AI and Machine Learning in Fund Management: How 93% of PE Firms Plan Major Adoption by 2028

Only 2% of private equity firms expect to realize significant AI-driven value in 2025, yet 93% anticipate moderate to substantial benefits within three to five years. This dramatic expectation gap reveals the fund management industry stands at an inflection point—early enough that competitive advantages remain available, late enough that adoption delays risk operational obsolescence.

The Current State of AI Adoption in Fund Management

Artificial intelligence has captured 50% of global venture capital funding, with more than half of survey respondents identifying AI and machine learning as top investment priorities for 2025. Yet this investment enthusiasm masks a slower operational adoption curve within fund management firms themselves.

The gap between investment thesis and operational implementation creates strategic tension. Fund managers evaluate AI-powered portfolio companies while their own operations rely on manual processes and legacy systems. This disconnect becomes particularly visible in deal sourcing, due diligence, and portfolio management—precisely the areas where AI delivers measurable efficiency gains.

Market Investment Versus Operational Deployment

Private AI deal volume has tripled since 2020, with software representing nearly half of all AI-related investments. In 2025, AI deal counts reached 2,250 with total deal value at $65 billion. Despite declining 40% year-over-year in total private capital fundraising, an unprecedented proportion of newly raised capital is earmarked specifically for AI investments.

This investment activity contrasts sharply with internal adoption rates. While fund managers deploy billions into AI companies, their back-office operations, investor relations workflows, and fund administration processes largely remain manual. The operational efficiency gains available through AI adoption—30% to 50% improvements in specific workflows—sit untapped.

Proven Efficiency Gains Across Fund Operations

Firms already implementing AI tools report concrete, quantifiable improvements across core operational areas. These gains extend beyond theoretical benefits to demonstrated time savings, cost reductions, and capacity expansions.

Deal Sourcing and Pipeline Management

83% of firms using AI in deal sourcing report meaningful efficiency improvements. Machine learning algorithms analyze thousands of potential targets against investment criteria, market conditions, and historical performance patterns—work that previously required weeks of analyst time. Firms report 50% increases in deal evaluation capacity without adding staff, fundamentally changing the economics of origination teams.

Natural language processing extracts key information from pitch decks, financial statements, and market reports, creating structured data for comparative analysis. This automation allows investment professionals to focus on relationship building and strategic evaluation rather than information gathering and organization.

Due Diligence and Risk Assessment

81% of firms report AI-driven efficiency gains in due diligence processes. AI systems analyze financial statements, contracts, regulatory filings, and market data simultaneously, identifying risks and opportunities that manual review might miss. Pattern recognition algorithms flag anomalies in revenue recognition, expense categorization, or working capital management.

Document review—traditionally consuming hundreds of billable hours—now happens in days rather than weeks. AI extracts key terms from acquisition agreements, identifies unfavorable provisions, and benchmarks deal structures against historical transactions. This acceleration doesn't replace legal expertise but amplifies it, allowing attorneys to focus on negotiation and risk mitigation rather than document review.

Portfolio Operations and Value Creation

69% of firms using AI for portfolio operations report meaningful productivity improvements. Portfolio monitoring systems track KPIs across dozens of companies in real-time, automatically flagging variances from budget or covenant thresholds. Predictive analytics identify operational issues before they impact financial performance, enabling proactive management interventions.

AI-powered financial planning tools model different growth scenarios, pricing strategies, or cost reduction initiatives across portfolio companies. These simulations help operating partners prioritize value creation initiatives and allocate support resources effectively.

AI Applications in Fund Administration

Beyond investment activities, AI transforms fund administration operations—the middle and back-office functions that consume significant resources yet rarely differentiate fund managers competitively. Automation in these areas delivers cost savings that flow directly to fund economics.

Automated NAV Calculations and Reconciliation

Robotic process automation handles daily or intraday NAV calculations, pulling valuations from multiple data sources, applying waterfalls, and reconciling cash movements automatically. Anomaly detection flags discrepancies for human review while routine calculations proceed without manual intervention. Early adopters reclaim days from quarter-end close cycles.

This automation extends to investor reporting. AI systems generate customized reports for each LP, applying their specific fee structures, currency preferences, and performance metrics. What previously required days of manual report assembly now happens automatically upon period close.

Natural Language Processing for Fund Documents

NLP extracts calculation rules directly from Limited Partnership Agreements and Private Placement Memoranda, converting legal language into executable logic for waterfall calculations. This eliminates manual interpretation errors and ensures distributions precisely match governing documents.

Rule-based engines interpret different waterfall structures—European versus American waterfalls, varying hurdle rates, catch-up provisions, and clawback mechanisms—without custom coding for each fund. As firms manage more funds with varying terms, this flexibility becomes operationally critical.

The 2026 Market Environment Demands AI Adoption

Private equity sits on over $2.5 trillion in undeployed capital, with improving financing conditions expected to sustain deal momentum well into 2026. However, 70% of surveyed buyers expect M&A volumes to rise, intensifying competition for quality assets. In this environment, operational efficiency becomes competitive advantage.

LP Expectations for Transparency and Reporting

Nearly three-fourths (73%) of asset management executives consider AI critical to their organization's future. This perspective extends to LP evaluation of GP capabilities. Limited partners increasingly expect real-time portfolio access, automated ESG reporting, and sophisticated analytics—capabilities difficult to deliver without AI-powered systems.

The ILPA's updated 2025 reporting templates raise transparency standards further. Firms unable to generate required reports efficiently face either significant labor costs or allocation disadvantages relative to operationally sophisticated competitors.

Talent Allocation and Operational Leverage

As private equity firms compete aggressively for investment talent, operational automation becomes a retention tool. Investment professionals prefer analyzing opportunities to compiling reports or managing administrative workflows. AI systems that eliminate low-value tasks improve both productivity and job satisfaction.

Firms report 50% increases in deal evaluation capacity without adding staff. This operational leverage matters in competitive hiring environments where compensation costs rise faster than fee revenues. Technology-enabled productivity allows smaller teams to manage larger portfolios effectively.

Implementation Roadmap for Fund Managers

The gap between current 2% significant-value-realization and projected 93% moderate-to-substantial benefits within three years suggests a clear adoption pathway for fund managers willing to invest now.

Starting with High-ROI Use Cases

Fund managers should prioritize AI applications where efficiency gains are proven and implementation complexity is manageable. Investor reporting automation, document review in due diligence, and portfolio company KPI monitoring deliver quick wins that build organizational confidence in AI capabilities.

Avoid the temptation to build custom AI systems internally unless technology development is a core competency. Specialized fund management platforms integrate AI capabilities specifically designed for private markets workflows, reducing implementation risk and accelerating time-to-value.

Data Infrastructure as Foundation

AI effectiveness depends on data quality and accessibility. Fund managers should audit existing data across systems—investor databases, portfolio company financials, deal documentation, and market research—identifying gaps and inconsistencies. Centralizing this data creates the foundation for AI applications.

Cloud-based platforms solve both data integration and AI deployment challenges simultaneously. Modern fund administration systems centralize operational data while embedding AI capabilities for reporting, compliance, and analytics.

Change Management and Organizational Adoption

Technology deployment represents the easier component of AI adoption. Organizational change management—training teams, revising workflows, and building comfort with AI-generated outputs—determines success. Start with departments experiencing acute pain points from manual processes, where efficiency gains will be most appreciated.

Maintain human oversight initially, using AI outputs to supplement rather than replace existing processes. As teams build confidence in AI accuracy and reliability, gradually expand automation scope.

Competitive Dynamics and Strategic Timing

The current adoption gap creates temporary competitive advantages for early movers. Firms implementing AI now will have refined workflows and demonstrated efficiency gains when the majority of competitors begin adoption in 2026-2027.

This operational sophistication becomes increasingly visible to limited partners during fundraising. The ability to provide real-time portfolio analytics, respond to DDQ questions instantly from centralized databases, and demonstrate operational leverage through technology differentiates fund managers in competitive allocation processes.

However, the window for differentiation is closing. As AI capabilities become embedded in fund management platforms, operational parity will shift from competitive advantage to table stakes. Fund managers who delay adoption risk moving from early-adopter premium to late-adopter penalty.

Sources

• Grant Thornton (2025). Global Survey: AI is Transforming Asset Management - 73% of executives say AI is critical to organizational future
• EY (2025). Private Equity Trends 2026: Leading Through Change - Only 2% expect significant AI value in 2025, but 93% anticipate benefits within 3-5 years
• Ropes & Gray (2025). Artificial Intelligence Global Report H1 2025 - AI deal value increased 127% from H1 2024
• Crunchbase (2025). Big AI Funding Trends Charts - AI accounts for more than 50% of global venture capital funding
• getdynamiq.ai (2025). AI-Driven Due Diligence in Private Equity - 95% of firms report 30%+ efficiency gains, with 50% increases in deal evaluation capacity