How AI technology transforms private equity fund operations
11 July 2025 - As reporting requirements intensify, PE firms are reappraising their infrastructure needs and exploring new, AI-powered options for improving their capability, say Citco Fund Services’ Claudia Bertolino and Timothy Harvey
As the industry’s assets under management have grown, how have the operating models of private markets firms changed?
Claudia Bertolino: The evolution has been significant. The industry has gone from legacy quarterly investor reporting, produced within 45 to 60-day timelines, to more frequent, granular reporting provided within substantially shorter timelines. Historically, there was far less focus on day-to-day operational support than there is today.
There is also an increasing reliance on third-party service providers to support operational functions in private markets. Outsourcing has been an established feature of public market back-office functions for years, and it is only relatively recently that the private markets space has begun to narrow that gap.
This has been driven primarily by regulatory necessity and LP demand. Alternative managers now require a larger operational infrastructure to support the compressed deadlines and more detailed reporting demanded of them, and outsourcing has offered a cost-effective option for providing that infrastructure. Outsourcing helps support growth of private market firms who utilize technology to achieve scale in turn easing the burden of meeting compressed reporting timelines.
Timothy Harvey: Five to 10 years ago, managers could send a PDF to investors each quarter and that would be sufficient, but as volumes and complexity has grown over time, and in parallel the granularity and transparency has also grown due to demand from investors, industry bodies and regulators. This means that digitised processes are essential to deliver the granularity required, and outdated technology architecture will no longer support the reporting needs and reporting timelines.
The fact that private capital has expanded beyond private equity and into other adjacent asset classes, such as private credit and infrastructure, is also another driver behind this trend. Managers are now running multiple investment strategies and funds across their platforms, so the need for greater automation to streamline processes and stay ahead of the nuances that come with each additional asset class has never been greater – indeed, the industry can no longer just rely on spreadsheets.
What tools are managers using to upgrade their operating models and to process their greater workloads?
TH: The availability of data, and the associated investment in technology to collate and present that data, has been a key factor in supporting the higher ask on reporting volumes and tighter deadlines. Managers are reviewing the entire digital lifecycle of their operations. How is data collected, processed and transformed within the manager’s operational ecosystem? How is that data then digitised and reported back to LPs? And how can managers facilitate a full digital flow of information between their firms and their investors? These are key questions the industry is working to answer.
CB: Digitisation and automation go hand in hand. Carried interest calculation for Waterfall for example, are a crucial process and have lived in spreadsheets for years because of the complexities and bespoke nature involved in the calculation. It is now possible, however, to automate that process by bringing digital capability into play – here, the key trends to look for will be the integration of AI and Machine Learning, which could revolutionize scenario modeling and risk assessment, providing even more accurate and timely insights.
And in time, as fund structures become more complex, we can also expect to see more tailored technological solutions that cater to specific fund types and strategies. It is the fund managers who embrace innovative technologies and adopt proactive risk mitigation strategies who will be better positioned to navigate these challenges successfully.
Looking at AI and generative data in particular, how are these technologies reshaping the digitisation of private markets operations, and what do managers need to have in place in order to unlock the full potential of AI?
CB: The fund administration market has reached a point where most providers are proactively automating end-to-end processes wherever they can, be it on the GP side or on the service provider’s end; identify what can be automated, get it documented, get it built and get it streamlined.
The industry is now at a stage where it is not only seeking to automate a process in order to meet a standard, but to build automated systems that can deliver bespoke capabilities and perform complex calculations on a regular basis. AI is a really helpful tool for delivering that, but harnessing the power of AI comes back to the point about availability of data. AI technology will not be able to run speedy calculations if the data required to run those calculations is not available in the first place.
The ability to create tools that can scrape, ingest, normalise and synthesise vast amounts of data is the foundation for using AI to drive more complex and detailed automation. In turn, it is the managers that can put such building blocks in place that will be the best placed to leverage AI to deliver data and reporting that provides insights and informs decision-making.
TH: That interconnection between data and AI definitely presents an interesting dynamic. You have to have the data available in order to use AI effectively, but AI can also be used to enrich the data collection process in the first place, and help with data validation.
Instead of having to tick every cell on a spreadsheet manually, clients can now use AI to review and validate data. PDF and document scraping technology has existed for many years, but there was always a question mark around whether that data was actually correct. We have found AI to be particularly powerful when it comes to exception-based reporting – as it removes much of the legwork and encourages more confidence in the data.
Last year, we launched Citco Document IntelligenceTM, which is the first AI-plus-human platform to offer GPs and clients / investors a fully-managed document service. By combining AI and fund reporting experts, it interprets, organises and transforms documents into discoverable information and insights, helping managers to avoid missing opportunities as a result of the high volumes of emails they receive and the lack of structured data. Technology has advanced at pace, and in addition to ingesting data, AI is now being used to catalogue data and making it usable for downstream users.
How far along the curve is the average GP in terms of utilising some of this AI-powered capability?
CB: We have observed quite a wide gap in capability across the market. Some managers are extremely invested in this and are well prepared to utilise it – these early adopters have been thinking about this and laying the foundations for a long time. At the other end of the spectrum, there are GPs that really haven’t looked much into AI and automation at all and are therefore relying heavily on their service providers in this regard – and then of course there is a large body in the middle.
Where the GP sits on the curve shapes how service providers will interact with them. For managers that are more advanced, the focus is on accelerating their connection with service provider tools and linking that up with the technology that GPs already have in-house. If GPs don’t have the in-house technology stacks in place, the priority is to introduce these managers to new technology and provide opportunities to build their businesses around a service provider’s tools at scale.
TH: There is an interesting pattern whereby managers that invest in AI companies as part of their portfolio and they are usually also at the forefront of how AI can help them in their own operations.
Equally, there are other managers who don’t have the in-house capability, but are very interested in how their fund accounting and CRM vendors, among others, are using AI to build and deliver their services, and then leverage that expertise. This might be for very advanced functions, or more simplistic tasks. As soon as the use cases start to snowball, however, I think we will certainly see more uptake and more GPs jumping in with two feet.
So, for a GP that is starting a digitisation process from scratch, what is the first step?
TH: That’s an interesting question, because it highlights how interlinked technology and the private equity operating model have become. The approach to embedding technology in a firm starts with a decision on whether the GP wants to proceed full steam with outsourcing from day one or still retain some functions in-house. That is not just a technology decision –it goes to the heart of how the firm is run and interacts with its LPs and portfolio companies. It also affects how a GP thinks about their team, and it informs hiring decisions. There are new senior roles emerging within firms all the time, as managers bring in the likes of chief data officers and data stewards.
Managers are changing, and their reliance on data, and the requirement for that data to be reliable and available in real time, is increasing. Ten to 15 years ago, that wasn’t the case in the same way in this asset class.
CB: When there is a blank canvas, the manager is in a very different position to make decisions compared to a manager where legacy processes and technology are retained. As Tim indicated, when there is a legacy aspect to a digitisation process, the scope of the project becomes much wider as the GP is not simply implementing a new software system or technology – they are instead proceeding with a project of transformational change, and that demands training and consultation with the firm’s people.
Each firm will have its unique requirements. The key priority is to build an operational model that aligns with a manager’s culture and priorities but also allows a manager to be nimble and in a position to keep pace with technological developments, without having to slow down from time to time to reintegrate and redesign an operating model.