AI-Driven Due Diligence Through To Exit: How Private Equity Firms Are Using GenAI in 2025

Thanks to advancements in generative AI (GenAI), private equity (PE) firms are learning the value of AI-driven due diligence. According to Pictet’s 2024 Private Equity Comes to Grips with AI survey, nearly two-thirds of private equity general partners are running GenAI pilots, and over 40% use it in their business processes. 

Yet, adoption still lags. Deloitte’s 2025 State of Generative AI report found that 35% of organizations are hesitating to adopt GenAI because it can produce errors. This applies to all financial organizations, but PE firms in particular, as mistakes can be critical in portfolio management and due diligence. 

There’s a reason to be skeptical. Too many deployments still run on generic models, black-box tools, or half-baked pilots. Additionally, not every private equity firm knows how to deploy or use the technology effectively.

This article covers how private equity firms should embed GenAI tools from due diligence through to exit. We’ll cover deployment strategies, success metrics, and the adoption challenges that you must learn to avoid.

GenAI Touchpoints in the Private Equity Lifecycle

Generative AI is applicable at every stage of the private equity lifecycle, from deal sourcing to due diligence. Let’s discuss how PE firms are using AI to provide deeper insights for investment decisions, significantly enhance deal closing times, and maximize returns.

Due Diligence and Red Flag Scanning

Information overload and time pressure can affect compliance due diligence for private equity firms. GenAI capabilities allow for processing structured and unstructured information in volumes far beyond those that humans can review during a deal window. We’re talking contracts, litigation records, regulatory disclosures, social media posts, and media reports.

AI-driven due diligence tools can help analyze the quality of software assets, codebases, and the scalability potential. In addition, GenAI initiatives can assess the company’s cybersecurity position and data privacy compliance.

GenAI for PE firms can be trained on compliance data, sanction watchlists, and corporate relationship networks to detect connections between seemingly unrelated entities. Consider this: AI could help you discover that a founder has connections to politically exposed persons or sanctioned individuals before you proceed with a deal.

Target Company Discovery

GenAI can analyze data from private databases (like Apollo, PitchBook, and Crunchbase), company websites, hiring pages, job listings, and social media networks. It’s a more comprehensive approach than traditional screening.

What makes GenAI useful is its ability to learn from failed deals. You can train the model to screen for specific risks, like whether a deal fell through due to cap table complexity or misalignments with C-level management.

ESG Compliance

GenAI helps ensure compliance with environmental, social, and governance (ESG) standards. Beyond static scoring, these tools analyze emissions data, disclosures, whistleblower reports, and regulatory feeds.

AI-driven tools can detect environmental risks that are often hidden from traditional screeners. For example, a manufacturing target might have a clean compliance record but was recently named in activist posts on social media. AI can detect these connections—even across non-English sources.

Value Creation

After due diligence, generative AI for PE becomes a value accelerator. AI agents with generative capabilities can monitor key performance indicators (KPIs), such as EBITDA targets, lead quality, or acquisition costs for clients.

Predictive models base their findings on prior transactions, operational telemetry, and external benchmarks. They can trigger alerts on deviations, predict problems before they happen, and suggest remediation paths based on historical data.

Automating Investment Memos

GenAI assists deal teams with investment memo drafts using real-time data sources and firm-specific templates. Your team can input a data room, a Confidential Information Memorandum (CIM), and meeting transcripts. AI-driven diligence software will generate a thesis outline with supporting arguments and risk disclaimers.

Consistency is another benefit. PE firms can configure AI to include baseline metrics, red flag disclosures, and IRR (internal rate of return) models with specific logic. AI tools with revision control also help highlight changes between drafts, so the final memo isn’t missing important information.

Exit Planning

AI technologies will help you translate years’ worth of enterprise proprietary data into pitches that can help sell the company. Generative AI in private equity can help demonstrate the company’s competitive edge, positive changes during your ownership, and potential for future growth. After all, buyers are more likely to up their offering after experiencing a well-organized narrative supported by data.

Instead of static reports, GenAI tools can build presentations with interactive models, valuation bridges, and forecasting scenarios. Better yet, the presentations can be readjusted to match each buyer’s priorities.

Unlike rule-based AI systems, GenAI for private equity improves the more it’s used. Due diligence processes, investment memos, performance reviews, and exit plans train the model, helping it refine its prediction accuracy and context awareness for future deals.

According to Pictet’s 2025 survey, over 60% of respondents attribute revenue increases at their portfolio companies to AI (mostly due to headcount reduction and productivity gains). How exactly do private equity companies measure the impact of AI? 

Benchmarks & Success Metrics for AI-Driven Diligence Implementation

It’s important to understand whether generative AI tools deliver real value. Measurable indicators should tie to real outcomes, such as returns, operational gains, and risk mitigation. 

Below are some of the key metrics you should prioritize for your AI initiatives.

Investment Return

• IRR delta. The incremental boost in Internal Rate of Return (IRR) attributed to GenAI-enabled diligence improvements.
• Value leakage avoidance. Flagged problems (technical debt, regulatory exposure, intellectual property problems, etc.) used to mediate the price.
• Deal fall-through prevention. The number of high-risk deals avoided due to AI-detected problems.

Deal Cycle Compression

• Due diligence time. An average time saved on due diligence after implementing GenAI.
• Time-to-Insight reduction. Time needed to surface material diligence concerns before and after AI integration.
• Analyst hours saved. Number of full-time equivalent (FTE) hours replaced or augmented by AI.

Precision and Coverage

• Issue detection accuracy. Percentage of GenAI-flagged potential risks that humans later confirm as material (without irrelevant issues or false positives).
• False negative rate. Number of issues that AI misses during due diligence.
• Coverage expansion index. Previously skipped (or inconsistently checked) risk domains and data layers covered after implementing GenAI.

Technical Risk Detection

• Technical debt assessment. Percentage of technology-driven targets where AI detects flaws (architecture design issues, legacy code, etc.).
• Security vulnerability identification. Number of cybersecurity weaknesses GenAI found compared to traditional due diligence methods.
• Issue exposure. Accuracy of GenAI flagging of critical third-party software risks and over-reliance on external providers for core functions.

Adoption and Feedback Integration

• Utilization rate. AI application touch-points across due diligence and PE stages.
• Feedback loop closure. Number of times analysts correct AI outputs and whether those corrections are used to train the model.
• Confidence score concordance. Correlation of confidence scoring with deal outcomes.
• Error rates. Changes in model accuracy or error frequency over time (basically, model degradation rates).

Success is only achievable if the system is integrated, monitored, and refined carefully. Usually, companies encounter roadblocks during the implementation.

Common Roadblocks of AI-Driven Due Diligence

In practice, PE firms often encounter several issues that can sabotage the adoption. Each one has consequences, but all of these can be addressed.

Security and Compliance

GenAI expands the attack surface because it has to process sensitive due diligence data, which is often logged through third-party cloud platforms. That’s why PE firms risk violating regulations (GDPR, HIPAA) and leaking confidential deal materials.

Solutions:

• End-to-end encryption (AES-256 or better) for data at rest and in transit.
• Zero-trust security architecture and least-privilege permissions.
• Data anonymization tools that replace personally identifiable information with artificial, realistic data (primarily for training and advanced analytics).
• Vendor compliance audits, including proof of independent penetration testing and certification renewals.
• Internal AI security officers who enforce compliance checklists in AI procurement and deployment workflows.

Data Readiness

AI models require structured, domain-specific, and high-quality training data to maintain context awareness and accuracy. In the worst-case scenario, the AI is likely to drift and hallucinate without regular reviews and fine-tuning.

Solutions:

• Train GenAI models on domain-specific legal, financial, and regulatory documents from past transactions.
• Preprocess due diligence files with OCR, classifiers, and embedding models.
• Create human-in-the-loop systems to validate AI summaries before use.
• Use prompt testing libraries to test how the model responds to different scenarios and edge cases.

Explainability and Trust

To be able to trust the GenAI outputs in due diligence, every decision must be auditable and trackable. It’s useless to adopt a model if it can’t show how it arrived at its conclusions if mergers are on the line.

Solutions:

• Use platforms that log sources, confidence scores, and documentable tracebacks.
• Integrate explainability layers such as SHAP, LIME, or model interpretation APIs.
• Mandate internal validation of all critical AI outputs before they enter deal memos.

Talent and Expertise

AI initiatives can stall because employees don’t know how to use them fully in their daily routine. Firms may also lack data specialists who can interpret and manage GenAI outputs.

Solutions:

• Invest in targeted training on prompt writing, model behavior, and flag validation for employees.
• Appoint cross-functional GenAI champions who will share technical literacy.
• Adopt visual workflow tools that let analysts interact with models without needing to code.
• Set up internal QA loops so teams can test GenAI performance before pushing outputs into production.
• Adopt low-code platforms to build, test, and adapt AI models without hiring more software engineers with machine learning skills.

What Operating Partners Are Doing Differently in 2025

Now, with the mass adoption of GenAI, many companies have created specialized roles, like AI Operating Partners. Operating partners are often involved with every aspect of PE firms.

As the senior partner of QuantumBlack put it in a 2024 McKinsey podcast, 60% of private equity companies have portfolio companies experimenting with generative AI, but only a small percentage have scaled these efforts. To capitalize on the potential, firms must first tackle the foundations. 

For an operating partner, AI strategies are now part of core value creation. They oversee GenAI deployments within portfolio companies, making sure they can accelerate due diligence and automate manual workflows.

Since 2024, private equity companies have been actively looking to hire AI operating executives who have relevant technical expertise and can help deploy the technology at portfolio companies. And effective deployment starts with planning.

Roadmap: Building AI Maturity Inside the Fund

Organizations need a deliberate path to adopt AI in a way that will compound in value. Our roadmap breaks down the key phases with checklists.

Initial Prototyping and Experimentation

At this stage, you should focus on repeatable applications of GenAI, preferably in areas that allow you to measure performance objectively. Learn using prototypes and focused pilots while keeping the risk low.

• Start with targeted use cases (contract extraction, red flag scanning, memo generation, etc.).
• Define clear success metrics for a deep understanding of the impact of GenAI on your operations.
• Test inside sandbox environments or short-term test contracts rather than demo datasets.
• Document results, problems, and lessons for internal reference.

Evaluating and Choosing AI Partners

Firms should run thorough tech evaluations of GenAI for PE tasks and actual deal materials. The goal is to understand if the model supports real use cases.

• Ask vendors to let you try the platform for real applications (full-length contracts, multilingual media checks, entity resolution tasks, etc.).
• Compare outputs to human analyst results on multiple cases.
• Check for certifications (SOC 2, ISO 27001, GDPR, HIPAA, CCPA, etc.).
• Simulate real deal cycles and monitor speed (latency, throughput, etc.).

Embedding into Strategic Processes

After the pilots, GenAI tools can be implemented into due diligence and PE workflows.

• Push toward integrations with deal flow software, CRM, or VDR platforms for seamless usage.
• Create a shared library of prompts, output templates, and red flag typologies for internal use.
• Assign governance roles (such as AI leads, technical advisers, and data officers).
• Define a review threshold (types of AI output that must be human-validated).
• Conduct structured output (performance) reviews and compliance audits.

Scaling AI Across Fund Operations

AI tools should evolve in parallel with the firm’s portfolio strategy, risk appetite, and compliance requirements.

• Build a shared library of prompts, output templates, and red flag typologies for internal reuse.
• Ensure tools support model customization and retraining on firm-specific data.
• Build internal fallback plans (who owns the model if the adoption fails or the vendor sunsets the product?).
• Ensure your AI workflows, data, and outputs can migrate if the vendor relationship ends (to avoid lock-in).

Conclusion

In 2025, leading private equity organizations are building GenAI systems into the deal lifecycle. The technology can change how you conduct target screening, red flag scanning, memo automation, and exits. But the real value comes only when the AI is tailored to your company’s workflows, runs on domain-specific data, and produces verifiable outputs.

Dynamiq can help you prototype, validate, and train GenAI models and AI agents on custom data inside your own infrastructure. Our low-code AI workflow builder and audit-friendly observability features will help you launch AI-driven diligence and other operations at scale without locking in with external consultants.

Want to train your GenAI for PE and due diligence in days? Contact us to learn how.