As private markets expand across institutional and wealth channels, managers need operating models that can keep pace with portfolios, deeper reporting demands and higher investor expectations, write Joep Hamers, Head of Real Assets, and Tim Harvey, Head of Client Service, Private Assets at Citco.
Private markets are growing in scale, reach and complexity.
Private credit now represents a larger share of institutional portfolios, while infrastructure has expanded into areas including renewable energy, data centres and digital infrastructure. Alongside this, secondaries continue to evolve, and private markets products are increasingly being developed for private wealth investors as well as institutional funds.
This creates opportunities, but the next phase of competition in private markets will depend less on capital raising and more on operating models that can support complex portfolios. This is not just a technology challenge; it is an operating model challenge.
More assets mean more variation
Growth does not only create more assets. It creates more operational complexity.
The challenge is particularly evident in real assets. A logistics property, renewable energy project and data centre may sit within the same portfolio, but each can produce different operational and financial information. They may have different ownership structures, financing arrangements, operating partners and regulatory requirements.
That makes real assets difficult to standardise. Scaling a portfolio is not simply a case of applying the same process to more investments. Managers must manage asset-level differences while still producing consistent fund-level reporting.
That becomes harder as portfolios expand across regions. Data may come from property managers, portfolio companies, lenders and joint venture partners. It may arrive in different formats, at different times and with varying levels of detail.
Each new jurisdiction also brings its own filing requirements, accounting treatments and local practices. Without the right processes in place, growth can quickly create more manual work and operational risk, just as investors are asking for more information.
Investors want more detail
Investor expectations have fundamentally changed.
LPs increasingly want to understand individual assets, not just overall fund performance. That can include operating performance, debt levels, cash flows, capital expenditure, valuations and exposure to particular markets or risks.
They are also asking for more non-financial information, including environmental and jurisdictional data, as they compare performance across a wider range of private markets investments.
For managers, the problem is often not whether the information exists, but whether it can be collected, checked and reported consistently.
That data remains unstructured. It may sit in financial statements, invoices, contracts, operating reports or spreadsheets. Managers need to extract it, standardise it and create a reliable source for valuations, fund accounting and investor reporting.
Technology helps, but does not remove complexity
Meeting those expectations will require better use of technology, but technology alone will not solve the problem.
AI’s immediate value is unlikely to come from transformation. It will come from reducing repetitive operational work, improving data capture and helping firms manage more complex reporting requirements.
Automation can reduce manual entry, support document processing and help move information from asset-level sources into reporting systems.
But technology works best when the underlying process is clear.
If data ownership is unclear, definitions vary between teams or information is spread across disconnected systems, adding another tool may simply let an inconsistent process run faster. Managers therefore need to design technology and operations together, with common data standards and clear controls. For example, that same discipline is increasingly important for waterfall scenario modelling, where reliable outputs depend on synchronised accounting data, consistent definitions and controlled calculation workflows.
Data security must also be part of the discussion. Investors are cautious about how confidential fund information is used by AI tools. The next phase of adoption will depend as much on governance and security as on capability. Human judgement remains important too: technology can identify a discrepancy or extract a figure, but it cannot always explain whether that figure is reasonable.
Connecting the asset, fund and investor levels
Even with better tools, the model will struggle if asset-level data, fund accounting and investor reporting remain disconnected.
Asset-level information may sit with one team or provider, fund accounting with another and investor reporting elsewhere.
This can lead managers to rely on multiple systems, data sources and administrators across different strategies. While that may address a need, it can also create duplicated processes, inconsistent definitions and a different experience for investors across funds.
A more scalable model connects the process from the underlying asset through to the end investor. That does not necessarily require consolidation into a single provider. It does require greater integration across systems, data standards and operational responsibilities.
This is especially important where managers operate across private equity, private credit and real assets. A common data layer, agreed definitions, validation checks and controlled delivery methods can reduce that fragmentation and support more reliable analytics.
Specialist asset-class expertise also remains essential. Infrastructure, real estate, private equity and private credit each have distinct operating characteristics. A common technology platform can create consistency, but the teams using it still need to understand the assets and reporting requirements involved.
Private credit shows the same pattern
Private credit shows why the operating model question extends beyond traditional fund accounting. Loan notices, cash movements, accruals, lifecycle events and yield calculations generate large volumes of operational data. Automation can digitise notices, reduce manual rekeying, improve lineage and move information more efficiently into accounting and reporting.
For managers expanding in private credit, the priority is to scale from document ingestion through to accounting, reconciliations, investor reporting and oversight without adding proportionate manual workload or control risk.
Operations are now part of the investor proposition
A manager’s ability to deliver accurate information, respond to investor questions and complete reporting on time now shapes market perception. Strong operational infrastructure can support product launches and expansion into new markets, while weak processes can slow growth and erode confidence.
The assets themselves will remain complex – however, the goal is not to eliminate that complexity, but to create operating models that manage it consistently.
As private markets enter their next phase, operational excellence will no longer be a back-office function; it will be a competitive advantage.
