Private markets are scaling quickly, and the use of AI within the sector is growing just as fast. But while many firms are exploring how AI can improve insight, reporting and productivity, the operational pressure behind industry growth is becoming harder to ignore.
Preqin’s 2025 Private Markets in 2030 research says global alternative assets could reach $32 trillion by 2030, while McKinsey’s 2025 asset management report finds operating complexity is keeping cost pressure stubbornly high across the industry.
Growth itself is not the issue. The challenge is supporting more complex structures, higher investor expectations and heavier workloads, without adding friction for the teams doing the work. For fund managers and finance teams, once-manageable operating models are now straining under more complex fund structures and faster reporting demands. AI may help firms respond, but only if it is built on trusted data, strong controls and systems designed to support the way private markets operate. Without that foundation, AI will not remove operational debt. It will expose it.
Manual operating models can’t keep up
Private equity and venture capital have always been built on relationships, expertise and specialist knowledge. For years, the back office has been viewed as a cost center, something to be managed rather than optimized. Many firms still rely on legacy systems and manual processes for everything from fund accounting to investor reporting.
That model is becoming harder to sustain. Funds now span multiple vehicles, currencies, asset classes and reporting obligations. Evergreen funds, private credit, hybrid vehicles and more demanding investors are only adding to the workload and the level of control required.
Teams also need to respond faster to investor requests while maintaining a clear audit trail. In a market where accuracy and accountability are essential, moving data offline to calculate or reconcile figures adds risk and inefficiency.
Today, every private markets firm needs to modernize fund operations. Complexity is not going away, and, as AI advances and firms adopt centralized, automated data environments, spreadsheet-based and manual processes are no longer fit for purpose.
AI needs trusted numbers, not just information
AI is now firmly on the private markets agenda, not just as an investment theme but as an operational question. Firms are funding AI-led businesses, assessing how portfolio companies can use the technology and asking how AI can improve their own reporting, data management and decision-making. The direction of travel was already clear in 2025, when State Street’s 2025 Private Markets Study found that 83% of institutions recognized the value of GenAI-based large language models to generate analyzable data and usable insights from unstructured information.
Many working in private markets understand the potential benefits that AI can bring. However, the sector’s operations still depend on something the technology cannot approximate: trusted numbers. Large language models can help turn unstructured material into usable insight, but core fund operations require accurate, auditable figures grounded in the right operational context.
The distinction is important because the closer AI gets to core fund operations, the more the quality of the underlying data determines its usefulness. For fund managers and finance teams, the real challenge sits closer to the operational core: can they trust the numbers feeding these tools?
If not, AI may increase the speed of analysis without improving decision quality. Worse, it may produce outputs that appear credible but are based on incomplete records, inconsistent calculations or data that has been manually adjusted outside the system of record.
The system of record has to come first
AI can only be trusted when the data beneath it is accurate, complete and traceable back to the system of record. It will not fix poor data and may amplify weaknesses in fragmented systems and manual workflows. Firms with a strong data foundation are far better placed to make AI useful.
For firms trying to scale without weakening oversight, the starting point is the integrity of the data, the strength of the system of record and the discipline of the processes supporting it. A trusted system of record gives firms one reliable view of fund, investor and transaction data, allowing AI to support work inside the operating model.
A modern, event-driven platform creates unified, machine-readable data with auditability built into the workflow. But for AI to support complex fund operations, the system must do more than make data accessible. It must represent the entire operating model in a way AI can understand, including the objects within the system, the relationships between them, the rules being applied and any firm-specific customizations. This allows AI to reason across the model rather than select from a fixed menu of predefined actions.
In complex fund structures, a single transaction can affect allocations, reporting, investor records and accounting outputs across the wider operating model. When those relationships and dependencies are captured in one place, AI can interpret each figure in context, understand how changes flow through the structure and support users without losing the link back to the system of record.
That depth of understanding is critical. If AI can see how a figure was created, which transactions contributed to it, which rules were applied and where it sits within the wider fund structure, it becomes far more useful to fund managers and finance teams. It can help explain why a number has changed, point users to issues that need attention and support faster investigation without weakening control. Without that foundation, AI may simply expose data gaps faster than teams can resolve them.
AI can help teams get ahead
In private markets, the most valuable role for AI is not to replace experienced professionals but to help firms handle growing scale and complexity with speed, consistency and confidence. Used properly, AI can help users understand unfamiliar processes, learn how to use the system more effectively and identify what needs attention before a small issue becomes a broader operational problem.
AI may have the biggest impact in fund operations by improving everyday tasks, rather than focusing only on the most complicated parts of fund processing.
Firms are moving into a period of intelligent assistance, where AI can understand what is inside a platform, and respond to a certain level of tasking and document extraction requests within controlled, auditable workflows. In practice, this can mean systems guide users step by step, explain anomalies as they occur and prompt the next action, such as closing a period or generating a report. The value comes from AI that understands the operational context, the workflow the user is trying to complete, and the rules that govern the data they are working with.
The more connected AI is to the system of record, the more useful it becomes. This delivers efficiency benefits, but the human is still doing the work, whereas the future is one of more autonomous automation.
Adoption improves when AI is built into the workflow
Currently, the greatest barrier to digital transformation is often not functionality. It is adoption. Platforms fall short when they require heavy training, depend on a small group of super-users or push teams back to spreadsheets when pressure builds.
That is where embedded AI can make a practical difference. By guiding users through processes, explaining unfamiliar steps and surfacing the right information at the right time, AI can help teams learn the system as they use it. Adoption improves because the platform becomes easier to understand, easier to trust and easier to use consistently.
That does not remove the need for experienced judgment. Senior professionals still make the decisions, but they spend less time chasing data and more time applying their expertise. AI can help focus attention where judgment is actually required, while keeping the process inside a controlled, auditable workflow.
That has a direct impact on operational performance. Firms can close faster, reduce avoidable errors and handle greater volume without simply adding headcount. They can also retain more knowledge inside the system, which matters in a market where experienced fund accountants are in short supply.
There is also a governance benefit. When data is structured, auditable and connected to the workflow, firms can explain how a figure was created and why a process was completed in a particular way. That level of traceability is essential as investor expectations rise and regulators continue to scrutinize operational resilience.
AI will reward operational discipline
AI will play a significant role in the future of private markets operations, but its value will depend on the groundwork firms put in now. The firms best placed to benefit will be those that strengthen the system of record and embed disciplined processes before moving into more advanced AI use cases.
IQ-EQ’s private markets CFO research found that 18% of CFOs surveyed do not currently have a clear data strategy, while 27% cite the high cost of upgrading systems as the biggest barrier to improving one. Those findings point to a practical reality for the industry. AI ambition will only translate into operational value if firms have the right data infrastructure in place.
Private markets are scaling, and the work behind them is becoming more complex. AI can help operations keep pace but only when it is built on trusted data and embedded into systems designed for the way private markets actually operate. For firms that get that foundation right, AI can become a practical tool for efficiency and control rather than another layer of operational complexity.
