"Private markets have a data problem. And AI alone won't solve it." — Insights from LPEA Insights Luxembourg 2026

As private markets expand into the wealth management channel, the industry's ability to deliver reliable, comparable and accessible data is becoming a commercial necessity. Better data is essential to investor trust, fund distribution and the effective adoption of artificial intelligence (AI).

Private markets have traditionally operated with a level of complexity that sets them apart from listed investments. Bespoke fund structures, varying reporting practices and limited transparency have long been part of the landscape. But as the industry seeks to attract a broader investor base, particularly through wealth managers and private banks, these characteristics are increasingly becoming obstacles to growth.

Improving data quality is no longer simply an operational or regulatory exercise. It is becoming fundamental to how private-market funds attract investors, distribute products and scale their businesses. 

Transparency is a prerequisite for investor trust

Unlike institutional investors, who may have dedicated teams and established processes for analysing private-market investments, wealth managers and distributors often need to assess these products alongside traditional asset classes. This requires information that can be understood, compared and incorporated into a broader portfolio view. 

She illustrates the challenge with a common investor concern: how can an adviser assess whether a portfolio is genuinely diversified if the underlying private-market exposures are insufficiently transparent? Private-market holdings can sometimes appear as a single allocation, without sufficient information about their underlying composition.

According to Longrus, this lack of visibility can have direct commercial consequences. "If you don't give me the data, I'm not going to look at the fund. If you don't give me the information, I'm not going to invest," she says, recalling feedback from investors, family offices and banks. “Sometimes it even matters how you give me the data, as it determines how the information is handled, or not handled well, in the investor workflow”, she adds. Better information can therefore determine whether a fund is considered for investment in the first place. 

Make information machine-readable

One challenge is finding the right balance between making private-market information more comparable and preserving the characteristics that make individual investment products different. Longrus cautions against simply applying the standardisation models developed for public markets to private-market investments.

"Private markets are inherently different because the complexity is there by design," she says. Fund structures, investment strategies, liquidity arrangements and investor requirements can vary considerably, often for legitimate commercial and regulatory reasons.

Instead, Longrus advocates an approach that uses technology to organise and interpret complexity, rather than remove it. The objective should be to make information machine-readable, comparable and accessible while retaining the nuances of different investment structures and investor needs.

This becomes particularly important in distribution, as private banks, family offices and other intermediaries have different due diligence processes, operating models and regulatory obligations. Understanding those differences is essential if private-market products are to reach a wider audience effectively.

Collect data once, create value many times

One opportunity is to derive greater value from the effort already invested in collecting and organising fund data. Private-market managers face growing demands for regulatory reporting, investor disclosures, due diligence and distribution-related information. Too often, they address these through separate processes, creating duplication and added operational costs.

For fund managers, the benefits extend beyond operational efficiency. Reliable, reusable data makes it easier to respond to investor requests, enter new distribution channels, and adapt to different regulatory requirements. Better data can also support business intelligence, improve decision-making and help firms respond to increasing cost pressures.

AI can process complexity, but it cannot guarantee accuracy

AI and its potential to transform how private-market information is collected, analysed and consumed is another major consideration. Longrus identifies analysing large volumes of fragmented, inconsistent information as one of AI's most credible applications. The opportunity is significant in an industry where documents, PDFs, manual processes and disconnected technology systems remain widespread.

There is also a less obvious challenge: digitisation does not necessarily make life easier for the investor. A fund manager may introduce an efficient digital investor portal, but an LP dealing with dozens of managers could still find itself navigating dozens of different platforms and data formats. AI systems may try to match information that isn't directly comparable, interpret inconsistently structured data, or overlook important context. As a result, the information presented to investors may not accurately reflect the fund manager's intended message or may lack the context from the original investor communication. 

Human review remains important, but Longrus suggests fund managers also rethink how they make information available in the first place. Rather than relying primarily on polished documents designed for human consumption, firms need to consider how automated systems will interpret their information.

This means prioritising structured, reliable and appropriately accessible data, while maintaining the confidentiality requirements that are particularly important in private markets. As AI becomes more embedded in due diligence and investment analysis, the quality of the underlying information will increasingly influence how accurately investment products are represented and assessed.

From data management to data strategy

According to Longrus, the priority over the next 12 months should be strategy. That means maximising the value of the time and effort invested in getting data right, while considering how that information reaches the wider market and is ultimately consumed by investors and intermediaries. As private markets become more accessible to a wider investor audience, the ability to structure, distribute, and use information effectively will increasingly influence investor confidence, operational scalability, and commercial success.

AI may accelerate this transformation, but it cannot replace the need for a sound data foundation. The firms best positioned to benefit will be those that approach data not simply as information to manage, but as a strategic asset to develop.