Public-private market convergence is no longer a portfolio theory — it is an operational imperative. The investment case has been settled. The infrastructure required to execute it has not. This is a research-driven examination of what genuine convergence demands and where the execution gap actually exists.
Overall Themes
Convergence as Strategy
Blending public and private assets is no longer a portfolio tilt. It is becoming the dominant architecture of institutional investing.
Data Before AI
AI deployment without a unified data foundation systematizes inconsistency rather than resolving it. Sequencing matters significantly.
Operational Ceiling
The infrastructure gap between public and private markets is not an inconvenience. It is a hard ceiling on how far convergence can scale.
Premium Erosion Risk
The forces democratizing private markets, such as better data, liquidity structures, and broader access, may simultaneously erode the illiquidity premium that justified the move in the first place.
Download the full Celent report — NextGen Portfolio Management Across Public-Private Markets
What Is Public–Private Market Convergence in Asset Management?
| Public-private convergence refers to combining liquid public assets and illiquid private investments into a unified portfolio strategy. This is managed through shared data infrastructure, integrated analytics, and a single operating model to improve diversification, returns, and risk-adjusted outcomes. |
The structural case for this convergence has been building for over a decade, but 2026 marks the inflection point where it moves from investment philosophy to operational imperative. As the Celent research highlights, nearly 90% of active managers have failed to consistently outperform public markets. This reflects structural erosion of public market alpha through indexation and correlation rather than a lack of talent.
Private markets, with their complexity, limited access, and illiquidity premium, represent the logical response. The question is no longer whether to converge. It is whether your operating model can support it.
$26T Projected AUM – Private markets are projected to reach $26 trillion in assets under management by 2029. This reflects both genuine return opportunity and a fundamental repricing of institutional portfolio construction. The technology infrastructure question follows directly.
How Does the Total Portfolio Approach Differ from Traditional Asset Allocation?
| The total portfolio approach focuses on overall portfolio outcomes such as risk, return, and liquidity rather than allocating into fixed asset class buckets. It requires a unified data infrastructure, cross-asset analytics, and an operating model that treats public and private investments as components of a single strategy rather than separate mandates. |
Traditional allocation models were built for a different era where asset classes had clear boundaries, predictable correlations, and were managed by distinct teams on separate systems. That architecture has not aged well.
In a converged portfolio, the distinction between a public credit position and a private credit position is primarily about liquidity profile and reporting frequency rather than analytical approach.
“Most firms are treating the total portfolio model as a strategy problem. It is an infrastructure problem. The firms that figure that out last will feel it most.” – Gurvinder Singh, Founder and CEO, Indus Valley Partners
The Celent research is clear on this point. Firms that adopt the total portfolio approach as an investment philosophy without rebuilding the infrastructure to support it find the model difficult to scale. Real-time portfolio monitoring across blended mandates requires a unified book of record, standardized data models, and cross-asset risk analytics that most legacy systems cannot deliver.
Why Is Technology Integration Difficult Between Public and Private Markets?
| Public markets rely on standardized, automated, real-time systems, while private markets operate on fragmented, manual, and largely unstructured processes. These fundamentally different infrastructure models make integration a complex data and engineering challenge rather than a simple connectivity issue. |
Public Markets Infrastructure
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Private Markets Infrastructure
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This gap makes convergence technically demanding. It is not about connecting systems. It is about translating between fundamentally different data structures. Capital call documents arrive as PDFs. Waterfall calculations rely on spreadsheets. Performance data may be months old before it reaches decision systems.
This creates what is often described as operational drag, but more accurately, it is a structural ceiling on scaling private market exposure.
What Is a Unified IBOR and Why Does It Matter for Multi-Asset Investing?
| A unified Investment Book of Record consolidates positions and transactions across all asset classes into a single, real-time source of portfolio truth. This enables accurate monitoring, reporting, and risk management across blended mandates. |
Without a unified IBOR, firms managing blended portfolios are effectively operating from multiple versions of reality — different systems producing different views of exposure, risk, and performance, often as of different dates. The investment team’s view of the portfolio may not match the risk team’s view, which may not match the reporting team’s output. In a pure public market environment, this is a nuisance. In a converged portfolio where a leveraged buyout and a liquid equity position are competing for the same capital, it is a decision-quality problem.
What Does Retailization of Private Markets Mean and What Does It Break?
| Retailization refers to making private market investments accessible to individual investors through structured products such as interval funds, evergreen vehicles, and model portfolios. It dramatically expands the addressable market for private assets, while simultaneously introducing operational and structural complexity that most managers are not yet equipped to handle. |
BlackRock, JPMorgan, and a growing cohort of global asset managers are building semi-liquid structures to bring private market exposure into defined contribution and retail channels. The commercial logic is straightforward: trillions in DC assets have historically had no meaningful access to private markets. Solving that access problem is a generational revenue opportunity.
But retailization also introduces a paradox that the industry has not fully resolved. The illiquidity premium that makes private assets attractive is a compensation for specific structural features: limited access, capital lock-up, and complexity. As technology and product innovation systematically remove those barriers, the premium they supported adjusts. The more successfully firms democratize private assets, the more those assets may come to behave like the public market environment investors were seeking to diversify away from.
How Is AI Used in Investment Management Operations — and Where Does It Fall Short?
| AI is being deployed for data extraction from unstructured documents, portfolio optimization, risk analytics, and reporting automation. Its impact is highest where the data is clean and standardized. In fragmented multi-asset environments, AI amplifies inconsistency rather than resolving it, making data foundation the prerequisite, not the afterthought. |
The report notes that 44% of firms surveyed are prioritizing AI for portfolio design and operational efficiency. The intention is sound. The sequencing problem is not. Most firms are attempting to apply AI capabilities to infrastructure that has not been designed to support them, including heterogeneous data sources, manual processes, and inconsistent reporting frequencies. The result is an AI that performs well in demonstrations and struggles in production.
Domain-specific AI, which includes tools embedded directly into investment workflows such as portfolio construction, manager due diligence, or waterfall modeling, shows significantly more promise than generic large language model deployments. However, even workflow-specific AI has a prerequisite: clean, standardized, and unified data. The competitive advantage in the next generation of asset management will not belong to firms that announce AI strategies first. It will belong to firms that build the data infrastructure required to make AI genuinely operational.
What Will Define the Next Generation of Asset Management Platforms?
| Next-generation asset management platforms will be defined by unified data architectures that span public and private assets, embedded domain-specific AI, seamless front-to-back integration across the investment lifecycle, and the operational flexibility to support both institutional and retail distribution at scale. |
The Celent research identifies a clear pattern among leading firms: they are investing in foundations before features. Rather than layering new capabilities onto legacy infrastructure, they are rebuilding the operating model from the data layer up by standardizing how private market data is ingested, normalized, and made available to downstream systems. The firms that complete this work now will find themselves structurally advantaged in a market where execution quality is becoming as important as investment strategy. The convergence of public and private markets is not a trend that will stabilize. The operational model that supports it will define who competes and who does not.
Public-private market convergence is no longer an investment thesis — it is an operational requirement. Firms that treat it as a portfolio decision without rebuilding their data infrastructure will encounter a hard ceiling on scale. The technology and data foundations required for genuine convergence are well understood; what separates leaders from laggards is the commitment to build them now.
NextGen Portfolio Management Across Public-Private Markets
The full research paper developed by Celent and commissioned by Indus Valley Partners examines what it takes to build the operational and data infrastructure required for genuine public–private convergence at scale.
- Why traditional integration approaches are failing
- What a unified data model looks like in production
- How leading firms are re-architecting their operating models
- Where domain-specific AI is delivering real value
- How to scale private markets without sacrificing returns
- The liquidity and risk management frameworks that work
