Private Equity (PE) is operating through a structurally different cycle. Global buyout activity has recovered from the 2022-23 trough, but on altered terms: exit markets remain uneven, holding periods have lengthened and record levels of dry powder are creating mounting pressure to deploy and realize capital. Industry data shows buyout-backed exits have declined sharply across trade sales, IPOs and sponsor-to-sponsor deals, while an increasing share of dry powder is now four years old or more.¹

As a result, Limited Partner (LP) conversations are shifting away from headline IRR toward Distributions to Paid-in Capital (DPI), cash generation and resilient value-creation models. Leading investment platforms are responding by investing in data and AI-enabled operating capabilities to scale value creation across portfolios. However, in many firms, the Finance & Accounting (F&A) function – critical to anchoring this shift – remains fragmented and manual, creating friction between operational progress and realized outcomes.

The PE Context

Today, the industry’s challenges revolve around finding good deals and converting unrealized value into cash. In response, large and sophisticated managers are re-thinking how value creation is executed, moving away from deal-specific transformation efforts to portfolio-wide operating capabilities anchored in data and Artificial Intelligence (AI).

One asset management firm is pursuing a dual-track AI strategy: embedding AI across ~190 portfolio companies while incubating 25 AI-native ventures.² Both initiatives are governed through a centralized operating model designed to scale AI capabilities consistently across the portfolio rather than deploy them opportunistically at the individual asset level.

A large alternative asset manager has established a centralized data science and AI function, supported by a distributed network of analytics leaders embedded across portfolio companies.³ This model has reduced valuation effort, delivering up to 50 percent lower valuation costs and 10x faster valuation cycles by standardizing analytics and decision workflows across investments.

A diversified, multi-asset investment platform with a proprietary technology backbone has focused on building a common data language across asset classes and extending it into private markets.⁴ By integrating external private-markets data into this platform, the firm has improved analytics consistency, comparability and decision confidence across investment management, portfolio oversight and exit preparation.

Taken together, these shifts highlight a clear direction: competitive advantage in PE is increasingly built through platform-based, data and AI-enabled value creation, rather than isolated, deal-by-deal execution.

The Finance Paradox
The Function that Measures Value but Limits Scale

The F&A function sits at the core of the investment lifecycle, translating operational decisions into financial outcomes and giving investors the visibility needed to manage performance and returns. It links operational levers – pricing, volume, cost – directly to EBITDA (Earnings Before Interest, Taxes, Depreciation and Amortization), cash and returns.

Yet as portfolios scale, finance often struggles to play this strategic role. The function tasked with measuring value is increasingly constrained by fragmentation and complexity. This is the finance paradox: finance is expected to guide value creation, even as its ability to do so diminishes.

The Portfolio F&A CoE
A Triple-lever Operating Model

A Portfolio F&A Center of Excellence (CoE) is a structural response to this finance paradox. It is a shared capability across the GP and portfolio that standardizes F&A processes, builds a cross-portfolio data foundation and embeds digital capabilities into finance workflows as re-usable platform assets.

This model is anchored in three distinct transformation levers.

• Fund Mechanics: Expertise across capital calls, distributions, DPI, Residual Value to Paid-in Capital (RVPI), carried interest and deal structures including leveraged buyouts, structured equity and secondaries

• Thesis Alignment: Translating deal theses into finance requirements, with monthly visibility metrics that validate or recalibrate the investment hypothesis

• Value-creation Archetypes: Processes tailored to buy-and-build platforms, carve-outs, turnarounds, infrastructure-style holds and tech / growth plays

• Sector Nuance: Specialized knowledge in vertical-specific Key Performance Indicators (KPI), such as annual recurring revenue and churn in SaaS or claims and reserving dynamics in insurance

• Unifies Portfolio Data Ingestion: Pulls ERP, GL, sub-ledger and planning data through consistent pipelines, regardless of source systems

• Integrates Operational Metrics: Incorporates sector-specific data, such as policy and claims metrics in insurance or subscriber metrics in SaaS, to directly support value creation

• Harmonizes Portfolio Dimensions: Aligns fund and strategy views with portfolio company, platform, geography and legal versus management structures

• Standardizes Canonical Metrics: Defines revenue, gross margin, normalized EBITDA and add-backs, cash conversion, working capital metrics, capex versus opex and sector-specific KPIs

• Establishes Institutional Intelligence: Creates a single economic source of truth and common financial language across funds, assets and operating teams

• Automation & Analytics: Streamlines Record-to-Report (R2R), Order-to-Cash (O2C) and Procure-to-Pay (P2P) flows, supported by diagnostic and predictive analytics for margin and working capital

• Gen AI: Accelerates insight generation by drafting board and IC commentary, variance explanations and first-cut portfolio review summaries

• Agentic AI: Uses autonomous agents to orchestrate actions, such as close and consolidation monitoring or working capital agents that identify trapped cash and trigger collections

A Scalable Execution Engine
The F&A CoE Pod Model

To operationalize these three levers, a portfolio F&A CoE is organized into specialized pods that work in concert across the fund and its portfolio companies to ensure smooth decisions and data flow. The pod-based structure provides a modular way to scale capabilities across the entire portfolio.

Portfolio
Finance Pod

• Owns the “single view” of performance by fund, strategy and asset
• Maintains KPI frameworks, EBITDA bridges, cash and working capital views
• Serves IC, investor relations and treasury teams with consistent portfolio-level reporting

• Runs or co-runs F&A for clusters of portfolio companies by sector or region
• Executes R2R, P2P, O2C and Financial Planning & Analysis (FP&A) processes using the unified operating model
• Provide “plug-in” support for carve-outs, high-growth assets and under-resourced finance teams

Portfolio
Company F&A
Operations Pod

Transformation
& Value
creation Pod

• Designs and executes cross-portfolio value-creation programmes (working capital, pricing, procurement, etc.)
• Converts portfolio-level insights into company-specific initiatives, timelines and impact tracking
• Works alongside operating partners and management teams

• Builds and operates the cross-portfolio finance data warehouse and semantic layer
• Publishes re-usable data products for finance and other functions
• Coordinates with CIO / CDO and technology teams on architecture, integration and governance

Data &
Platform Pod

AI & Agentic
AI Pod

• Designs, prioritizes, pilots and scales AI, Gen AI and Agentic AI use cases across the portfolio
• Curates a re-usable library of models and agents (for close, reconciliation, working capital, etc.)
• Ensures alignment between AI designs and the underlying data platform

• Oversees data, AI and process governance, in conjunction with risk, compliance and internal audit
• Tracks value realized against deal theses and value-creation plans
• Ensures the CoE remains compliant and transparently value-accretive

Governance &
Valuemanagement
Pod

Cross-pod Engagement
Use Case Examples Across the Portfolio

A few end-to-end examples illustrate how this model works.

Use Case 1

Use cross-portfolio working capital program

Portfolio Finance Pod

• Baselines Day Sales Outstanding (DSO), Days Payable Outstanding (DPO), Days Inventory Outstanding (DIO) and cash conversion at portfolio, sector and company levels using the warehouse

• Identifies outliers and high-impact levers

Transformation & Value-creation Pod

• Designs a working capital playbook

• Co-creates company-specific plans with CFOs and COOs

AI & Agentic Pod

• Deploys working capital agents that monitor aging, disputes and inventory patterns, highlight trapped cash and draft collection / re-negotiation actions

Operations Pod

• Implements process changes in billing, collections, accounts payable and inventory

Governance & Value-management Pod

• Tracks improvements in working capital days and cash release versus baseline and targets

Result: Lower DSO / DIO, optimized DPO, faster cash release and improved liquidity visibility across the portfolio

Use Case 2

Close acceleration and consolidation quality

Portfolio Company F&A Pod

• Standardizes close calendars and task lists across entities

Data & Platform Pod

• Ensures close-relevant data is well-modeled in the warehouse

AI & Agentic Pod

• Deploys close agents that monitor close progress, detect anomalies and propose standard accrual patterns

• Deploys reconciliation agents to reduce manual detective work between sub-ledgers and GL

Portfolio Finance Pod

• Uses near-final numbers earlier, enabling faster narrative building and management review

Result: 2-5 days faster close, fewer post-close adjustments and improved reliability during exit processes

Use Case 3

Covenant and liquidity monitoring

Portfolio Finance Pod

• Integrates facility and covenant data into the warehouse.

AI & Agentic Pod

• Deploys covenant agents that track key ratios versus thresholds and simulate shocks, such as EBITDA declines or rate changes

Transformation Pod

• Works with deal teams and CFOs to pre-emptively plan mitigations

Result: Fewer surprises, better lender communication and more room to maneuver on exits or refinancings

The Portfolio Advantage
How the CoE Compounds Value for the PE Firm

When a portfolio F&A CoE becomes fully operational, its impact becomes visible at three distinct levels:

Finance as a Strategic Asset
The Opportunity for this Decade's PE

A portfolio F&A CoE cannot be built by one capability alone. It requires deep finance-operating expertise and a technology backbone to run it at scale.

Partners with domain expertise bring process discipline, policy coherence and real-world understanding of how F&A operates across diverse portfolio companies. Technology partners provide the cloud, ERP, integration and data engineering foundations that enable a cross-portfolio data warehouse and an AI-ready platform to function reliably in complex and multi-system environments.

Together, these capabilities create far more than a shared service. They build a finance platform that shapes portfolio performance, turning data into conviction, insights into action and value-creation plans into repeatable outcomes. For firms competing on operating excellence, this becomes the strategic asset that carries the advantage forward into the next decade of private equity.

References

1. https://www.bain.com/insights/private-equity-outlook-liquidity-imperative-global-private-equity-report-2024/

2. https://www.ainvest.com/news/apollo-dual-ai-strategy-hedging-disruption-powering-portfolio-innovation-2512/

3. https://www.73strings.com/insights/case-study/how-blackstone-uses-73-strings-to-help-enhance-efficiency-and-accuracy-in-valuations-and-portfolio-monitoring/

4. https://www.blackrock.com/aladdin