For most of the past decade, alternative asset managers operated within a comfortable bandwidth. A hedge fund could deploy capital quickly with lean technology stacks. A private credit manager could track loans through a combination of specialized systems and spreadsheets. An infrastructure investor could manage a portfolio of 15–20 operational assets with manual monitoring and annual reviews.
But that model breaks at scale.
The industry has reached an inflection point. Hedge fund adoption of artificial intelligence jumped from 18% in 2024 to 46% in 2025, with an additional 30% actively exploring deployment. Private credit markets have ballooned to more than $2 trillion in assets under management, intensifying pressure on fund managers to process complex, bespoke transactions across systems that were never designed to communicate. Institutional investors now expect real-time portfolio transparency, standardized performance metrics, and auditable data lineages—not quarterly reports with explanatory footnotes.
At the same time, the economics of manual processes have become untenable. When a credit manager tracks syndicated loan agreements across three administrators, reconciles cash flows daily, and prepares investor reports by extracting data from five separate platforms, the operational friction is not a nuance—it directly reduces returns. Every basis point consumed by reconciliation breaks or reporting delays is a basis point subtracted from LP distributions.
The most sophisticated managers recognize this new reality. As a result, they are not tinkering with tools or upgrading systems. They are conducting a systematic reassessment of how data flows through operations, how technology enables decision-making, and how teams can scale without collapsing under operational load. This shift is clearly visible to asset managers who have moved ahead, and the lag for others will become a competitive liability.
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The Real Cost of Data Fragmentation
The severity of this challenge is quantifiable. Across private credit and hedge fund operations, 48% of finance leaders cite data fragmentation and disconnected systems as the single greatest operational obstacle—ahead of talent acquisition and strategic alignment. The cost impact is substantial: when accounting systems, portfolio monitoring tools, LP reporting platforms, and risk systems operate in isolation, the average firm absorbs hundreds of thousands of dollars annually in remediation costs per portfolio company, largely through manual reconciliation and delayed reporting.
For a credit manager operating 50 loans across three syndication structures, for example, this situation is a tangible problem. Each loan agreement contains unique terms: varying interest rate formulas, covenant baskets, payment frequency, and subordination structures. The agent bank provides cash flow reports in one format. The fund’s accounting system records activity in a different format. The risk team monitors exposures in a third format. The portfolio company’s financial statements exist in a fourth. Reconciling these views daily—which is essential for tracking available liquidity, enforcing covenants, and reporting to investors—becomes a manual forensics exercise rather than a routine operation.
Despite analysts beginning work at 5 AM, funds regularly fail to close by the 8 AM reconciliation deadline, unable to complete validation checks before the market opens. The issue is not incompetence but architecture: manual processes create exponential error rates at scale.
For infrastructure funds, this challenge manifests differently but with equivalent friction. A portfolio spanning toll roads, power generation assets, and digital infrastructure across multiple geographies collects operational metrics—traffic volumes, capacity utilization, energy production—from various operators using different reporting standards. The fund office needs a consolidated view to assess whether return assumptions are tracking, to forecast cash distributions to LPs, and to identify portfolio optimization opportunities. Without integrated data infrastructure, this analysis requires weeks of manual mapping and validation.
The consequences extend beyond operational friction. Overreliance on spreadsheets has triggered some of the largest financial errors in institutional asset management. Goldman Sachs’ 2014 valuation of Tibco Software used a spreadsheet that double-counted shares—a calculation error discovered through SEC-level scrutiny that ultimately settled after tens of millions of dollars in litigation costs. Again, the error was not caused by poor methodology. It resulted from the error-prone nature of manual spreadsheets at institutional scale.
Three Structural Shifts Making the Crisis Acute
First, data complexity has exploded without corresponding operational scaling. Private credit has been one of the fastest-growing segments of private markets. Each new strategy—opportunistic credit, GP-led secondaries, continuation vehicles, preferred equity structures—creates new data challenges. Hedge funds have similarly broadened. Single-strategy quant funds now operate multi-asset systematic models while long/short equity managers run volatility and credit overlays alongside equities. The data flows from these strategies are fundamentally different from what legacy systems were designed to handle.
Second, investor expectations have made transparency mandatory. The updated 2025 ILPA reporting guidelines, while technically voluntary, have become the de facto standard for institutional investors in private markets. LPs now systematically evaluate GPs on data governance maturity during due diligence, and managers demonstrating robust data infrastructure, standardized reporting, and traceable data lineage are favored in manager selection and re-ups. The 2025 Waystone asset management outlook notes that co-sourcing models—which embed data-fluent operators within fund managers’ technology environments—are gaining traction precisely because they strengthen operational oversight and data integrity.
Third, AI deployment creates urgency for data infrastructure investment. AI use across hedge funds and credit managers has moved beyond experimentation. Industry surveys show that adoption is accelerating rapidly, with only a minority of firms reporting no AI projects. However, the industry is rapidly discovering a critical constraint: AI amplifies the effects of data quality, both good and bad. Firms that deploy sophisticated machine learning models on fragmented, inconsistent, or poorly governed data do not get intelligent decisions—they get bad decisions made automatically, at scale. Firms seeing meaningful ROI from AI are those that have already invested in data architecture, governance, and system integration.
Three Dimensions of the Modern Operating Architecture
Forward-leaning managers across hedge funds, credit, and infrastructure are addressing this issue with a coordinated approach spanning three dimensions: data governance and architecture, technology integration, and organizational structure.
1. Data Governance as the Foundation
Leading alternative asset managers now treat data governance not as a compliance obligation but as the operating foundation that enables scale, transparency, and intelligence. Building this foundation begins with establishing clear data ownership and control mechanisms across the entire investment lifecycle.
For private credit managers, this is particularly critical. Unlike liquid markets where standardized identifiers and APIs streamline portfolio monitoring, private credit depends on bespoke loan documents, irregular cash flows, and covenant structures that vary by deal. Without firm-wide data standards, each credit analyst may define “available liquidity” differently, use different covenant calculation methodologies, or categorize covenant waivers using different schemas. When these definitions vary, the portfolio office can’t reliably answer fundamental questions about how much committed capital is deployed, what the aggregate exposure to specific industries is, or which loans are closest to covenant breach.
Effective governance frameworks typically address five components:
- Clear responsibility for data accuracy at each stage—deal capture, valuation, covenant monitoring, cash processing—is assigned by ownership. Managers that formalize ownership and require workflow approvals before booking investments significantly reduce downstream reconciliation issues.
- Stewardship defines who implements policies, maintains data quality, and enforces standards across portfolio management, operations, and risk teams.
- Quality standards set explicit thresholds for data completeness, accuracy, and timeliness. For example, requiring covenant calculations within two business days of period close and dual sign-off from operations and investment teams.
- Lineage and auditability ensure systems document where data originated, when it was modified, and who approved changes, all of which are critical for regulators and LPs.
- Access controls specify who can access what data and at what level of detail, protecting sensitive information while enabling firm-wide analytics.
Firms that implement this type of governance eliminate weeks of month-end reconciliation work, improve audit readiness, and free up teams to spend more time on forward-looking analysis instead of data repair.
2. Technology Integration: Breaking Free From System Silos
The second dimension involves designing technology environments that connect deal sourcing, portfolio monitoring, fund accounting, and reporting within an integrated operating system. Disconnected systems create friction at every stage of the investment cycle.
The challenge is acute for funds managing complex structures. A multi-strategy hedge fund may need to track long-only equities, credit opportunities, and volatility strategies, each of which involves different pricing sources and risk models. In a fragmented environment, different systems handle each strategy separately, manual data mappings connect them, and reporting cycles depend on stitching together multiple extracts in spreadsheets.
Integrated platforms address this in several ways:
- A unified data model provides a single, normalized representation of positions and exposures, supporting various asset types while preserving comparability and consistent definitions.
- Straight-through processing ensures that when a borrower makes a payment or a trade settles, the cash, position, accounting entries, and investor reports all reflect the change without manual rekeying.
- Real-time exception visibility surfaces reconciliation breaks and unusual data patterns immediately instead of at month-end, allowing faster resolution and better risk control.
- Automated reporting generates investor reports, regulatory filings, and management dashboards directly from the integrated data, shrinking reporting cycles from multiple weeks to just a few business days for many managers.
For multi-billion-dollar platforms managing hundreds of instruments or loans, these integrations do not simply reduce cost. They change what the front office can do, enabling richer portfolio analytics and more frequent scenario analysis.
3. Organizational Design: In-House vs. Co-Sourcing vs. Outsourcing
The third dimension of a modern operating architecture addresses how teams and service providers are structured to deliver operational capabilities at scale. Historically, managers chose between building large in-house operations or fully outsourcing to administrators, often at the cost of data visibility and agility.
A hybrid co-sourcing model is gaining traction. Co-sourcing embeds an experienced operations partner directly within the manager’s technology environment, combining the scale and specialization of an outsourced provider with the control of an internal team. Waystone’s 2025 outlook explicitly highlights co-sourcing as a model that will become more popular because it allows managers to maintain control and real-time data access while leveraging an administrator’s specialized resources.
Case studies in private credit, for example, describe managers that avoided large fixed-cost hiring plans by pairing an in-house core team with a co-sourcing partner responsible for fund accounting, waterfall calculations, and intra-month reporting, all within the same core platform. These firms reported more stable capital workflows, shorter reporting timelines, and the ability to launch new funds without linear growth in headcount.
For hedge funds and infrastructure investors, the logic is similar. Co-sourcing provides continuous KPI monitoring, covenant tracking, and cash forecasting without forcing the manager to overbuild the back office. The common thread is governance. The manager defines standards and retains oversight, while the co-sourcing partner delivers execution capacity at scale.
The AI Question: Why Sequencing Matters More Than Adoption Speed
Across alternative asset management, enthusiasm about AI’s potential is high. Market analyses show that a growing majority of hedge funds are either using AI in production or running pilot projects, with only a minority not considering AI at all. Surveys indicate productivity gains of 20 to 30% in certain operational and research workflows when AI is deployed systematically.
However, the industry is discovering a critical sequencing problem: AI amplifies data quality, both good or bad. A hedge fund deploying a machine learning model for trade signal generation on fragmented, inconsistent historical data will not get better signals—it will get bad signals automatically at scale. An infrastructure fund using AI for anomaly detection on asset performance metrics collected through ad hoc, manual processes will generate false positives and alert fatigue.
Firms seeing genuine ROI from AI have first invested in data architecture, governance, and system integration. In private credit, for example, anomaly detection on cash flows and covenants only becomes useful once loan terms, payment schedules, and covenant definitions have been standardized across the book. Once that foundation is in place, AI can shorten exception resolution times, flag emerging risks earlier, and extend the capacity of lean teams.
Strategic forecasts suggest that by the end of this decade, a meaningful share of alternative managers will use AI for continuous portfolio valuation updates and risk monitoring—but that adoption will be gated by the underlying data infrastructure.
LP Expectations and the Transparency Shift
For decades, LP–GP communication around performance and fees has been fragmented. Different managers used different fee categorizations, disclosed expenses inconsistently, and calculated performance metrics through different methodologies. LPs found it difficult to compare funds on a like-for-like basis or to verify fee and expense allocations.
The January 2025 ILPA reporting guidelines update marks a step-change in expectations. While not a regulation, the guidance has quickly become a reference standard for institutional LPs assessing manager reporting practices. Industry commentary and service-provider guidance stress that adopting the ILPA templates is increasingly viewed as best practice in due diligence and fundraising processes.
The update introduced two material changes:
- A revised fee template that separates internal chargebacks and related-party payments from third-party expenses, giving LPs clearer visibility into true operating costs vs. GP economics.
- A performance template that standardizes how IRR, TVPI, and DPI are reported and requires detailed cash flow mapping, including performance with and without the use of subscription facilities.
Implementation is recommended from 2026 onward, but many managers are already working with administrators and technology vendors to align systems and workflows with the new templates. Firms with integrated fund accounting and reporting stacks can usually adapt more quickly, while those relying on spreadsheets and fragmented systems face longer remediation cycles.
Implementation Reality: Building the Right Way
Managers that are successfully modernizing operating models tend to follow a disciplined implementation sequence aligned with consulting and vendor best-practice guides.
They start by mapping current-state architecture and pain points, quantifying the cost of manual work and recurrent breaks. They then define data governance—ownership, stewardship, quality thresholds, access—and only after this step move into selecting and implementing technology.
This approach prioritizes integration over breadth. High-friction areas such as reconciliation, fund accounting, and investor reporting are addressed first, before expanding into portfolio analytics and AI use cases. Implementation plans factor in the reality that complex integrations typically take longer and require more change management than initial vendor quotes suggest.
Finally, successful programs invest heavily in stakeholder alignment and training, recognizing that changing how people work is as important as changing the tools they use.
The Future of Scale: IVP’s View on Operating Excellence in Private Markets
From IVP’s perspective, scalable growth in private markets will be defined less by asset selection alone and more by the strength of the operating foundation. The managers best positioned for the next phase of growth are those that:
- Treat data as a strategic asset, with clear ownership, governance, and auditability across the investment lifecycle
- Standardize and integrate core systems to eliminate silos across front, middle, and back offices
- Automate high-friction workflows such as reconciliation, portfolio monitoring, and investor reporting to scale without increasing headcount
- Maintain control across outsourced functions through shadow accounting, oversight frameworks, and real-time visibility
- Sequence AI responsibly, layering advanced analytics and automation only after establishing reliable data architecture
- Design operating models for growth, enabling new strategies, vehicles, and geographies without rebuilding infrastructure
IVP enables this transformation by providing a unified platform and managed services that help private market managers move from reactive operations to proactive, insight-driven decision-making. As scale, complexity, and investor expectations continue to rise, operating excellence will increasingly separate market leaders from the rest.
