Welcome to this week’s edition of The Private Capital Compass, a curated weekly analysis from Private Capital Global designed to cut through the noise and surface what matters most and what it means for investors and operators.

Operating partners are being asked pointed questions about AI deployment at quarterly board meetings, yet most firms remain stuck in debates over which chatbot to use. This week, we examine where AI is actually delivering measurable operational alpha and outline the questions boards should ask to separate activity from impact. We also explore how continuation vehicles are transitioning from niche structures to mainstream liquidity tools, and why operational value creation is finally catching up to the rhetoric.

For those wanting to go deeper into AI cycle dynamics, we're recommending this week's Dry Powder podcast from Bain, which challenges prevailing narratives about AI deployment timelines.

Compass Points

Key insights at a glance:

• Middle Market PE Moves From Financial Engineering to Operational Partnership: Private equity in the lower middle market is shifting from leverage-focused financial engineering to hands-on operational partnership, according to a recent CLA Insights article. PE sponsors are modernizing systems, strengthening leadership teams, and improving supply chains to drive sustainable EBITDA and revenue growth. Founder-led businesses are responding positively to this collaborative approach, valuing partners who can help institutionalize operations while preserving culture. Middle-market companies with PE ownership are reporting higher year-over-year revenue, employment, and EBITDA growth, reflecting the combined impact of capital, governance, and active operational support.

  PCG Take: While the "operational value creation" narrative has been the PE talking point for years, the data is finally catching up to the rhetoric. The real question for sponsors: are your operating partners equipped to deliver this hands-on value, or are you still staffed for a financial engineering model? The firms winning middle-market deals in competitive processes are the ones who can credibly demonstrate functional expertise on Day 1, not just access to capital.

• AI, Carve-Outs, and Untapped Opportunities Shape the Playbook:As private equity enters 2026, technology deployment, sector innovation, and evolving exit strategies are redefining value creation, according to FTI Consulting. IPOs may reopen for marquee names, offering critical signals for exit timing and market positioning. Nontraditional sectors, including legal services, are attracting PE interest as firms explore creative investment structures to capture growth while navigating regulatory constraints. Carve-outs are shifting from financial engineering to operational transformation, with Day-1 readiness and standalone operating models becoming essential to realize value.

  PCG Take: The carve-out observation is the most actionable here. Buyers expecting clean separations and turnkey operations on Day 1 are consistently disappointed; most carve-outs require 12-18 months of intensive operational work to achieve true standalone capability. If your underwriting assumes immediate value capture from carved assets, you're likely overestimating Year one EBITDA.

• Continuation Funds Go Mainstream: CFO.com argues that continuation vehicles have moved from niche to mainstream as PE firms face longer holds and fewer exit options. Sponsors are using these structures to unlock liquidity while retaining their best assets, but the approach is fueling concerns about conflicts of interest, pricing, and governance as managers sit on both sides of the transaction. A pending Delaware case could establish important precedent for how these deals are scrutinized.

  PCG Take: The "mainstream" framing overstates reality. Continuation vehicles still represent only ~15% of PE distributions. The more accurate read: traditional exit markets remain broken, forcing sponsors to manufacture liquidity through increasingly complex structures that require sophisticated governance to maintain LP trust. For operating partners, CVs signal extended hold periods and heightened board scrutiny of value-creation milestones.

• AI Changes the Questions Boards Need to Ask: A Forbes article featuring Verdane and Northzone highlights how leading firms are making AI a strategic priority at the board level. Traditional signals such as early ARR or initial retention are proving less predictive in an environment where AI accelerates experimentation but compresses competitive advantage. The more consequential questions now sit with boards: which processes must be redesigned for human–AI collaboration, where proprietary data or distribution truly compounds, and whether today's product can evolve into a platform in an AI-native future.

  PCG Take: This gets to the heart of the valuation problem for AI-enabled businesses. Boards need to stop asking "Are we using AI?" and start asking "Where does our proprietary data or distribution create a durable advantage that AI amplifies rather than erodes?" Most AI implementations are replicable—the moat comes from what AI operates on, not the AI itself.

• Capital Concentration Becomes the Signal in AI Venture: A new Inc. analysis highlights eight AI unicorns that collectively raised unprecedented sums in 2025, underscoring how venture capital is consolidating around a small number of perceived category leaders. More than half of all U.S. VC dollars flowed into AI last year, with mega-rounds defining the market: OpenAI alone raised $40 billion, while Anthropic, Anysphere, and others vaulted to valuations once reserved for public-market incumbents. The list spans frontier model developers, AI-native application layers, and infrastructure players, but the common thread is scale—capital intensity, rapid revenue expansion in select cases, and aggressive bets on owning foundational layers of the AI stack before competitive dynamics fully settle.

  PCG Take: This is less a story about “AI winners” than about capital acting as a strategic weapon. At these funding levels, balance sheets are shaping market structure—who can train frontier models, secure scarce compute, lock in distribution, or subsidize adoption long enough to become the default. For private capital and boards, the takeaway is cautionary: massive funding rounds do not equate to durable value creation. The real diligence question is whether scale capital is compounding an underlying moat (data gravity, workflow lock-in, ecosystem control) or merely financing speed in markets where advantages decay quickly. In AI, capital can buy time—but only structural advantages buy permanence.

Deal Spotlight: EQT Acquires Coller Capital

Transaction: EQT, a global private equity firm, has signed definitive agreements to acquire Coller Capital, one of the world's leading private equity secondaries specialists, in a transaction expected to close in Q3 2026. Under the terms of the agreement, Coller Capital will become part of EQT's global platform while maintaining the independence of its origination and investment processes. Jeremy Coller, chief investment officer and managing partner, will continue to lead the business as head of the newly formed Coller EQT and will join EQT's executive committee, reporting directly to Per Franzen, EQT's CEO and managing partner.

Following the close, Coller Capital will form Coller EQT, establishing a new secondaries business segment alongside EQT's existing private capital and private real assets segments. The combined platform aims to strengthen relationships with institutional, private wealth, and insurance-related clients through an enhanced product offering while creating deeper global capabilities across private equity, infrastructure, real estate, and secondaries. Franzen stated the firm's ambition to double the size of Coller's business in less than four years post-acquisition.

Why It Matters: EQT's acquisition of Coller Capital represents a strategic bet that secondaries will transition from a specialized liquidity solution to a core portfolio construction tool for institutional investors. The timing is significant: as private equity holding periods extend and distribution activity remains suppressed relative to historical norms, limited partners face growing pressure to rebalance portfolios and manage liquidity without waiting for traditional exit events.

The secondaries market has experienced explosive growth over the past decade, evolving from a $15 billion annual market in 2010 to over $130 billion in transaction volume in 2024. What began as a niche solution for distressed sellers has matured into a sophisticated marketplace where LPs use secondaries strategically for portfolio optimization, exposure management, and return acceleration. GP-led transactions, which allow sponsors to extend hold periods for high-performing assets while providing liquidity to existing investors, now represent nearly half of all secondaries volume.

Deep Dive: From AI Experimentation to Operational Alpha

Operating partners now face pointed questions in quarterly reviews about AI deployment strategies, yet many firms remain trapped in surface-level discussions about which general-purpose assistant to adopt. This misses the larger opportunity: translating AI access into measurable operational improvements that appear in EBITDA, working capital efficiency, and productivity metrics.

Garrett Fitzgerald, CIO at Invision Capital, observes that while boards broadly expect management teams to leverage AI for efficiency gains, these expectations often lack specificity. Operating partners bridge this gap by grounding conversations in concrete examples demonstrating cost reduction, time savings, and revenue impact. Over the next 12-18 months, these expectations will sharpen as boards become more educated on where AI creates genuine value.

Starting with Value, Not Technology

The most productive AI implementations share a common starting point: they begin with business problems, not technological capabilities. Leading operating partners ask which operational bottlenecks, cost drivers, or growth constraints matter most, then evaluate whether AI can change outcomes. High-return use cases cluster around revenue enablement through improved pricing and forecasting, cost reduction through automation, and working capital optimization through better demand planning and procurement analytics.

Michael Marchand, Operations and Strategy Expert, frames the transformation clearly: "AI is taking tribal knowledge and making it systematized, observable, and governable at scale. Most port- cos don't fail for lack of strategy—they fail because the 'middle layer' struggles to translate intent into reliable field execution." The smart approach focuses on banking low-hanging wins first to build confidence and momentum rather than pursuing the most technically sophisticated solution.

Where Value Actually Appears

In middle-market portfolios, AI impact follows distinct patterns. Operators report that enabling technologies help teams perform analysis and correspondence faster, though measuring direct P&L impact remains challenging. Sales effectiveness improvements through lead analysis and automated follow-ups show promise, but success depends critically on data quality, AI tools amplify existing CRM hygiene problems before solving them. The immediate opportunity lies in using AI to clean and normalize data first, then deploying automated workflows.

Workflow automation tools have made it surprisingly feasible to bridge process gaps quickly. Companies can now build internal tools to extract data from PDFs, categorize service issues, or clear IT backlogs that have languished for years. The key differentiator is execution agility: firms that move past analysis paralysis to pick a technology lane and implement rapidly will pull ahead.

The Build vs. Buy Decision

Buying makes sense when use cases are common, data structures standardized, and speed matters more than differentiation. Best-in-class solutions already exist for pricing optimization, marketing attribution, and FP&A analytics. What makes buying increasingly attractive is deployment speed, tools requiring six-month implementations two years ago now launch in weeks.

Building becomes compelling when problems are proprietary, data is unique, or competitive advantage depends on differentiated insight. New development environments and AI-assisted coding have dramatically reduced build costs and timelines. Richard Denton, CEO of Askraa, describes the transformation: "Vibe coding tools allow creation of prototypes at lightning speed at a tenth of the cost and time. A business user can design solutions exactly as they envision them, utilizing specific domain expertise." This eliminates the traditional multi-person development process with its inherent miscommunication and delays.

Strategic Finance as High-Leverage Application

Finance operations represent one of the clearest AI value creation opportunities. Asif Rahman, Head of PE Partnerships at Brex, identifies the execution gap as the primary target: embedding AI agents directly into the spend layer allows CFO teams to shift from receipt chasing to strategic oversight, closing books in days rather than weeks. This speed transforms performance dashboarding from reviewing 30-day-old lagging indicators to accessing real-time EBITDA visibility and cash flow data. Beyond faster closes, AI enables finance teams to shift from periodic reporting to continuous intelligence. Anomaly detection identifies margin compression before it appears in monthly reviews. Predictive analytics surface customer churn risk and trapped working capital in real time, providing management more runway to respond effectively.

Security Through Adoption, Not Avoidance

Doug Rinehart, CISO at Marigold, challenges the common response of restricting AI until governance frameworks are complete. "Organizations should assume AI is already in the enterprise, sanctioned or not," he explains. Outright prohibition doesn't stop usage—it creates shadow AI with no logging, policy enforcement, or audit capability. The real risk is not having visibility into which AI tools are accessing company data.

Successful implementations treat AI as a new risk category requiring updated governance frameworks. Organizations set clear acceptable use policies, implement technical measures to monitor for misuse, and include human-in-the-loop processes that limit tool autonomy initially while building appropriate controls. This enables value creation without introducing unintended exposure. The path forward requires accepting that early pilots will be discarded as part of learning. Teams succeed when they invest time experimenting to develop new mental models for AI-enabled work, treating early efforts as capability building rather than demanding immediate production-ready results.

The diagnostic framework matters because activity metrics deceive. High adoption rates, numerous pilot programs, and budget allocated to AI platforms tell you nothing about whether EBITDA is improving, working capital is becoming more efficient, or productivity is actually increasing. Michael Marchand suggests four diagnostic tests to cut through the noise and reveal whether AI is creating leverage or simply redistributing effort.

If you cannot identify which specific metrics are moving, the use case was likely poorly conceived from the start. If decision cycle times remain unchanged despite AI deployment, you are not capturing the acceleration advantage that justifies implementation. If managers report that AI tools add procedural steps rather than eliminate friction, you have created overhead, not efficiency. And if front-line teams cannot articulate how AI helps them succeed in their roles, adoption will never scale beyond early enthusiasts.

How and where are you measuring AI impact today? If you can't answer with specific EBITDA, cycle time, or working capital metrics in the next 30 seconds, you're measuring activity, not outcomes and your board will figure that out soon.

Closing Remarks

Thank you for reading this week’s edition of The Private Capital Compass. Across 2026, PCG will convene investors, operators, and advisors through a growing slate of curated events in Austin, Boston, Chicago, London, New York, and San Francisco, each designed to move beyond surface-level discussion and toward practical insight, peer exchange, and real-world application.

Our mission remains consistent: to deliver clear-eyed analysis, relevant intelligence, and informed perspective that helps private capital professionals translate market signals into durable value across deals, portfolios, and platforms.

We appreciate your continued engagement and look forward to navigating the year ahead together.

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