March 1, 2026

From Data to Decisions

As a result, investors can now access vast amounts of information previously locked in disconnected documents and systems. The question is no longer how to obtain data, but how to use it effectively. The real value of artificial intelligence lies not in digitization alone, but in its ability to connect the dots. Financial results and operational signals alongside qualitative context from manager reports and market developments can now be brought together into a single, evolving view.

The shift is changing how investors interact with their portfolios. By moving from static performance snapshots to dynamic and connected data, they can zero in on the decisions driving results, identify risks emerging beneath the surface, and understand exposures across the full portfolio. This is not simply better reporting, but a fundamentally different way of engaging with private markets.

Why Context Matters

Recent developments in private credit highlight why this shift is becoming critical. After years of rapid growth, the asset class is entering a more challenging environment, with rising redemption pressures and increased scrutiny around asset quality and underwriting standards. Questions are being raised about valuation practices, sector classifications, and whether performance metrics fully reflect underlying risk.

While the full picture is still emerging, the implications for investors are clear. Surface-level performance is no longer sufficient. Investors need to look through layers of fund reporting to understand where risk actually resides and how it is evolving. They need the ability to drill down into their holdings, build a historical view of performance, compare valuations and sector designations across assets, and assess qualitative commentary against financial performance.

The next step in this evolution is not simply more data, but better context: the ability to connect these inputs and use them to understand and manage portfolios more effectively.

Early Adoption, Lingering Constraints

Despite rapid technological progress, adoption remains at an early stage. A 2025 Northern Trust study found that while 79 percent of asset owners are increasing technology adoption, 77 percent still cite efficiency and automation as major challenges, and 68 percent rely on relatively small teams to manage increasingly complex datasets.

Many investors are beginning to use AI in a limited capacity, often as a support tool, but skepticism remains. Concerns around data sensitivity, model reliability, and explainability continue to shape adoption. For AI to be used more broadly, two elements are essential: clean, consistent data and strong governance. Models must be tested, explainable, and aligned with security protocols and fiduciary responsibilities. When deployed in this way, AI can reduce operational burden while expanding analytical depth, strengthening human judgment rather than replacing it.

Turning Data into an Advantage

While technology is at the forefront of this change, the objective is not simply to adopt new tools, but to embed data-driven thinking into how teams evaluate assets, monitor performance, and manage risk. Achieving this requires alignment across several dimensions.

First, investors need clarity on how data and technology support their objectives. What is their north star and what information do they need to get you there.  Second, the quality and structure of underlying data are critical. Improving consistency and accessibility, particularly at the asset level, enables more accurate comparisons and more effective oversight. Third, adaptability is essential. Technology and market standards are evolving rapidly, and investors who remain flexible and work with the right partners will be best positioned to incorporate new capabilities as they emerge.

From Analog to Intelligence

Private markets have grown in scale and complexity, but the way information is collected and understood has not kept pace. As fragmentation, inconsistent reporting, and limited visibility become more pressing, the path forward is becoming clearer. Stronger data foundations and the thoughtful application of AI allow investors to move beyond static snapshots toward a more integrated and continuous understanding of their portfolio. The fundamentals of investing have not changed, but the ability to achieve them has. The shift underway is not just from analog to digital, but toward a more intelligent way of operating, where insight is continuous, context is integrated, and decision-making is more informed.

The past few years have brought a sea change in private markets technology, with new companies emerging across the value chain to address long-standing challenges through digitization. Capabilities such as document extraction, workflow automation, and improved reporting have significantly expanded access to structured data. At the same time, advances in artificial intelligence are accelerating this progress, making these processes faster, more flexible, and more cost-effective.