AI in Private Equity: Automating Due Diligence

Artificial intelligence is becoming a component in private equity operations. Firms are deploying AI to improve various stages of the investment lifecycle, particularly in due diligence. The goal is to enhance efficiency, reduce manual effort, and potentially identify opportunities or risks that human analysis might overlook. However, the path to achieving substantial return on investment remains a practical challenge for many.

Market Adoption and Growth

The private equity sector is actively integrating AI into its core functions. Data indicates a clear trend toward increased AI use in investment decisions and deal evaluation processes.

Current Adoption Landscape

Approximately 95% of venture capital and private equity firms currently use AI in their investment decision-making. A significant portion of dealmakers, 49%, report using AI tools on a near-daily basis. Almost two-thirds of these firms have applied AI to due diligence and data analysis. The industry recognizes AI's potential for transformation. An estimated 84% of private equity funds anticipate a significant impact from AI. This anticipation is reflected in organizational changes, with 84% of private equity firms reportedly appointing a chief AI officer. This indicates a strategic commitment to AI integration at a leadership level.

Financial Impact of AI in PE

The financial landscape surrounding AI deals within private equity has shifted significantly. The deal value for AI and machine learning in private equity grew from $41.7 billion in 2023 to $140.5 billion in 2024. This represents a more than threefold increase. AI deals constituted 8% of total private equity deal value in 2024, an increase from 3% in 2023. The volume of private equity deals in AI increased by 49% year-over-year in 2025, from 104 deals in the first half of 2024 to 155 in the first half of 2025. Non-infrastructure AI deals also saw a 65% year-over-year increase in 2025. These figures demonstrate a focused investment in AI-centric companies and technologies by private equity firms.

Budget allocation also reflects this trend. Nearly half of private equity respondents are investing 25% to 50% of their budgets in AI projects. A further 38% anticipate spending more than half their budget on AI initiatives. By 2026, one-third of firms expect to invest over $100 million in AI. These financial commitments underscore the strategic importance placed on AI within the private equity industry.

Efficiency Gains and Productivity

One of AI's primary appeals in private equity is its potential to improve productivity and save time across various tasks, particularly in the labor-intensive due diligence process.

Streamlining Due Diligence Processes

AI tools are designed to shorten the duration of due diligence tasks. Activities that traditionally required hours can now be completed in minutes. Multi-week projects may be reduced to days. Reported productivity gains range from 35% to 85%. Time savings on review and analysis tasks are estimated at 60% to 70%. Manual due diligence hours can be reduced by up to 70% through AI-assisted document parsing. For example, Confidential Information Memorandum (CIM) processing time has been reduced by 93%, from 40 minutes to between 2 and 3 minutes.

Firms are experiencing increased deal flow capacity without proportional increases in headcount. A 50% increase in deal flow capacity has been observed in some instances. AI can identify 195 relevant companies in the time an analyst might identify one. This highlights the capacity of AI to process vast amounts of information quickly, allowing human analysts to focus on higher-value tasks.

Document Analysis and Review

Artificial intelligence, particularly large language models (LLMs), can process thousands of pages of documents. This allows firms to find relevant information quickly. Automatic extraction of key performance indicators, churn metrics, and cohort data from investor reports is possible. Contract analysis capabilities include reviewing hundreds of contracts instantly, identifying unusual clauses, and summarizing terms. One platform, Xapien, generates summarized reports in under 10 minutes. This level of automation significantly reduces the time and effort required for document-heavy aspects of due diligence.

Deal Sourcing Enhancements

AI systems contribute to deal sourcing by autonomously scanning market data. This process helps identify potential acquisition targets. Platforms such as Grata, Cyndx, and Inven are used for middle-market dealmaking. Inven, for instance, provides data on 23 million companies globally. Blackstone uses AI to analyze financial reports, industry trends, and news sentiment to inform its deal sourcing strategies. These tools allow firms to expand their search parameters and identify a broader range of potential investment opportunities.

Portfolio Monitoring

AI also supports ongoing portfolio monitoring. KKR uses machine learning algorithms to analyze market trends. This allows the firm to adjust strategies in real time. Autonomous agents can monitor portfolio key performance indicators continuously. Predictive pricing, supply chain optimization, and customer analytics are areas where AI drives improvements in EBITDA. This proactive monitoring enables firms to respond to market changes and optimize portfolio performance more effectively.

Real-World Outcomes and Challenges

Despite widespread adoption and investment, the practical impact of AI on financial performance for many companies remains limited. This indicates a gap between expectation and execution.

Measuring AI's Return on Investment

Only 20% to 25% of companies have production-ready generative AI applications. This suggests that while many are experimenting, fewer have successfully moved beyond pilot projects. A smaller percentage, 10%, have achieved significant return on investment, with another 11% reporting moderate returns. A study from MIT indicates that 95% of companies have seen little to no profit and loss impact from their AI initiatives. These figures suggest that while AI offers substantial theoretical benefits, realizing these benefits in practice requires careful implementation and strategic focus. Simply deploying AI tools does not automatically translate into financial gains. Firms must develop clear strategies for AI adoption to ensure it impacts workflows and financial outcomes positively. For guidance on developing such strategies, consider exploring our AI governance solutions.

Key AI Capabilities in Practice

Specific AI capabilities are proving valuable across the private equity investment lifecycle, from initial screening to post-acquisition management.

Document Analysis

The ability of large language models to process unstructured data is significant for due diligence. They can review legal documents, financial statements, and operational reports. This analysis identifies critical clauses, financial health indicators, and operational inefficiencies at a speed manual processes cannot match. This capability reduces the risk of overlooking material information during time-sensitive deal evaluations.

Deal Sourcing

AI platforms support deal sourcing by aggregating and analyzing vast datasets. These datasets include public company filings, private company data, industry reports, and news feeds. The systems can identify companies meeting specific investment criteria, track market trends, and even assess competitive landscapes. This proactive approach to deal origination can enhance proprietary deal flow and reduce reliance on traditional sourcing channels.

Portfolio Monitoring

For existing portfolio companies, AI offers continuous monitoring capabilities. This involves tracking operational metrics, market sentiment, and competitive developments. AI can predict potential issues or identify growth opportunities earlier than human analysis alone. This allows for more timely interventions and strategic adjustments to maximize asset value. Implementing an AI-driven playbook can further refine these processes. More information on operational playbooks can be found on our playbook page.

Notable Platforms

A range of specialized AI platforms has emerged to serve the specific needs of private equity firms.

Specialized AI Tools for Private Equity

Keye is an AI solution developed by private equity investors for the industry. ToltIQ offers an AI-driven due diligence platform specifically for Virtual Data Room (VDR) content. Xapien focuses on compliance and open-source intelligence reports. Grata provides middle-market intelligence and semantic search capabilities. Cyndx assists with fundraising and merger and acquisition activities. Affinity specializes in relationship intelligence. Standard Metrics supports portfolio monitoring for venture capitalists. Blackstone Document AI is used for contract and document analysis within Blackstone's operations. These platforms represent a growing ecosystem of tools designed to address specific pain points in private equity.

2026 Predictions

The future trajectory of AI in private equity points toward increased autonomy and strategic integration.

Predictive Trends in AI for PE

Agentic AI systems are predicted to become mainstream. These systems will sense, decide, and act in real time. This implies a shift towards more autonomous AI operations across various workflows. The focus for firms will be on intentional, top-down AI programs. These programs will target high-impact workflows with clear business objectives. Responsible AI frameworks covering bias, transparency, and governance will become non-negotiable. This emphasizes the need for ethical and accountable AI deployments. Furthermore, production deployments demonstrating tangible value will be required. Proof-of-concept projects alone will no longer suffice. These predictions indicate a maturing AI landscape where practical, governed applications are paramount.

Major Firms Using AI

Several prominent private equity firms are actively integrating AI into their operations, demonstrating diverse applications.

Industry Leaders and AI Integration

Blackstone utilizes AI for deal sourcing and employs Document AI for contract analysis. KKR applies AI to portfolio optimization, risk assessment, and performance forecasting. General Atlantic and Schroders Capital have integrated dedicated AI roles onto their investment committees. BayPine focuses on driving digital transformation within its portfolio companies using AI. GrowthCurve Capital employs AI for due diligence and enhancing portfolio value creation. These examples illustrate the varied approaches and strategic commitment of leading firms to AI adoption.

Private equity is increasingly adopting AI to streamline due diligence, enhance deal sourcing, and optimize portfolio management. While the technology offers significant efficiency gains and the potential for new insights, firms must move beyond experimental phases to achieve demonstrable return on investment. The focus is shifting towards deliberate, integrated AI strategies with clear governance and a commitment to practical application.

To understand how AI can improve your due diligence processes, consider an expert review of your current systems. Visit our audit page for details. For broader AI integration strategies within your firm, explore our comprehensive services.