Billionaire Bill Ackman took an optimistic approach to private equity: “I think good private equity investors create a lot more economic value than they destroy.”
To create value, however, private equity investors must be equipped with the right tools. AI-powered automation technology, with its promises of improved speed, data accuracy and easy integration, might seem like a tool for easy growth.
However, attitudes about AI within private equity vary. In this article, we’ll examine some publications about the role of AI-powered automation within the evolving sector of private equity, including where there might be complex perspectives about AI.
Sometimes, when people visualise integrating AI technology into their company IT, they imagine hours on the phone with their tech team. However, secure integration is achievable via API, secure file transfer or using the interface of a secure tool directly.
However, getting a tool to high performance is a different issue, particularly if you’re training it on proprietary data. Trowers, rightly, flags a key problem with training an AI model with proprietary data: ‘The key to minimizing risk and maximizing GenAI’s potential requires human intelligence and interventions to integrate with unbiased and ethically sound data.’
These issues can be sidestepped by using an AI-powered automation tool that doesn’t require extensive training (such as for data extraction, website chatbots and AI security tools).
Additionally, the article references predictive analysis to identify high-potential investments, which might not represent every use case in private equity. EY suggests that AI should be deployed to ‘bolster value across the entirety of your investment lifecycle’ - an ambitious stipulation considering that most PE companies are in the test-and-learn phase of adopting AI, according to Bain.
As we’ve identified before, there is a lot of nebulous language in private equity about using AI to drive value and not a lot of information about the technical integration of AI into existing systems. One of the best resources for mapping successful integrations is AI vendor case studies, which should mention how a particular technology was implemented to meet a private equity client’s requirements.
It is generally accepted that the right implementation of AI can improve the accuracy of data like financial reporting and portfolios. Practically, AI can improve data quality by:
…and so forth, depending on the use case. However, as First Rate noted, precision and accuracy are only one facet of a high-functioning AI-driven private equity solution. The need for accuracy shouldn’t eclipse the need for ‘dynamic calculation methodologies’, where clients can interpret and report data flexibly.
Obviously, the need to balance precision and usability presents a challenge to private equity solution developers. Yet, within private equity culture, accuracy is non-negotiable. AI solutions promising ‘95% accuracy’ aren’t going to be of great help to professionals working 90-hour weeks to prepare and action financial data.
The Financial Times suggests that AI will change the culture of investment, leading decision-making away from intuition and towards ‘greater emphasis on data and analytics.’. CFO Tech clarifies the effects of pouring unstructured data into the hands of fund managers: ‘The escalating volume of data raises concerns about declining accuracy and quality.’ Maintaining accurate, compliant and relevant data presents a new challenge for private equity professionals. AI can help private equity firms take a data-driven approach by funnelling more information into their hands. Yet, AI itself requires a data-driven approach, meaning that firms will need to have a robust understanding of what generative AI can achieve in terms of statistical techniques.
We interviewed David Gilchrist, Head of Product Development at Ocorian Fund Services, about attitudes to AI.
Ocorian is a global provider of fund administration services, offering comprehensive solutions to support alternative investment fund structures. It provides fund administration, accounting, and compliance support for private equity funds, ensuring efficient management of investment structures.
Q: Given the diversification of investor profiles in private markets, the convergence of public and private market expectations, and the demand for greater transparency, how can firms proactively leverage AI and other emerging technologies to stay relevant and effectively implement new solutions as they evolve?
A: Diverse investor profiles and new fund structures for private capital require a whole new level of transparency and data-led analytics. Clients and their investors now expect real-time, on-demand data access. Fund service providers must offer secure, readily available records and tailored digital workflows that empower self-service and transparency. Deploying advanced solutions like AI requires a robust data architecture and consistent processes. Ocorian Fund Services has established this foundation through a global operating model, enabling the rapid and scalable deployment of solutions like portfolio and fund-level analytics and AI-powered workflows. This allows our clients to proactively meet their evolving needs.
Q: How are you using technology to keep up with the pace of change in the fund administration industry?
A: Technology is absolutely essential for scaling our operations and effectively leveraging our deep expertise and data insights in today's rapidly changing environment. We are strategically and securely integrating readily available AI-enabled solutions into our tech stack. A robust data architecture is central to this transformation, enabling us to provide clients with the real-time access and self-service capabilities they now demand, allowing them to analyse their data instantly and in any way they choose. Continued investment in our technology is crucial for meeting these evolving needs.
Furthermore, fund structures are more complex than ever, requiring us to capture significantly more data points. Without the right technology and structure, providers rely on labour-intensive manual processes to handle incoming data. Straight-through processing and tools that efficiently extract data are crucial for servicing the types of mandates we're seeing today. At Ocorian Fund Services, we’re prioritising solutions that automate repetitive manual tasks, streamline unstructured data processing, and expedite “first draft” reporting, to service our clients even more proactively.
The markets are evolving and expanding at an unprecedented rate, pushing clients, investors and providers to adopt new technologies and develop innovative solutions. Automation, on-demand data access and data visualisation dashboards are no longer optional—they're necessities. Our focus is on freeing up our team's expertise from manual processing so they can dedicate more time to collaborating with clients, understanding their needs and designing scalable solutions that directly address those requirements.
Gilchrist’s comments reflect the importance of identifying operational processes - such as data extraction - that are preventing PE analysts from client-facing processes. Currently, there are no AI tools that can automate client communication, which is sensitive and requires a human touch.
A vibrant and proactive sector, private equity is poised to make AI a necessity. As David Gilchrist noted, the private equity sector is being ‘pushed’ into adopting, developing and experimenting with AI. Intra-sector perspectives on AI will vary, but that won’t necessarily hinder its adoption, as the consensus across many private equity publications seems to be that it can increase accuracy as long as the integration and culture shift are handled carefully.
The priority for PE firms should be to move out of the ‘test and learn’ stage into real implementation. It’s one thing to explain the benefits of AI. It’s another one to translate it into real results for your organisation.
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