When completing research for this article, we noticed the phrase ‘value creation’ kept appearing. Private equity (PE) – and alternative finance generally – is a dynamic industry rapidly embracing AI. However, describing AI as a driver of value creation is simply not a substantive reflection of AI’s potential in PE operations.
In this article, we’ll address the current challenges in private equity and where AI can be usefully deployed (and where it can’t – as of writing this). We’ll discuss practical and actionable insights about integration from our perspective as an AI developer who frequently speaks to PE clients.
The PE industry in 2025 is facing several challenges in the form of:
Let’s take a closer look at each challenge.
Increasing regulatory requirements can impact PE firms and individual associates alike. For example, US and EU regulators have heightened their scrutiny of PE deals that may harm competition, particularly in the context of antitrust enforcement. In practice, PE professionals may need to analyse the competitive effects of acquisitions beyond concentration by completing quantitative assessments and adopting different deal structuring methodologies. The result? More paperwork.
However, antitrust is only one regulation heaping admin tasks onto associates’ desks. Others include the SEC’s 2023 Private Fund Rules and the EU’s Sustainable Finance Package.
Overall, regulatory requirements will continue ramping up in 2025 and beyond. PE firms are under pressure to remain cognisant of the risks and take proactive steps to avoid noncompliance.
Inflated operational costs for PE firms originate from several economic angles in a post-pandemic landscape.
For example, complex deals are becoming increasingly ubiquitous. According to law firm Mayer Brown, the value of carve-outs backed by private equity firms more than tripled in 2022, surging from £3.8 billion to £13.4 billion. However, complex deals such as carveouts and add-on acquisitions cost PE firms more through the requisite due diligence, specialised expertise and integration costs.
Other factors inflating operational costs include general inflation and the high-interest rate environment. PE firms are likely paring (back) front and back-end costs to improve profitability.
As reported by McKinsey, fundraising by global private markets fell 22% in 2023 to $1.0 trillion, marking the lowest total since 2017. This decline was evident across all asset classes and regions.
For PE professionals, fundraising dips mean securing capital for new investments requires a more strategic approach. Deals may become more competitive, fundraising timelines may extend, and portfolio companies may be on the receiving end of lower valuations. In other words, falling fundraising affects everyone in a PE firm, from investment managers sourcing deals to the back-office staff completing fund administration.
A ramping focus on ESG in private equity puts pressure on ESG professionals to reflect social and sustainable values at every stage of the investment process. Achieving an equilibrium between short-term gains and long-term sustainability requires a long-term, data-driven perspective. Driving measurable positive change means translating social responsibility into practical initiatives – and AI may be able to help. Examples of these practical initiatives will depend on the company’s ESG responsibilities but may include projects like automated due diligence, modelling, real-time reporting, exploring and tracking changing regulations, etc.
As previously stated, AI’s applications to ESG demand accountability and transparency. Furthermore, if AI automates ESG data collection and analysis, it could accelerate and improve the accuracy of ESG reporting.
AI-powered tools for PE have varying functionalities. Here are three of the most common.
‘Unlocking’ useful data from long documents like Private Placement Memoranda (PPM), Limited Partnership Agreements (LPA), due diligence reports, financial statements, and quarterly or annual reports is a tedious manual process contributing to inflated PE operational costs. Newer AI tools can extract, validate and structure the data from long documents. As a result, associates can easily access actionable information rather than clicking around to find specific data.
Of course, if AI technology can ‘read’ financial data and manipulate it into a defined structure, it make sense that it can also complete robust analytics. For example, our tool, Financial Statements AI, can calculate financial ratios from uploaded documents.
In summary, document workflows can drain back and front-end resources. AI offers a reliable solution for most document-related tasks.
More sophisticated, emerging generative AI tools offer analytical capabilities. For example, these technologies can:
Instead of simply offering the raw financial data, new technologies can calculate financial ratios such as:
The ratios are then downloadable in a convenient format – Excel, JSON, CSV, etc.
AI particularly excels at identifying data patterns in datasets, meaning it can flag data that doesn’t conform to historical trends. In PE contexts, AI can be trained to detect unusually worded or categorised expenses or revenue spikes in portfolio companies.
Validation algorithms will work hand-in-glove with manual intervention. The algorithms should automatically correct inaccurately extracted or calculated data using internal consistency algorithms. Any missing data should be corrected or flagged for review and handled appropriately without skewing the output or affecting its decision-making (if inputted into a decision-making system).
AI-powered chatbots can push potential clients further down the pipeline without human intervention. AI excels at gathering user data and leveraging it to personalise customer experiences.
One specialised use of AI for improving communication is through Investor Relationship Management (IRM) software. Such software is designed to provide personalised data about investors and expedite communication. It consolidates investor interactions and tracks engagement, proferring valuable insights to enhance relationship management.
In summary, AI-powered tools for private equity generally automate repetitive manual processes like extracting and structuring data, personalising investor accounts and performing simple analyses.
For PE firms with significant economic and talent resources, developing an internal system for financial management can be an attractive option. The customisability and ownership of high-performance internal AI-powered technologies may win over stakeholders.
However, developing an AI-powered system is a somewhat formidable endeavour. Despite commercial AI firms like OpenAI offering easy-integration APIs, building an AI system with consistent functionalities can be a frustrating experience. We speak from experience.
Case in point, financial data comes with certain challenges. For example, its confidential nature makes training AI models difficult. Also, financial data’s inherent intricacy means AI will need rigorous training to (accurately) represent the relationships between large or complex financial datasets.
Even assuming your project managers and developers have sidestepped the issues plaguing ground-up internal AI development, you will later experience issues with maintaining performance. The good news is that AI’s functionalities are consistently improving. You only need to review the differences between ChatGPT 4 (released in March 2023) and its predecessor, 4-o (released in May 2023), to note how AI’s natural language and analytical abilities are doing this.
Maintaining a competitive performance from an in-house model will require constant training and updating. On a practical level, associates may also complain if the AI system often changes the formatting or quality of its output. In sum, building an in-house solution requires time and resources (as well as specialist expertise) that many companies may not have but are willing to invest in.
Using an AI vendor means outsourcing your technology: a potentially cheaper approach that might worry data security-conscious PE executives.
Some PE firms may, however, be concerned about outsourcing their data to a third party, driving them towards proprietary or in-house solutions.
Outsourcing to an AI vendor with security certifications (such as SOC2 and/or ISO27001) is crucial. That way, your data’s confidentiality is legally protected, and you can save the costs of building an in-house model.
Finding specialised alternative finance AI vendors can be challenging, albeit less so in an increasingly competitive landscape. By selecting a vendor with technological credibility (e.g. awards or contributions to academia), you can ensure that they develop their AI capabilities while keeping your data safe.
Claiming that AI offers a complete use case for private equity would be specious. There are still situations where AI might flounder in PE scenarios. Let’s examine three of them.
The difference between a junior and a senior executive often boils down to maturity and decisiveness. AI’s decision-making abilities mean it lacks the contextual nuance and sophisticated projection of outcomes that a senior executive will instinctively wield.
Of course, this is not to undermine AI’s potential contributions towards decision-making – such as identifying undervalued assets, competitive analysis and validating or disproving disruption theses during due diligence. But AI simply lacks the maturity to make decisions. Instead, consider using generative AI to augment decision-making.
AI’s processing abilities may falter when confronted with large spreadsheets filled with complex data (e.g. thousands of columns and rows). Many AI tools convert spreadsheets into images via computer vision and then analyse them. So, converting tens of thousands of cells creates convoluted images that many AI models would struggle to analyse.
Processing enormous spreadsheets is not a use case endemic to PE. Nonetheless, if you find the quality of your data is compromised during AI analysis, you could either increase the processing power of the AI model or segment parts of a spreadsheet for an AI model to analyse.
The accuracy and relevance of the output of current AI models are highly dependent on the quality of the training data. Though the training data isn’t inherently a limitation, maintaining it to improve or change an AI’s output can significantly impact the AI’s performance.
In the context of PE, where the accuracy of financial data is paramount, PE developers must be careful of how historical data is fed into the model and rigorously test changes to the model’s predictions. Inaccurate AI outputs can cause irreparable damage to a PE firm’s prospects, causing reputational damage and inducing non-compliance fines and, ultimately, financial losses.
Ensuring that AI has a smooth landing in your PE firm’s IT architecture and culture requires a conscientious approach. You’ll need to consider a few key angles when implementing AI.
Clients and investors may (rightfully) worry about potential data leaks through AI. That’s why it's essential to ensure they understand the security measures in place to protect their sensitive information. Additionally, they should be informed whenever they interact with an AI system, such as IRM software or chatbots.
A harrowingly titled Financial Times article – ‘Automation is coming for private equity’s junior roles’ – suggests that technical abilities may become less important as AI becomes better at processing information and actioning it (for example, in the form of financial modelling).
A side effect of automation is fear of displacement. As AI continues to replace some of the staples of private equity – such as spreadsheets – introducing AI may provoke friction with existing employees. An interesting article by the Harvard Business Review suggests several actionable strategies for getting colleagues on board. Examples include (clearly) establishing who receives credit with what and rewarding colleagues for the value that AI creates.
Let’s say that you’ve identified an AI vendor that matches your PE firm’s use case or drawn up the scope of an in-house AI project. How do you ensure the AI does what the vendor promises and all stakeholders are on board? Consider following these steps:
Generally, AI washing presents a serious problem for private equity and AI. For PE firms, AI washing could prevent a serious compliance issue. Throughout 2024, the U.S. Securities and Exchange Commission (SEC) has litigated against the private capital markets. The SEC intends to examine all AI-related claims made by companies or firms seeking to raise capital, regardless of their public or private status. Consequently, false claims about the presence and performance of AI technologies in portfolio companies or investment products could have serious implications for PE firms.
However, AI washing can also be an issue when adopting AI technology. As specialised AI software for PE starts to enter the sector, AI washing can compromise its welcome. For example, a data extraction tool might claim it can extract data from private equity documents but may, in reality, require time-consuming oversight. False claims about AI can damage its reputation and undermine what AI can really do for private equity.
One strategy to avoid integrating AI-washed technology into your PE firm is to choose the right vendor. Request a Proof of Concept (PoC) that proves that the AI cana what its vendor says it can.
Choosing the right AI vendor will require due diligence. You will need to check:
It’s understandable to have concerns about establishing a robust integration with your company’s IT architecture. Various integration methods exist – the most prominent is API. Commercial AI firms like OpenAI continue to offer connection via API. However, other effective modes exist, such as secure file transfer. Overall, there is no superior method of integration – the fastest and most secure choice will depend on your current workflow.
No matter how high-performing the integration, it instantly becomes worthless if compliance is breached. If sensitive data is leaked, or the integration breaches GDPR or induces a cyberattack, the results could be catastrophic for your firm’s future. Diligent, proactive compliance on the vendor and the company’s ends can ensure incident-free, long-term use.
As private equity and generative AI evolve hand in hand, we may notice certain developments, including:
Instead of financial engineering like leveraged buyouts and debt restructuring, private equity is increasingly pivoting towards smaller businesses.
What is AI’s role in this shift? Its increasing speed and accuracy will make it an efficient partner for expediting due diligence. As specialised tools for AI-powered analytics advance, portfolio performance predictions will become more accurate. PE professionals will also be able to use AI to identify and quickly act upon growth opportunities without compromising the resilience of their portfolios.
In December 2023, EY indicated that 80% of surveyed PE professionals disclosed that their primary focus was on improving companies' understanding of their cash and liquidity positions. The role of PE professionals has increasingly changed to empower firms to optimise working capital and reduce costs without compromising their ability to drive top-line growth. Again, developing AI projection tools can come in clutch: predicting future cash flow trends, which allows businesses to anticipate and prepare for potential shortfalls.
AI’s role in PE is more complex than (simply) ‘generative AI adds value’. Bain & Company put it best: ‘[AI] doesn’t create value by itself but by linking explicitly to pragmatic business objectives’. For private equity, AI offers a more cost-effective way to fulfil crucial data management processes and serve customers better. AI-powered automation and insights can help PE firms avoid regulatory and cost-related challenges.
Effective integration isn’t as simple as securely connecting two systems via an API. Meaningfully interacting with AI means getting all stakeholders – developers, clients and colleagues – sold on AI’s contribution towards your company.
Interested in learning more about transforming your private equity firm’s document workflow? Evolution AI extracts data from financial documents, and our newest product, Financial Statements AI, also calculates ratios from financial statements. Get in touch today via hello@evolution.ai.
