Could US megafunds be a victim of their own success?

As PE managers grow their operations – be that through launching new funds, moving into new markets or acquiring other firms – it’s commonplace for certain issues to creep in. Managing more portfolio companies, providing investors with increased levels of data, dealing with incompatible technology systems and struggling with old systems and processes that aren’t necessarily fit for purpose will sound all too familiar to those in the industry. 

The reality is that, even in this day and age, with advanced technology such as AI breaking into the industry, GPs, administrators and other parties are using multiple (often legacy) tools. This has led to data fragmentation across systems, spreadsheets (including the ubiquitous Excel) and portfolios. 

Indeed, a survey of PE CFOs from early 2026 reveals that 89% cited data fragmentation and manual aggregation across disparate systems and portfolio companies as their primary reporting challenge, while 54% described manual reconciliation as a capacity ceiling. This only compounds across systems that can’t handle multi-country, real-time multi-asset reporting. 

Further research shows that a large number of asset managers duplicate their fund administrator's data internally, with 55% keeping an internal accounting book of record, and staff consumed by oversight or replication of fund admin data. 

From an operational perspective, fragmented data can lead to slower processes; manual extraction, reconciliation and validation of data can result in duplication of effort; and manual assembly of data for LP and regulatory reporting can create higher compliance or reporting risk. 

And the consequences extend well beyond internal operations. 

When data is unreliable, GPs risk stalling fundraises, losing LP confidence and undermining exit valuations. Industry research increasingly links poor data quality to abandoned strategies, longer fund-close timelines and growing investor reluctance to re-commit – turning what may seem like a back-office problem into a direct threat to a firm’s competitive positioning and long-term capital formation.

Solving these data issues is just part of a bigger technological challenge for GPs – data is just one symptom of a weak technology infrastructure. More broadly, weak technology creates friction wherever information has to be captured, moved, checked, analysed, communicated or acted upon.

Consider the workflow and processes that involve technology throughout the PE fund lifecycle – from deal execution, valuations and onboarding, through to capital calls, portfolio monitoring and investor reporting. Without robust workflow technology, tasks can fall through the cracks, approvals take longer and, critically, there can be limited visibility across the status of work and portfolio companies.

Despite this, the figures for the state of technology in PE are concerning. According to the 2026 MSCI General Partner Survey, 88% of GPs have yet to achieve centralised data flow, 42% still operate on partially connected technology stacks, 32% have standardised but largely manual systems, and 13% remain entirely siloed.

It feels glaringly obvious that managers should make the most of technology because of the operational efficiencies and competitive edge it can provide – especially considering the emergence of AI. 

Fragmented data and the lack of a robust technical infrastructure create multiple risks for GPs. If data is incorrect, incomplete or not reported correctly – either to investors or regulators - it can result not only in cost penalties, but also in reputational damage, which can have a serious impact on future potential business. Yet while many GPs acknowledge the importance of technology and the future role of AI, implementation of robust, let alone innovative, tech strategies continues to lag.

As if the above challenges aren’t difficult enough, PE also faces a host of talent issues both at GP and portfolio company level. And the challenge isn’t simply finding enough people. As PE managers expand their operations, they increasingly need people with a combination of skills that can be difficult to find – investment expertise alongside operational experience, technology fluency, data literacy and the ability to deliver transformation – what are increasingly being called ‘hybrid’ professionals.

Operating partners (OPs) are a good example. Not every OP needs to be a technology specialist, but increasingly they need to understand how technology and data can support value creation. At the same time, dedicated technology, digital and AI operating partner roles are emerging as firms look to build technology capabilities directly into their value creation strategies. 

Yet these hybrid profiles are relatively scarce. And while it’s natural to assume that mega funds can pay the big wages and attract all the people they want to, they’re competing not only with other PE firms but also with Big Tech, financial services, consultancies and technology companies that can offer substantial salaries, resources and career opportunities. Equally importantly, these non-PE businesses can also offer an intellectually challenging environment. Talented people in PE will quickly get annoyed  and demotivated if they’re continuously working with manual systems and doing unrewording work.

And it’s not just about recruitment. There’s a looming talent gap because firms aren’t investing sufficient time in growing the next generation of leaders. If a firm wants a sustainable operating model, it needs to have those talents growing within the business in order for succession planning to be robust. 

The obvious question is: what can PE managers actually do about these challenges? There’s no single technology, system or hiring strategy that will solve the problem. The starting point has to be a coherent operating strategy that brings together data, technology and talent, rather than addressing each in isolation.

There are some relatively quick wins. GPs should look first at the complexity they’ve created themselves – particularly the number of technology providers, fund administrators and other third parties they rely on. Rationalising these relationships can remove unnecessary duplication and make it easier to establish consistent processes and data. 

The next step is to establish the foundations. Firms need a clear view of their data architecture, including where critical data sits, who owns it, how it moves through the organisation and what constitutes the single source of truth. That should inform decisions about the core systems and platforms the firm needs, whether those capabilities are built internally, provided by a fund administrator or delivered through another specialist third party. 

Once those foundations are in place, technology and talent can be developed together. Firms need to determine which capabilities they genuinely need internally and which are better accessed through external specialists. That might mean building dedicated operating teams or developing technology and data expertise internally, while using third parties for specialist implementation, cybersecurity, AI or transformation projects. 

Crucially, firms also need to invest in developing their own people. A sustainable operating model can’t rely indefinitely on buying expertise from outside; it needs a pipeline of people who understand the firm's systems, processes and investment model and can develop with the business.

All of this requires a medium- to long-term technology and data strategy, including the potential use of AI, which needs particular care. AI can clearly create efficiencies, but it’s only as useful as the data and systems beneath it. There’s little point implementing sophisticated AI tools if the underlying information is fragmented, unreliable or trapped in manual processes. 

Nor does every problem require a new technology solution. Excel, for example, is unlikely to disappear from PE, and the objective shouldn’t be technology for technology's sake. The test should always be whether a solution makes a process more efficient, improves decision-making or reduces risk over the long term.

There’s also a wider commercial consideration. Technology is often viewed primarily as a cost – something that requires investment at a time when managers are already under pressure on fees and margins. But for larger PE firms in particular, the more useful question is what that investment enables. Better systems can allow a manager to support more funds, more portfolio companies and more complex structures without increasing operational headcount at the same rate. 

Better data can improve reporting and decision-making, while stronger controls can reduce operational and regulatory risk. In that sense, technology isn’t simply an operating expense – over the longer term, it can be an investment in scalability, efficiency and ultimately revenue.

Fund administrators themselves are undergoing a parallel transformation – evolving from back-office processors into real-time strategic partners capable of delivering AI-driven workflows, automated audit trails and same-day NAV reporting. sGPs that leverage this shift can accelerate their own modernisation without building every capability in-house.

In the end, the question for PE managers isn’t whether they need to become more digital. It’s whether their operating model is capable of supporting the scale and complexity they’re trying to achieve and whether that’s backed by having the right people in the right place at the right time. For the largest funds, that may be one of the most important strategic questions of all.