound.

Private equity thrives on a simple equation: acquire the right companies, unlock hidden value, and exit at a premium. Traditionally, success depended on financial acumen, operational expertise, and strong networks. Today, AI enhances each element of that equation. It can help funds identify targets faster, evaluate them more rigorously, and manage portfolios more effectively. Within portfolio companies, it offers tangible improvements in efficiency, revenue growth, and resilience.

This article from The Barton Partnership examines how large-cap private equity firms are leveraging AI at both the fund and portfolio levels, the challenges they encounter, and a roadmap for capturing sustainable value.

1. AI at the Fund level: Smarter deals, faster decisions

Large-cap funds operate in a competitive landscape where information asymmetry has historically been a key advantage. AI shifts this balance. By combining vast datasets, predictive analytics, and natural language processing (NLP), funds can surface opportunities and risks that might otherwise remain hidden.

Deal sourcing and screening.

AI platforms can analyse thousands of company filings, regulatory documents, market reports, and even sentiment data from news and social media.Thisallows firms to spot acquisition opportunities earlier and evaluate more targets without proportionally increasing human resources.

Blackstone, for example, has invested heavily in AI and data intelligence to strengthen deal origination. In2024it committed $300m to DDN, an AI and data solutions leader (Blackstone, 2024), while portfolio companies such as Ontra (AI-driven legal workflows) and Link Logistics (machine learning across2bn data points) show how AI is already embedded in sourcing and screening processes (Blackstone, 2025).

Due diligence acceleration.

Generative AI is nowbeing appliedto virtual data rooms and diligence materials. Instead of armies of analysts combing through contracts, financial statements, and compliance records, AI tools can summarise findings, flag anomalies, and benchmark metrics against comparable companies.

ABF Journal (2025) notes that AI-powered VDRs and automated quality-of-earnings reviews are now “compressing diligence from weeks to days” (ABF Journal, 2025). Similarly, McKinsey (2025) reports that Gen AI enables sponsors to “accelerate the diligence process, gain richer insights, and make decisions with more speed and confidence”(McKinsey, 2025), with case studies showing reductions in diligence timelines of 30% or more. Speed matters when auctions are competitive and deal windows are narrow.

Portfolio monitoring.

Once deals close, AI enables funds to maintain a sharper line of sight into portfolio performance. Machine learning models can continuously monitor financial and operational KPIs, market movements, and regulatory signals. Instead of quarterly surprises, funds can receive near real-time alerts about underperformance, emerging risks, or new opportunities, allowing for proactive interventions.

Together, these applications provide large-cap funds with what they value most: speed without sacrificing rigour, and insight without incurring ballooning costs.

2. AI in Portfolio companies: Driving operational value

While fund-level applications improve investment decisions, the real impact of AI often materialises within portfolio companies. For large-cap firms managing hundreds of billions in enterprise value across industries, embedding AI into operations can unlock transformative value.

Operational efficiency.

Predictive maintenance is a mature AI use case already delivering results in manufacturing, energy, and transportation. By combining IoT sensor data with machine learning, companies can anticipate equipment failures, reduce downtime, and extend the lifespan of their assets. Supply chain optimisation is another high-impact area. AI models help forecast demand, optimise routes, and even automate supplier selection, improving resilience while reducing cost.

Revenue growth.

AI can drive the top line as well. Predictive customer analytics help companies anticipate churn, segment customers more precisely, and personalise marketing at scale. Dynamic pricing engines, powered by AI, adjust prices in real time to balance demand and margin. Retailers, subscription businesses, and industrial companies alike are already experimenting with these tools. For PE-owned companies, which often need to accelerate revenue growth quickly, the upside is significant.

Strategic transformation.

Beyond efficiency and revenue, AI can catalyse entirely new business models. For instance, industrial companies can shift toward “as-a-service” offerings by using predictive analytics to guarantee uptime. Consumer brands can develop digital-first experiences powered by personalisation engines. For portfolio companies under pressure to evolve in fast-changing industries, AI offers a lever to reinvent.

ESG and compliance.

Large-cap companies also face heightened scrutiny around environmental, social, and governance (ESG) standards. AI can also help in this area: monitoring supplier compliance, tracking carbon emissions, and identifying reputational risks from vast datasets. Funds can reassure regulators and limited partners (LPs) by demonstrating more rigorous oversight, enabled by technology.

3. Challenges for large-cap firms

While the promise of AI is compelling, execution in large-cap contexts is complex.

Data fragmentation. Portfolio companies often operate across multiple geographies with legacy systems in place. Aggregating and cleaning data is usually the first, and hardest, step.
Scalability. A pilot project in one business unit is one thing; rolling out AI across a global enterprise requires governance, integration, and change management.
Talent constraints. AI expertise is scarce. Funds must decide whether to build in-house teams, partner with vendors, or encourage portfolio companies to hire directly. Often, the answer is a mix.
Regulatory and ethical risks. AI models must be explainable, compliant with data privacy laws, and free from bias. For sensitive deal data, ensuring confidentiality when using third-party AI platforms is critical.
Measuring ROI. Quantifying AI’s impact can be tricky, particularly when it influences multiple levers simultaneously. Clear KPIs are crucial for sustaining momentum and justifying investment.

Large-cap firms, with their scale and visibility, must address these issues head-on to capture AI’s benefits without incurring outsized risks.

4. A roadmap for AI in large-cap Private Equity

To move from experimentation to enterprise value, PE firms and their portfolio companies can follow a structured path:

Assess AI and data maturity. Begin with a diagnostic of fund processes and portfolio operations. Identify where data is fragmented and where AI pilots already exist.
Define high-impact use cases. Prioritise areas with clear potential for value creation, predictive maintenance, supply chain optimisation, pricing, churn reduction, or diligence acceleration.
Pilot and prove. Launch small pilots in selected portfolio companies or fund functions. Capture lessons, measure outcomes, and refine.
Build or partner for capability. Decide whether to invest in internal data science teams or collaborate with specialised vendors. Large-cap firms often use both approaches.
Establish governance. Create frameworks for AI ethics, data privacy, and regulatory compliance. Transparency builds trust with LPs, regulators, and employees.
Upskill teams. Train investment professionals and operating partners to interpret AI outputs and make informed decisions. Encourage portfolio companies to do the same.
Scale with measurement. Once pilots succeed, expand thoughtfully. Track metrics such as reduced downtime, increased revenue, margin improvements, or faster diligence cycles. Use these to demonstrate value to stakeholders.

This roadmap acknowledges both the promise and the complexity of AI. Success lies not in adopting AI for its own sake, but in aligning it directly with the value-creation levers that define private equity.

5. The strategic imperative

The broader investment community is already watching how AI reshapes industries. Public market investors are pricing in AI’s impact on tech companies, manufacturers, and service providers. For private equity, the stakes are even higher: unlike public investors, PE firms own the operational levers and can directly shape how companies deploy AI.

In large-cap private equity, where competition for deals is intense and LPs demand differentiated performance, AI offers a rare combination of offensive and defensive benefits. Offensively, it allows funds to find deals first, price them more accurately, and accelerate portfolio growth. Defensively, it reduces operational risk, improves compliance, and increases resilience against market shocks.

Ultimately, AI is not a silver bullet; it is a tool. Its effectiveness depends on leadership vision, data discipline, and the ability to embed technology into organisational culture. But firms that succeed will redefine what operational value creation looks like in the 21st century.

Conclusion: From advantage to necessity

AI is rapidly shifting from a competitive edge to a baseline expectation. For large-cap private equity firms, the question is no longer whether to adopt AI, but rather how quickly and effectively it can be integrated into fund processes and portfolio operations.

Those who move early and thoughtfully will capture outsized benefits: faster deal cycles, sharper diligence, more resilient operations, and stronger exits. Those who wait riskbeing left behindin a market where data, speed, and precision increasingly define success.

The opportunity is clear: AI is not just another technology investment. It is a strategic lever for the next era of private equity value creation.

The Barton Partnership is a global leader in value creation talent for private equity, spanning both funds and portfolios. Find out more about how we deliver value creation talent that empowers private equity investors and portfolio company executives to drive transformation and maximise returns, from strategy to execution.

Large-cap Private Equity: The next frontier of value creation

Thomas Ferguson

Introduction - from buzzword to boardroom imperative

This article from The Barton Partnership examines how large-cap private equity firms are leveraging AI at both the fund and portfolio levels, the challenges they encounter, and a roadmap for capturing sustainable value.

1. AI at the Fund level: Smarter deals, faster decisions

Deal sourcing and screening.

AI platforms can analyse thousands of company filings, regulatory documents, market reports, and even sentiment data from news and social media.Thisallows firms to spot acquisition opportunities earlier and evaluate more targets without proportionally increasing human resources.

Due diligence acceleration.

Generative AI is nowbeing appliedto virtual data rooms and diligence materials. Instead of armies of analysts combing through contracts, financial statements, and compliance records, AI tools can summarise findings, flag anomalies, and benchmark metrics against comparable companies.

Portfolio monitoring.

Together, these applications provide large-cap funds with what they value most: speed without sacrificing rigour, and insight without incurring ballooning costs.

2. AI in Portfolio companies: Driving operational value

While fund-level applications improve investment decisions, the real impact of AI often materialises within portfolio companies. For large-cap firms managing hundreds of billions in enterprise value across industries, embedding AI into operations can unlock transformative value.

Operational efficiency.

Revenue growth.

Strategic transformation.

ESG and compliance.

3. Challenges for large-cap firms

While the promise of AI is compelling, execution in large-cap contexts is complex.

Data fragmentation. Portfolio companies often operate across multiple geographies with legacy systems in place. Aggregating and cleaning data is usually the first, and hardest, step.

Scalability. A pilot project in one business unit is one thing; rolling out AI across a global enterprise requires governance, integration, and change management.

Talent constraints. AI expertise is scarce. Funds must decide whether to build in-house teams, partner with vendors, or encourage portfolio companies to hire directly. Often, the answer is a mix.

Regulatory and ethical risks. AI models must be explainable, compliant with data privacy laws, and free from bias. For sensitive deal data, ensuring confidentiality when using third-party AI platforms is critical.

Measuring ROI. Quantifying AI’s impact can be tricky, particularly when it influences multiple levers simultaneously. Clear KPIs are crucial for sustaining momentum and justifying investment.

Large-cap firms, with their scale and visibility, must address these issues head-on to capture AI’s benefits without incurring outsized risks.

4. A roadmap for AI in large-cap Private Equity

To move from experimentation to enterprise value, PE firms and their portfolio companies can follow a structured path:

Assess AI and data maturity. Begin with a diagnostic of fund processes and portfolio operations. Identify where data is fragmented and where AI pilots already exist.

Define high-impact use cases. Prioritise areas with clear potential for value creation, predictive maintenance, supply chain optimisation, pricing, churn reduction, or diligence acceleration.

Pilot and prove. Launch small pilots in selected portfolio companies or fund functions. Capture lessons, measure outcomes, and refine.

Build or partner for capability. Decide whether to invest in internal data science teams or collaborate with specialised vendors. Large-cap firms often use both approaches.

Establish governance. Create frameworks for AI ethics, data privacy, and regulatory compliance. Transparency builds trust with LPs, regulators, and employees.

Upskill teams. Train investment professionals and operating partners to interpret AI outputs and make informed decisions. Encourage portfolio companies to do the same.

Scale with measurement. Once pilots succeed, expand thoughtfully. Track metrics such as reduced downtime, increased revenue, margin improvements, or faster diligence cycles. Use these to demonstrate value to stakeholders.

This roadmap acknowledges both the promise and the complexity of AI. Success lies not in adopting AI for its own sake, but in aligning it directly with the value-creation levers that define private equity.

5. The strategic imperative

Ultimately, AI is not a silver bullet; it is a tool. Its effectiveness depends on leadership vision, data discipline, and the ability to embed technology into organisational culture. But firms that succeed will redefine what operational value creation looks like in the 21st century.

Conclusion: From advantage to necessity

AI is rapidly shifting from a competitive edge to a baseline expectation. For large-cap private equity firms, the question is no longer whether to adopt AI, but rather how quickly and effectively it can be integrated into fund processes and portfolio operations.

Those who move early and thoughtfully will capture outsized benefits: faster deal cycles, sharper diligence, more resilient operations, and stronger exits. Those who wait riskbeing left behindin a market where data, speed, and precision increasingly define success.

The opportunity is clear: AI is not just another technology investment. It is a strategic lever for the next era of private equity value creation.

​

AI is rapidly shifting from a competitive edge to a baseline expectation. For large-cap private equity firms, the question is no longer whether to adopt AI, but rather how quickly and effectively it can be integrated into fund processes and portfolio operations.