Executive Summary
Artificial intelligence (AI), especially generative AI (GenAI), is swiftly becoming a transformative force in private equity (PE). A Bain survey of private investors managing $3.2 trillion in assets found that most portfolio companies are participating in some phase of AI testing, with nearly 20% having operationalized GenAI use cases. Likewise, EY reports that 74% of PE-backed companies are piloting or actively using AI in their transaction processes.
However, industry adoption remains inconsistent, driven by concerns over data privacy, unclear return on investment (ROI), and regulatory uncertainty. Despite these challenges, early adopters are already seeing measurable results in deal sourcing, due diligence, portfolio monitoring, and value creation.
This white paper examines how leading PE firms leverage AI, the challenges they face, and the strategic steps needed to unlock AI’s full potential.
AI-Powered Deal Sourcing and Operations
AI tools are greatly enhancing the speed and accuracy of deal origination. For example:
- A large European fund developed a platform that processes millions of data points to identify high probability investment targets. Initially used in venture capital, the system is now applied across multiple asset classes.
- Another global firm uses AI to scan market trends, news, and social content to identify early-stage companies aligned with its investment thesis.
AI also streamlines internal fund operations by automating CRM workflows and legal contract analysis, generating investor reports and marketing materials with GenAI assistants, and accelerating RFP responses and software development processes.
Vista Equity Partners exemplifies this trend. The firm has integrated AI into its core operational planning, requiring each of its 85+ portfolio companies to outline annual GenAI goals. Hackathons and CEO councils encourage cross-portfolio collaboration, while AI copilots have boosted productivity by up to 30% in software development tasks.
AI-Enabled Smarter Due Diligence
AI is increasingly improving the due diligence process:
- Modern AI platforms can now analyze more than 10,000 customer reviews in minutes, extracting sentiment insights and identifying flaws in products or services.
- Companies use AI to evaluate disruption risks, model market scenarios, and design new business models.
- According to Bain, one buyout firm developed a competing prototype of a target company’s proprietary AI solution during the diligence phase and discovered that it outperformed the original, ultimately influencing the investment decision.
As we move forward, AI-powered diligence scorecards—assessing data readiness, use case viability, and innovation history—are likely to be as standardized as financial or legal due diligence.
AI as a Catalyst for Portfolio Growth and Efficiency
PE firms utilizing AI throughout their portfolio companies are experiencing substantial value creation. Notable examples include:
- LogicMonitor (Vista): Its AI agent, Edwin AI, delivers an average of $2 million in annual savings per customer through predictive IT monitoring.
- Cengage (Apollo): AI projects have reduced content production costs by 40%, improved customer care efficiency by 15%, and automated up to 20% of sales processes.
- Multiversity Group (CVC): Launched an “MVP accelerator” to test over 30 AI initiatives, including bots that reduced professors’ time spent on routine questions by 80%.
These AI-driven efforts improve profit margins, strengthen exit narratives, and enhance the attractiveness of portfolio companies to future buyers.
Establish AI Centers of Excellence and Collaborative Ecosystems
Successful AI mobilization often relies on strong internal leadership and structured support systems. For example, Apollo has established a Center of Excellence (CoE) staffed by internal partners and guided by an external advisory board. This CoE is responsible for evaluating AI vendors, conducting workshops to align technology with strategic goals, and assisting portfolio companies in identifying three to five use cases linked to core business objectives.
Similarly, firms like Hg promote shared learning and collaboration across their portfolios by utilizing their expertise in mid-sized software providers to scale and distribute GenAI solutions.
Overcoming Barriers in AI Adoption
Despite increasing optimism, several risks need to be addressed:
- Cost Overruns: Firms risk overinvesting in AI pilots that deliver low ROI.
- Data Risks: Poor-quality inputs can lead to biased or inaccurate outputs.
- Model Unreliability: Hallucinations and black-box reasoning challenge explainability.
- Privacy and IP Concerns: Sensitive proprietary data must be protected against misuse or exposure.
- Ethical and Social Risks: Deepfakes, misinformation, and biased training data remain pressing concerns.
- Regulatory Uncertainty: Ensuring compliance with GDPR, SEC guidelines, and emerging AI rules is complex and evolving.
For example, a Scandinavian PE firm established an AI compliance forum that prohibits the uploading of personal data or firm-specific information into public Large Language Models (LLMs), ensuring compliance with GDPR and minimizing operational risk.
AI and Portfolio Valuations: A Regulatory Catalyst
AI could address one of private equity’s most persistent challenges—valuation opacity, which can lead to reduced valuations. Deloitte forecasts that AI-driven valuation tools will:
- Accelerate reporting cycles by including non-financial data such as foot traffic, app usage, and hiring patterns.
- Enable more regular valuations to enhance compliance and improve transparency for limited partners (LPs).
- Assist in addressing the “denominator effect,” where outdated valuations create artificial overallocations in PE portfolios during market downturns.
This trend aligns with evolving regulatory frameworks in the U.S. and EU, which are facilitating access for retail investors and raising the demand for real-time disclosures.
Approaches to AI Transformation in Private Equity
As private equity firms navigate the evolving landscape of AI, a strategic approach to its adoption can help unlock value across fund operations and portfolio companies. The following outlines key areas where AI can be integrated to support transformation.
Strategic Integration Across Investment Firms
AI can be integrated into investment strategies and operational models by:
- AI Readiness Assessments: Evaluating a firm’s digital maturity, data infrastructure, and AI potential to identify impactful use cases aligned with strategic priorities.
- Center of Excellence Design: Establishing internal or external Centers of Excellence (CoEs) to nurture AI experimentation, enhance capabilities, and promote knowledge sharing among portfolio companies.
- Governance and Risk Management: Establish governance frameworks, ethical use guidelines, and data privacy protocols to mitigate risk, ensure compliance, and foster stakeholder trust.
Operational Efficiency Enablement
At the fund level, AI can streamline processes and reduce overhead in areas such as:
- Deal Origination: Utilizing custom platforms to evaluate and score investment opportunities by employing proprietary and third-party data.
- Due Diligence: Tools that analyze and summarize large datasets, including financials, customer feedback, and market trends, to improve the speed and depth of diligence.
- Valuation Enhancement: AI-driven tools that enable more frequent and comprehensive portfolio valuations by utilizing structured and unstructured data, aligned with evolving regulatory expectations.
Portfolio Company Enablement
AI initiatives at the portfolio level can enhance EBITDA improvement and speed up digital maturity through:
- MVP Accelerators and AI Labs: Collaborating to develop AI pilots and minimum viable products across customer experience, R&D, and operations functions.
- Workforce Transformation: Facilitating change management, conducting training programs, and integrating AI into workflows to support adoption and cultural alignment.
- Industry-Specific Solutions: Utilizing domain-specific AI applications across sectors like software, healthcare, education, and manufacturing to meet unique business needs.
Comprehensive AI Lifecycle Support
A comprehensive AI strategy typically includes:
- Advisory and Strategy: Performing portfolio scans, prioritizing use cases, creating playbooks, and measuring ROI.
- Engineering and Implementation: Developing and integrating tailored AI models into enterprise systems while ensuring scalability.
- Managed Services and Support: Offering continuous monitoring, optimization, and compliance oversight to maintain long-term value.
The Time for Artificial Intelligence in Private Equity Is Now
AI in private equity is now a strategic necessity. Firms like Vista, Apollo, and CVC show that aligning AI with business objectives can improve returns, boost transparency, and future-proof portfolios. As adoption accelerates and regulatory demands grow, firms that invest wisely in AI will become tomorrow’s market leaders.
Wipro emphasizes a pragmatic and precise approach to AI adoption in this rapidly evolving landscape. The focus is on delivering measurable outcomes through targeted initiatives that align with broader strategic goals. Whether organizations are piloting GenAI use cases or planning a full-scale portfolio transformation, the aim is to ensure that the AI efforts are purposeful, scalable, and integrated into long-term value creation.
PE firms need trusted advisors to guide implementation, integrate AI solutions, and ensure long-term success across complex portfolios. The time to act is now.
