By Justin Partington, Global Head of Fund and Asset Managers
Artificial intelligence (AI) has morphed from the stuff of science fiction into an everyday commodity in an astonishingly short period of time. From AI-powered assistants such as Alexa to smart home appliances, email spam detection and predictive text, AI is now deeply embedded in our lives.
Public markets algorithmic trading is also dominated by big data and AI, but the private markets ecosystem has been slower to embrace this technology, choosing to rely on acquired wisdom and gut instinct instead.
This is now changing. In a fiercely competitive environment, data and not human acumen, is now king. It is data that unearths deal opportunities; supports deal screening; enables better informed investment decision making; improves portfolio management and optimises exits, as well as enhancing investor relations and operational efficiencies.
Meanwhile, generative AI and now agentic AI have truly captured private markets’ imagination. Since ChatGPT exploded onto the scene in 2022, everyone has become excited about the potential of large language models (LLMs), and AI’s potential is now dominating our conversations with clients. These burgeoning technologies have the potential to be gamechangers for the industry, transforming firms from front to back office, and across every aspect of the investment life cycle.
AI across the investment life cycle
AI is being actively deployed in the deal origination and curation phase, with EQT’s Motherbrain amongst the most notable examples. AI-based origination tools can be used to track vast quantities of evolving data points including companies, historical deals and people, with bespoke algorithms layered on top to help identify and then secure a competitive angle in an increasingly competitive investment market.
AI is then fast becoming an established feature of due diligence, where it can be used to generate insight from a wide array of third-party sources of information. Examples include the ability to scan a target’s website on a time tracked basis, using natural language processing to identify weaknesses in employee retention through the job ads, or by ingesting social media content to get a handle on customer sentiment.
AI also has an interesting role to play in the portfolio monitoring and value creation phase, where it can be used to improve efficiency, drive profitability and reduce risk. A lot of firms are still relatively passive in their investment approach, reacting to company results but not proactively predicting future performance. That might be about to change, however, as AI-powered analytics could help private equity firms not only identify future trends (through predictive analytics) but also respond to those trends with appropriate action (prescriptive analytics), enabling them to adapt their strategy accordingly and potentially change the outcome of an investment.
Finally, AI is supporting exit activity, in what has undoubtedly been a protracted exit drought. AI can be used to track precedent transactions and profile potential acquirers, as well as helping to optimise exit timing.
AI and fund administration
As an industry that is built on data, AI is also revolutionising fund administration, where it is being used to automate manual processes, streamline documentation and, increasingly, interact with clients and answer queries.
For a number of years now, dedicated data platforms or portals, hosted by the administrator, have enabled private markets firms and their investors to access reporting, transaction management, regulatory compliance and data analysis and visualisation tools anywhere, anytime, without having to rely on direct communication with the fund admin.
This self-service model, whereby data platforms process data from both LPs and GPs and then present the information back to clients in a format that is easy to digest, analyse and interrogate, has fostered trust, transparency and real-time visibility as well as faster fund operations and improved efficiency. It has also enabled a more customised service, with platforms tailored to clients’ specific needs.
In addition to their own data, clients can access ‘big data’ that is integrated into the system, facilitating benchmarking and trend analysis. Ultimately, these administrator-led data platforms will be able to provide predictive, and even prescriptive, analytics to clients, helping shape investment and operational strategy. These tech advances are both complex and fast-moving. The good news from the client perspective, however, is that the entire data platform will continue to appear seamless on the surface.
Meanwhile, with the advent of generative AI, client interaction and self-service models are continuing to evolve – at speed. Administrators are developing tools that leverage natural language processing to enable clients to query their own data packs in the same way that they might type a question into Google.
At IQ-EQ, we have direct data exchanges with some of our larger clients, who’ve described this as life-changing. They’re able to access their data quickly and independently through our platform, then take it forward for reporting and ingestion into other systems. Now we’re going to be able to do all this and more, by enhancing the service with AI.
The role of AI agents
AI-enabled client chat platforms, based on agentic AI, are also being explored. AI-powered agents will not only be able to converse with clients but also autonomously take steps to meet that client’s objectives, using an ensemble of machine learning, natural language processing and automation technologies.
An AI agent will soon be able to answer client questions about portfolio data, compliance status and trade history, for example. An AI-based conversational bot will be able to take into account all the contextual elements relating to a particular client and then provide advice accordingly. This is all part of our future roadmap.
All these developments pertaining to client interaction and self-service will help drive efficiency, accuracy and transparency within the private markets industry. It all comes down to labour arbitrage. As AI becomes part of our ‘business as usual’, it will speed up delivery to clients whilst also increasing data accuracy and integrity.
Handle with care
Despite the seemingly limitless possibilities that AI appears to offer, it is, of course, vital to remain cognisant of the risks. From hallucinations to data protection concerns, AI needs to be handled with care.
Client data must be safeguarded entirely from the now ubiquitous ChatGPT. Closed AI models are essential in the private markets industry. And, for now at least, it’s vital to ensure there’s a human in the loop at all times when it comes to producing client deliverables.
Indeed, it’s important to view AI as a supplement to the human element of the private markets landscape – a tool that can magnify the human capacity for manual tasks and absorbing information, while augmenting decision-making and freeing up people for the people-led side of the business.
After all, while it may theoretically be possible that bots could one day run a completely hands-off investment cycle, with AI responsible for all functions from back-office administration to investor relations, origination and transacting, that scenario seems unlikely. Human rapport remains paramount.
There is no doubt, however, that data is now the currency of this industry and leveraging the latest iterations of the most powerful technologies to maximise its potential will increasingly become a critical differentiator at every stage of the investment journey.
