If you work in private equity (PE), the writing is on the wall. AI is not like a lot of other, older tech, which many firms rightfully viewed as too expensive, too slow to deliver value and a pain to use. AI today is spreading across PE, transforming investment processes, fundraising and firm management.
AI is advancing, because it can address today’s PE challenges: Achieving attractive returns is harder than ever. Leverage costs are high. And competition—including from multi-asset private markets platforms—has intensified. But AI can open new paths to target selection, value creation, improved exits, nimble firm management, and more.
Today’s leading PE firms are bringing together investment and operating specialists to integrate AI across their operations. Our recent survey indicates that 50% of PE respondents believe generative AI (GenAI) and agentic AI will have the most transformative impact on their industry over the next three years, with 54% citing these two technologies as the highest investment priority in the next year.1 The takeaway is clear: Without a move into AI, your firm will be competing without a full toolkit and you’ll likely be left behind.
Let’s have a look at specific opportunities available to PE firms right now, across three key capabilities—investing, fundraising, and firm-level operations.
Investment cycle: Enhance deal sourcing, diligence, value creation and exits
AI is transforming the private equity investment life cycle. Here’s how it looks.
Deals: New tools find new targets
AI-driven sourcing tools can streamline internal processes and ingest more data than humans ever could, so you can consider more targets, better identify them, and dedicate your people’s time to top prospects.
Leading PE firms are building AI sourcing tools to scan their vast data sets and developing proprietary models that draw on the respective firm’s unique expertise. This type of “sourcing engine” conducts an initial triage—often finding patterns that humans might miss—and ranks potential investments. Leaders can then focus on the highest-potential targets. The engine assesses both hard metrics (like financials) and patterns drawn from past investments. The end result is a tool that can help teams identify opportunities that may not be obvious.
And the engine keeps learning: As it inputs more data and the results of its own recommendations, it improves its machine learning algorithms and its outputs. And since it has an interface powered by GenAI, people can query it in plain language. The result? Their sourcing and investment teams can consider more prospects and more data, and more quickly conduct in-depth analyses on a more carefully chosen few.
Diligence and investment committees: Faster, more data-driven decisions
With AI automating so much data work, diligence can be quicker and deeper—and you can test deal theses in new ways. Could a new product turn the company around? AI simulations can let you “try it out.” And before the deals team goes to the investment committee (IC), AI can study past minutes and memos, and then ask the questions that your IC probably will. Leaders are using these tools to further stress-test their thesis and have proof points ready for discussion. In benchmark testing, we’ve seen users get productivity gains of 35%-85%, with some diligence tasks (such as competitor analysis and internal financials analysis) going from weeks to days.
Some leading firms are willing to go even further, having a platform become a non-voting IC member. It can analyze deals and market data, challenge groupthink and help the IC avoid blind spots. The end result is a process where winning firms can get the kind of detailed results that used to take months in a matter of weeks.
Portfolio company value creation: Boost revenue, cut costs
Where to find new top-line growth—fast? AI can deliver go-to-market optimization, pricing strategy, and customer analytics in portfolio companies. For example, some leading PE firms have used AI to analyze location and consumption data to help select new sites for retail-based business, understanding location data and consumer patterns. The result is a rapid expansion, informed by AI-driven forecasting. Another example lies in software development, where AI has the potential to cut time spent programming at portcos by over 50%.
Some firms are thinking even bigger. They’re conducting AI “cross-pollination,” where they start by investing in AI native companies in industries ranging from warehouse robotics to software development. They then integrate the tech from these companies into other portcos across a variety of sectors ranging from e-commerce to manufacturing. Results include automated logistics and supply chains, greater throughput, and lower labor costs.
These kinds of moves are boosting growth in portfolio companies, fast. Higher EBITDA or growth, leading to improved multiples, will come to sponsors that integrate AI early and show top-line results well before exit time.
Realization: Bespoke buyer experiences and more profitable exits
Reach the right buyers, give them the right experience, and choose your exit strategy based on hard data—all thanks to AI. AI can scan potential acquirers and investors to identify top prospects, based on strategic fit and past deal patterns. Then it can offer a bespoke experience. The right AI tool can assess how different buyers might react to different assets and tailor presentation to please them. It can, for example, quickly create a customized virtual data room for different buyers and answer common questions.
AI might then assess market conditions, valuation multiples, and macro indicators to help pinpoint the right window for an IPO or the time period when strategic buyers are cash-rich and acquisitive. And would a trade sale, a sponsor-to-sponsor sale or a continuation fund deliver a higher return? AI can simulate the likely results under multiple market scenarios to help you improve exit strategy. These activities used to be left to the expertise of third parties—at significant cost. Now, the AI tools can take the first step.
We’re seeing firms use AI to model out exit scenarios like comparing exit via trade sale versus sponsor-to-sponsor versus continuation. This is an opportunity for firms to quickly see which path makes the most sense, giving them a scenario-based vision for the future that they wouldn’t have otherwise.
Fundraising: Find and cultivate higher-probability prospects
Fundraising is tough these days. Since 2023, total capital fundraising has fallen 35%.2 The race for capital is becoming more competitive with some multi-asset managers continuing to out scale competitors. But AI can help firms with smaller fundraising teams build out new ways to find opportunities, identify untapped investors and get to them quicker, streamline prospecting and improve conversion rates.
We have seen leading firms that are leaning into AI use it to identify and target potential investors, whether institutional limited partners (LPs) or private wealth clients. The models mine historical fundraising data, LPs’ prior investments and market trends to find top prospects—like a pension fund or family office that is looking for the strategies or geographies that you’re offering. This results in a more targeted discussion, quickly demonstrating the firm’s expertise, providing more targeted content on untapped investors, and provide such information at a rate quicker than usual.
AI-powered data platforms can map the global private wealth landscape—including registered investment advisors (RIAs), broker-dealers, family offices, endowments, and foundations—to find potential LPs. As AI sifts through vaster datasets that humans could, it can often identify new prospects, like a family office that just gained liquidity from a portfolio company exit.
With AI having found the highest-probability prospects, your team can focus on them—and use AI to quickly create customized presentations, growth simulations and more.
Firm management: Grow your capacity—not your footprint
Top PE executives get it: AI isn’t a point solution. It’s the basis of a new operating model for management companies—enabling firms to find new ways of working, becoming centered around data analysis. Leading firms usually start with conversational AI chatbots that anyone can use. These firms offer their employees prompt libraries, role‑based micro‑trainings, and champion networks to get people to use AI and innovate with it. Partly-automated LP letters, KPI reconciliations, and policy memos are just some of the “quick wins” that non‑technical users have delivered.
Some have gone further, adding domain-specific AI tools for areas where the general chatbot isn’t good enough. These can be specialized SaaS offerings in areas like legal and compliance. They can be bespoke solutions based on hyperscaler offerings. Or they can be completely custom. With the right platform, firms have quickly customized and orchestrated teams of agents to execute even highly complex tasks in finance, HR and more (such as invoice screening, treasury forecasts, and talent acquisition).
Whatever path you choose, the goal is the same—a nimble, cost-effective, automated middle and back office that can quickly scale as needed. AI will sit between transaction platforms and systems of record. It will orchestrate close processes. And it will automate or augment compliance checks, treasury operations, investment onboarding, investor queries, and portfolio‑data flows. As AI does the repetitive, detail-oriented work—while people guide it, oversee it, and correct any possible mistakes—you can support faster growth without adding any more “corporate” bureaucracy.
How can I move our GenAI projects to the next step?
With so much change in the AI space going on so quickly, it can be difficult to find the plan of attack that makes the most sense. Winning firms are keeping three things in mind as they build their projects.
Apply AI the way you would put in a value creation plan as a portco. Winners know where their strengths are, lean into the change, and are willing to apply the sophistication and discipline of value creation into their AI programs.
Make your team GenAI ready. Firms need to maintain their industry expertise to be able to analyze the results generated. To do that, leaders are investing in training and spending time with the tools so that their team can benefit the most.
Seeing is believing. Firms that are going deep on AI started by showing their leadership what GenAI can do and by finding an implementation that fundamentally changes the way work is done. It’s always difficult to change a process, but when leadership can see the change in real time, acceptance becomes easier.
Even if you’re just exploring how AI can benefit your organization, it’s easy to integrate and upscale quickly. Leaders have gone all in, having AI be a part of all their capabilities from investing to fundraising.
Our suggestion is to focus on why investors wanted to work with your firm. Focus on your expertise and what you bring to the market. That remains the same. But, GenAI has the potential to expand, grow, and scale your strengths beyond where you are playing today. All you have to do is start.
A special thank you to Erich Butters, Nate Barnes, Thomas Evans, Adrien Fontannaz, Bruce Hutchison, Bert Janssen, and Sanjay Subramanian for their contributions to this piece.
