Welcome to this week's edition of The Private Capital Compass, brought to you by Private Capital Global (PCG), a Sparc Group company. 

This week, we spotlight the strategic imperatives driving value creation in an increasingly complex private capital environment, from AI applications that deliver measurable EBITDA impact to rapid exit readiness strategies that transform market headwinds into competitive advantages.

We examine how middle-market firms are navigating deployment challenges in private credit, the critical role of talent selection in PE value creation, and emerging trends in covenant flexibility that signal shifting borrower dynamics across the capital markets.

We hope you enjoy the newsletter! 

Compass Points

Key insights at a glance:

• Private Equity Builds Dry Powder for a Dealmaking Rebound: According to recent data published by BDO, nearly half of private equity fund managers and operating partners (41%) are prioritizing dry powder for new platform investments, with 27% focusing on distressed opportunities, positioning to capitalize as M&A activity recovers. Despite geopolitical tensions and macroeconomic headwinds, firms are encouraged by progressing trade negotiations, a new tax cut bill, and potential interest rate reductions, with equity favored as the primary financing source for acquisitions (30%). 

• Rapid Exit Readiness Increases Valuations: Alvarez & Marsal says 2025 PE exits are bogged down by record holding periods (5.8 years), high rates, valuation gaps, and volatility, creating a $3T backlog and liquidity strain. Their prescription: a Rapid Portfolio Exit Readiness plan that prioritizes near-term, quantifiable value creation (cost, margin, working capital, commercial acceleration), full integration and bolt-ons, selective partial realizations and continuation funds, and targeted AI to boost efficiency and growth. 

• In Middle-Market PE, Talent Risk Is Value-Creation Risk: In private equity’s middle market, C-suite hiring is existential because leadership impact is immediate and support layers are thin—yet frequent CEO turnover and limited in-house HR leave firms at a disadvantage. Many rely on conflicted search assessments or one-size-fits-all psychometrics that miss context, while operating partners lack dedicated human-capital capacity. Compounding the risk, traits PE investors prize, agility and confidence, often backfire when over-indexed, yielding poor follow-through or low coachability that derails integrations and scaling. 

• Weaker U.S. Borrowers Push for Looser Covenants to Boost Debt Capacity: Moody’s reports a growing trend among U.S. companies seeking more flexible covenants in credit agreements to raise additional debt without full lender consent. Between early 2024 and May 2025, 10% of new credit deals incorporated such provisions, allowing debt capacity increases of 40% to 300% of EBITDA, often for dividend recaps, acquisitions, or add-ons. 

• Private Credit Deployment Slump: Private credit’s widely anticipated deployment rebound is proving elusive. Blue Owl’s Q2 new deployments fell ~67% YoY to $1.1B, Blackstone Secured Lending’s were down 51%, and Ares Capital’s originations dropped 33% YoY, reflecting a thinner pipeline as tariffs and slower M&A constrain supply. While dry powder is plentiful and sponsors remain engaged, the near-term environment is pushing lenders toward larger, higher-quality credits and tighter selection.

Equities

• S&P 500 (SPY): 643.47 (+1.77% past week)

• Dow Jones (DIA): 447.11 (+1.69% past week)

Commodities

• Brent Crude: 66.72 (-1.88% past week)

• USO ETF: 73.48 (+.08% past week)

Deep Dive: AI That Moves EBITDA

For middle-market private equity portfolios, AI has emerged as a practical driver of measurable EBITDA improvement but only when deployed strategically against the right use cases with proper foundations. While technology giants capture headlines with generative AI breakthroughs, the real value creation for portfolio companies lies in applying machine learning to operational challenges that directly impact cash flow, margins, and growth velocity.

Revenue Optimization Through Dynamic Pricing and Forecasting

Portfolio companies are achieving 2-4% revenue lifts by deploying AI models that analyze customer behavior, competitive positioning, and market conditions to optimize pricing in real-time. Industrial distributors use machine learning to adjust pricing across thousands of SKUs based on demand signals, competitor moves, and customer price sensitivity. B2B services firms are particularly benefiting from churn prediction models that identify at-risk customers 60-90 days before traditional indicators appear. These early warning systems enable proactive retention efforts that improve gross retention rates by 3-7 percentage points while reducing the cost of acquisition by focusing sales resources on expansion opportunities rather than replacement revenue.

Operational Efficiency Through Intelligent Automation
AI is transforming back-office operations by automating complex, judgment-based processes that previously required human intervention. Collections operations see 15-25% improvements in recovery rates through models that optimize contact timing, channel selection, and negotiation strategies based on debtor profiles and payment history. Service organizations deploy AI to optimize technician routing, predict equipment failures, and automate scheduling.

Data Infrastructure: The Foundation That Determines Success

The difference between successful AI deployments and expensive failures lies in data quality and infrastructure readiness. Middle-market companies often struggle with fragmented systems, inconsistent data definitions, and poor data governance.

Essential Data Requirements

Effective AI applications require clean, consistent, and sufficiently granular historical data spanning at least 18-24 months. Revenue optimization models need transaction-level data including customer identifiers, product codes, pricing history, and external factors like seasonality or competitive actions. Operational AI requires process data captured at appropriate frequencies—daily for inventory optimization, weekly for demand planning, and real-time for fraud detection or equipment monitoring.

Integration and Workflow Considerations
AI models must integrate seamlessly into existing business processes to drive adoption and impact. The most effective deployments embed recommendations directly into the systems operators use daily—CRM platforms, ERP systems, or custom dashboards. This integration approach ensures models influence actual decision-making rather than producing insights that gather dust in separate reporting tools.

Build vs. Buy: Strategic Considerations for Middle Market

Middle-market portfolio companies face unique constraints when implementing AI solutions. Limited technical resources, compressed investment timelines, and the need for rapid ROI favor specific approaches to AI development and deployment.

• When to Buy: Off-the-shelf AI solutions often provide the fastest path to value for common use cases. Revenue management platforms like Vendavo or PROS offer sophisticated pricing optimization capabilities that would require years and millions of dollars to develop internally. The buy approach works best for standardized processes where industry solutions have proven effectiveness. Companies should prioritize vendors offering rapid implementation timelines, clear success metrics, and integration capabilities with existing systems.

• When to Build: Custom AI development makes sense for proprietary processes that create competitive advantages or when existing solutions don't address specific industry requirements. Build approaches require dedicated data science capabilities or trusted external partners who understand both AI development and industry-specific requirements. These projects typically require 6-18 month development cycles but can deliver transformational competitive advantages when executed successfully.

Managing Model Risk and Change Fatigue

AI implementations in middle-market companies face unique organizational challenges that require careful change management and risk oversight. Unlike large enterprises with dedicated AI governance functions, smaller organizations must embed oversight into existing management structures while maintaining operational agility.

• Model Risk Management: Effective AI governance begins with clear model validation processes that ensure predictions remain accurate over time. This includes establishing performance benchmarks, monitoring data drift, and implementing alert systems for model degradation. Companies should designate model owners responsible for ongoing validation and establish clear escalation procedures when performance degrades. Regular model audits should assess both technical performance and business impact. Models that no longer deliver measurable value should be retired promptly to avoid consuming operational resources and creating user confusion.

• Organizational Change Management: The most sophisticated AI models fail without proper user adoption. Successful implementations include comprehensive training programs that help operators understand model outputs and integrate recommendations into daily workflows. Change management efforts should emphasize how AI enhances rather than replaces human judgment, positioning technology as a tool that enables better decision-making rather than automated replacement of expertise. Companies should start with pilot implementations in specific departments or use cases, demonstrating clear value before expanding to additional areas. This approach builds organizational confidence while allowing teams to develop AI fluency gradually rather than overwhelming operations with simultaneous technology changes.

Deal Spotlight: Centerbridge Partners to Take MeridianLink Private in $2.0 Billion Deal

Transaction: Centerbridge Partners will acquire MeridianLink (NYSE: MLNK), a leading provider of digital lending and credit reporting software, in an all-cash deal valuing the company at approximately $2.0 billion. Shareholders will receive $20.00 per share, representing a 26% premium to the closing price of August 8, 2025. The transaction, unanimously approved by the board and backed by holders of 55% of outstanding shares, is expected to close in the second half of 2025 pending shareholder and regulatory approvals. 

Upon completion, MeridianLink will become a private company headquartered in Irvine, California. Larry Katz will assume the role of President & CEO, succeeding Nicolaas Vlok, with plans to accelerate product innovation, enhance AI and data capabilities, and strengthen customer experience. “Together with Centerbridge, we will unlock the potential of this company by accelerating innovation and delivering exceptional customer experiences,” said Katz.

Why it Matters: This acquisition highlights the growing appetite among private equity firms for vertical SaaS leaders in fintech infrastructure; companies with resilient recurring revenues, entrenched customer bases, and expansion potential through AI and product integration. By taking MeridianLink private, Centerbridge can pursue long-term investments in platform capabilities and bolt-on acquisitions without the quarterly earnings pressures of public markets. The deal also reflects a broader trend of PE sponsors targeting mid-cap fintech players as anchor assets for consolidation strategies, leveraging their established market positions to drive growth. 

With nearly 2,000 financial institution clients, MeridianLink offers significant cross-sell and scale advantages, aligning with PE’s focus on scalable, mission-critical technology. In a more selective lending environment, this transaction signals that well-positioned fintech infrastructure providers remain premium targets for private capital deployment.

In today's challenging exit environment—characterized by extended hold periods, selective buyers, and intensified due diligence—the winners are those actively preparing their portfolio companies for exit while simultaneously driving value creation. These strategic priorities will help global private capital teams transform market uncertainty into compelling investment narratives, streamlined processes, and premium outcomes.

• Accelerate value creation through exit-focused operational excellence. Transform exit preparation from a reactive checklist into a proactive value-creation engine. Focus on high-impact, 90-day initiatives that generate measurable EBITDA improvements: optimize pricing and product mix, drive SG&A efficiency, and unlock working capital. These efforts create underwritable cash flows that support premium valuations and reduce buyer risk.

• Build institutional-grade transparency that accelerates decision-making. Establish the financial infrastructure that sophisticated buyers expect from day one. Implement GAAP-compliant reporting segmented by product, customer, and geography. Deploy real-time KPI dashboards tracking growth, margins, retention, and cash conversion. Prepare normalized working capital analyses that eliminate buyer uncertainty and compress diligence timelines.

• Deploy flexible exit strategies that maximize optionality. Complete portfolio company integrations and execute strategic bolt-on acquisitions before initiating exit processes. Consider partial realizations to provide interim liquidity while preserving upside participation. Leverage continuation funds selectively when additional time can meaningfully enhance value realization.

Opening & Closing Remarks from Erik Boender, Vice President & COO, Private Capital Global (a Sparc Group company)

Thank you for reading this week's edition of The Private Capital Compass. The strategies we've highlighted this week, from AI-driven operational improvements to systematic exit readiness, represent the tactical precision required to generate premium outcomes in today's environment.

We hope this edition provided actionable insights into the shifting dynamics reshaping value creation across private markets.