AI’s Moment in Private Markets
In private markets, artificial intelligence has rapidly evolved from concept to critical capability. Fund administrators, general partners, and investors alike are already using AI to automate reconciliations, extract insights from unstructured data, and improve transparency.
But beneath the froth, a simple truth often gets lost: AI is only as reliable as the systems that govern it. In private markets, where billions move through opaque structures and regulatory expectations are rising, trust and precision matter more than novelty. That’s why the conversation is shifting from experimentation to integration: from probabilistic models that generate possibilities to deterministic systems that deliver repeatable results.
Why Data Is the New Alpha
Every firm now calls itself data-driven, but few truly are. Data remains fragmented across accounting, portfolio monitoring, and investor-reporting platforms. Files live in silos. Formats vary by fund, and context disappears in transit. Consider the four V’s of big data—volume, velocity, variety, and veracity. Without addressing these dimensions, firms risk drowning in data rather than using it intelligently.
This is more than an inconvenience: it’s a barrier to AI adoption. Large language models (LLMs) and machine-learning systems can’t learn from inconsistent, unstructured data. Data-preparation and engineering tasks often represent the lion’s share of time consumed in AI and machine-learning projects.
In public markets, standardized disclosures and daily pricing data create a natural foundation for AI. Private markets have no such luxury. That’s why a strong data-governance framework—clear taxonomies, defined ownership, and automated validation—is now the differentiator. Data, not algorithms, is the new alpha.
From Probabilistic to Deterministic
Modern AI models are probabilistic by design. Ask an LLM the same question twice and you will receive subtly different answers. That variability is useful for discovery and reasoning, but when precision is non-negotiable—for example, when booking NAVs or processing capital calls—it’s unacceptable.
Private-market operations demand determinism: identical inputs must yield identical results. To achieve that, leading firms are blending AI’s flexibility with traditional control frameworks, creating what I call operationally deterministic systems.
Formally: Probabilistic AI + Deterministic Business Logic + Structured Workflow = Operational Determinism
This hybrid approach allows firms to harness AI’s reasoning power without compromising the precision investors and regulators expect.
How It Works: Building the End-to-End Framework
Turning AI from an exploratory assistant into a dependable operational partner requires a layered approach—integrating data, workflow, and control mechanisms that together provide operational governance.
1. Contextualization through RAG and MCP
Retrieval-Augmented Generation (RAG) grounds LLM responses in trusted data—fund documents, investor notes, or transaction logs—reducing hallucinations.
The emerging Model Context Protocol (MCP) extends that logic, enabling AI agents to connect securely to enterprise systems and act within guardrails. Think of MCP as a next-gen API that lets AI retrieve or post data—say, verifying a subscription document or initiating a ledger entry—without ever breaching compliance boundaries.
2. Workflow Orchestration
Automation requires accountability. That’s why AI outputs must flow into workflow engines that enforce sequencing, validation, and audit trails. A human-in-the-loop checkpoint remains for material actions, such as finalizing a valuation or releasing investor distributions.
3. Legacy Automation, Reinvented
Robotic Process Automation (RPA) handles the repeatable, deterministic steps—logging into portals, updating GLs, reconciling cash. AI decides what to do; RPA executes how to do it.
Rule-based systems then cross-check AI recommendations against business thresholds and compliance logic.
4. Feedback Loops for Continuous Learning
Each validated outcome then becomes new training data. Over time, probabilistic reasoning is continually aligned with deterministic expectations, improving accuracy and reducing oversight load.
Avoiding the Shiny-Object Trap
As firms race to modernize, it’s easy to mistake more technology for better service. Such an attitude would be misguided: In practice, investors care less about how intelligent the platform is and more about how clearly it delivers the information they need.
The market is saturated with AI-enabled platforms promising end-to-end solutions. But too often, firms choose systems based on vendor flash rather than functional fit. Customizing around legacy workflows can lead to complexity without progress. Instead, firms should prioritize flexibility and modularity—building workflows that can adapt to evolving technologies and investor needs.
A recent CSC study found that nearly half of LPs are dissatisfied with the reporting they receive from GPs. The most common complaints? Clunky portals, inaccessible data, and a lack of forward-looking insights. Today’s LPs expect investor portals to act like dashboards, not dropboxes.
The Data Continuum: From Back Office to Front Office
Data doesn’t stop at the accounting ledger; it informs portfolio monitoring, ESG scoring, and investor communication. A unified data model turns every operational record into analytical fuel.
In practice, that means reconciling the “books and records” world with the “insights and strategy” world. When a performance dashboard and an accounting system share a common data layer, scenario analysis and stress testing become real-time disciplines rather than quarterly exercises.
To implement these tools effectively, firms need automated workflows and structured data pipelines. Manual processes won’t cut it. That’s why the ability to quickly ingest data, adjust variables, and generate predictive insights is a competitive advantage.
Looking Ahead: Insight as Infrastructure
The next competitive edge in private markets won’t come from faster spreadsheets or prettier dashboards. It will come from confidence. Investors will favor managers whose data pipelines are auditable, whose AI systems are explainable, and whose outputs can be trusted as much as their inputs.
AI won’t replace human judgment in private markets so much as refine it. The firms that thrive will be those that build bridges between probability and precision—between creativity and control.
In a field defined by opacity, insight itself becomes infrastructure. And in that future, data does more than just report performance: it enables it.
Author Bio: Dr. Solomon Ndungu is Head of Data Analytics at MUFG Capital Analytics, with over 20 years of experience in financial reporting, data science, and portfolio management. He specializes in leveraging AI and advanced analytics to optimize private market strategies. Solomon holds a Ph.D. in Finance from the University of North Texas and an M.S. in Data Science from Southern Methodist University.
LinkedIn: https://www.linkedin.com/in/solomon-ndungu-ph-d-6199528/
