2023 Annual - (released February 2024)
SA's quarterly Private Equity & Venture Capital magazine
GenAI use in Private Equity
by Martin James McGrath
There is no doubt that advances in technology, especially in computing, can create an enormous amount of hype, speculation, overstatement and confusion – Generative AI, Quantum Computing, the multiverse, cybersecurity and “the cloud”, to name a few.
​
My first degree was in Mathematics and Computing Science, and my early career was spent at IBM and Accenture (UK, Europe and North America). Since then, I have also held senior executive leadership positions in two other global technology companies. But most people know me as the person who has led Professional & Business Services firms and been a strategic advisor to corporations, the public sector and investors (including private equity – GPs and LPs, and Pension Funds) on deals and transactions.
But this last year (especially) has got me thinking. Where have all these experts suddenly come from? Is their purpose to explain, simplify or confuse? Many times, recently, I’ve attended Investment Committees, board meetings and listened to presentations from “technology experts” who had been invited to share their expertise… bad idea.
So here is my simple advice to any senior PE executive. Keep it simple and stay away from the hype.
Artificial Intelligence is not new – think ATMs, GPS, robots in manufacturing... they need humans; “garbage in, garbage out”. Generative AI will definitely have a huge impact on the private equity industry and, currently, in 10 ways. Yes, it’s this simple.
Data-driven decision making: AI can analyse vast amounts of data quickly, and extract meaningful insights, so private equity firms can leverage AI to make more informed investment decisions by analysing financial data, market trends and other relevant information.
Deal sourcing: AI tools can help private equity firms with deal sourcing by automating the screening of potential investment opportunities. AI algorithms can analyse various data sources, including financial statements, news articles and social media, to identify potential targets that align with the investment criteria.
Due diligence: AI can streamline the due diligence process by automating the analysis of legal documents, financial records and other relevant information. This can enhance the efficiency of due diligence and help in identifying potential risks and opportunities associated with a deal.
Portfolio management: AI can be used to monitor and manage portfolio companies more effectively. Predictive analytics and machine learning algorithms can provide insights into the performance of portfolio companies, helping private equity firms to optimise their strategies and maximise returns.
Operational improvements: Private equity firms can use AI to drive operational improvements within their portfolio companies. This may include implementing AI-powered technologies for process automation, supply chain optimisation, and other efficiency-enhancing initiatives.
Risk management: AI can assist in identifying and managing risks associated with investments. Machine learning algorithms can analyse historical data to predict potential risks and market fluctuations, allowing private equity firms to make proactive decisions to mitigate these risks.
Exit strategies: AI can play a role in optimising exit strategies by analysing market conditions, identifying potential acquirers, and providing insights into the best timing for exits.
Communication and reporting: AI-powered tools can improve communication and reporting processes within private equity firms. Automated reporting systems can generate real-time updates and performance analytics for investors.
Cybersecurity: With the increasing digitisation of financial processes, private equity firms need to focus on cybersecurity. AI can be employed to enhance cybersecurity measures and protect sensitive financial information from cyber threats.
Talent management: AI can assist in talent management by analysing data to identify key skills and competencies within portfolio companies. This information can be valuable in making strategic decisions related to talent acquisition and development.
While the integration of AI in private equity brings huge opportunities, it also poses challenges related to data privacy, ethical considerations, and the need for additional skilled professionals to manage and interpret AI-generated insights. As the technology continues to evolve, private equity firms will need to adapt and strategically leverage AI to stay competitive in the market. A fund needs an inhouse AI strategy and implementation plan, as well as driving AI strategies into the assets under management.
For years, I have been recommending that GPs and LPs need to have an “inhouse” OP (operating partner), but cost allocation, deal provisions, fund raising and other priorities have prohibited this. Instead, a whole industry has been spawned – Big Four (deal services, transaction support, DD etc) specialty firms. AI has huge potential for PE, and it is time to own it.
McGrath is Managing Partner at Cornhill Walbrook LP, Corporate leader, PE Strategic Advisor, Value Creation and Sector specialist.