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In brief
Private equity (PE) is at a turning point. Until now, two value levers have dominated: financial engineering – the skillful structuring of capital – and operational excellence – increasing efficiency and growth in portfolio companies to enhance corporate value through higher EBITDA margins and improved market multiples. Today, artificial intelligence (AI) emerges as a standalone third value lever. It is more than a tool for operational excellence as it enables data-driven real-time analysis and autonomous decision-making, fundamentally changing business models. According to a recent EY study, 84% of PE funds expect AI to have a significant transformative impact on their business. Thus, AI is not just a component but the next evolutionary stage of existing value enhancement levers – with the potential to sustainably transform the industry.
This development is no coincidence. The rapid progress in AI research, especially in generative AI models (GenAI) and large language models (LLMs), has revolutionized the possibilities for data analysis, automation and decision-making. At the same time, a number of macroeconomic and industry-specific factors are pushing into the PE sector, unlocking new levers for value creation: On one hand, margin pressure is growing in core industries, accompanied by rising expectations from limited partners (LPs). On the other hand, data availability is increasing, allowing for faster and better-quality assessments of investments – alongside record volumes of investable capital ("dry powder").
This article highlights how PE firms are addressing this dual challenge: On one hand, they optimize their internal processes and the deal process through AI-driven technologies. On the other hand, they transform their portfolio companies comprehensively by deploying AI-based solutions in various areas – from finance functions to operational processes to sales, marketing and finance functions – thereby responding to the disruptive changes that AI induces in business models. It becomes clear that AI is much more than a mere technology tool – it represents a strategic force that significantly influences all phases from deal sourcing to investment decision to exit.
In the following, we will analyze the role of AI in three central areas:
Recent breakthroughs in AI are fundamentally based on the development of large LLMs and GenAI. Models like OpenAI’s GPT-4 or Google’s PaLM are capable of understanding and generating natural language tasks, from texts to programming code to complex data analyses. The possibilities range from automated document creation, analysis of contracts to generating strategic scenarios. About two-thirds of our PE clients had implemented at least one AI initiative in their portfolio by 2024.
For GPs, this opens up numerous concrete advantages. On one hand, AI enables enhanced data analysis: It can evaluate immense data volumes from various sources such as financial reports, media, market data and internal systems in real-time, allowing for more informed and faster investment decisions.
For example, EQT has been using its internal platform “Motherbrain” since 2018 to automate and analyze public and web data sources in conjunction with AI and human expertise to identify potential targets.1 Blackstone has also successfully utilized AI in deal sourcing since at least 2021: An internal AI platform supports pipeline screenings while significantly reducing processing time.
On the other hand, repetitive tasks are automated, making standard processes in due diligence, reporting or LP management more efficient. Moreover, AI improves forecasting capabilities by considering not only historical data but also external influencing factors such as macroeconomic trends and competitive developments. Additionally, AI enables personalized communication by targeting LPs, management teams and other stakeholders with tailored content.
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Read moreWith the official launch of GPT-5 on 7 August 2025, the next generation of large language models will be available. Compared to GPT-4, it offers improved processing of longer and more complex contexts as well as expanded capabilities for multi-stage analyses – an advantage for PE applications such as automated screenings, analyses or scenario planning. The speed at which such technological advancements occur is difficult to predict, prompting GPs to continuously monitor technological developments to leverage efficiency and value enhancement potentials early. Given increasing competition, rising investor expectations and an exponentially increasing amount of data, the targeted use of such AI technologies will become a decisive factor for long-term competitiveness.
Structural trends drive AI adoption in PE:
The combination of structural industry trends, increasing competitive pressure and growing data availability heightens the necessity to unlock new value enhancement levers in PE. The technological democratization of AI – that is, the now significantly easier, cheaper and more flexible access to AI solutions – enables GPs to systematically and broadly deploy transformative technologies. Thus, AI becomes a decisive factor in securing competitive advantages and realizing sustainable value creation in an increasingly data-driven environment.
The increasing integration of AI into the organizations of GPs represents one of the most significant transformations within the industry. By applying AI technologies in internal processes, GPs can substantially enhance their operational efficiency and effectiveness. Below, we explain the use of AI that we observe in the market and with clients.
AI is becoming increasingly important for GPs as it helps make business processes more efficient and effective. Here, the focus is not on technical innovation but on the pragmatic use of AI as a tool to optimize existing workflows. AI automates manual tasks such as data processing, report analysis or document creation, enabling significant efficiency gains. Furthermore, AI supports data-driven decision-making by analyzing large data volumes and providing relevant insights in real-time. The implementation of AI occurs gradually and builds on existing processes to improve them purposefully. The goal is to establish pragmatic solutions that create short-term value and can be flexibly adapted to new requirements in the long term. The following section details the central application areas of AI in GPs, supplemented by practical examples.
AI fundamentally changes central GP tasks. From efficiently managing existing LPs to more precise fundraising forecasts to reporting, talent acquisition and due diligence – intelligent applications noticeably increase speed, accuracy and strategic impact in everyday private equity operations.
Managing existing LPs is one of the core tasks of GPs. AI analyzes preferences, information needs and communication histories, thereby steering interactions purposefully. It automates and personalizes reporting, quarterly reporting, update calls, communication letters and announcements. Ardian uses its own AI-based tool for this purpose which creates individualized status reports, automatically answers questions about portfolio development and significantly enhances the efficiency and consistency of dialogue with LPs.2
In the fundraising context, GPs are increasingly relying on AI-driven forecasting models to enhance efficiency and planning certainty. Machine learning models analyze historical fundraising data, market indicators and investor behavior to predict the likelihood of capital commitments. An example is Blackstone: Since at least 2021, the company has been using ML models to simulate fundraising quotas and calculate commitment probabilities.3 This allows for more strategic control of fundraising processes and alignment with expected developments.
The finance departments of GPs are under high pressure to deliver precise and timely reports for investors and supervisory bodies. AI significantly facilitates this task. For instance, EQT has been using an AI-based cash flow forecasting model for liquidity management since 2022. Dynamic KPI dashboards enable real-time representation of key figures such as IRR, multiple, EBITDA development or valuation changes. A North American mid-cap fund was able to reduce its reporting time from four person-days to under one hour through such AI dashboards. Additionally, generative AI tools like GPT-4 are used to automatically create standardized and individual reports with commentary analyses. C Partners, for example, uses the iLEVEL platform for semi-automated creation of portfolio reviews and dashboard reports.
Access to experienced management talent remains crucial for the success of portfolio companies. AI creates real value here: It enables targeted matching of talents to the requirements of the investments. For other HR topics, such as assessing team dynamics, identifying development potential and deriving targeted measures, AI-based applications are employed. Providers like CultureAmp offer automated feedback evaluations for objective assessment of team performance in companies. Furthermore, PEs benefit from AI applications in onboarding processes and knowledge management: LLM-based assistants support new hires with context-relevant information and automate standard and routine inquiries.
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Read moreThe use of LLMs as an internal thought partner is quickly becoming the standard in the PE environment. These technologies automate the creation of deal-relevant documents such as contracts, investment memoranda or due diligence questionnaires and adapt them individually to the specific requirements of each deal.
EY also actively uses AI, particularly LLMs, to make due diligence processes more efficient and precise. AI-driven tools are employed for the automated review and analysis of large volumes of documents. This helps EY support PE firms in identifying risks more quickly and making informed decisions – a crucial competitive advantage in an increasingly data-driven market.
AI has become a central value driver in the investment process: In all phases of the deal lifecycle, it supports PE firms in acting faster and making better informed decisions.
Thus, the use of AI in the PE sector goes far beyond process automation. What remains crucial is how purposefully AI solutions are integrated and further developed. Only in this way can sustainable efficiency, better investment decisions and a strong positioning against LPs be achieved. However, without clear strategic anchoring, much potential remains untapped – the competitive advantage arises only from the interplay of technology, organization and competence.
AI is increasingly having a strategic impact in portfolio companies. GPs should systematically identify potentials and deploy them in central functions:
Pioneers like Vista Equity rely on cross-functional AI systems – a model that is increasingly becoming the standard.
AI not only questions processes but entire business models. Disruptions occur along three dimensions:
New AI-native competitors operate with platform logic, low marginal costs and high scalability. Traditional market analyses are no longer sufficient – GPs need adjacency scans, open-source monitoring and tech radars.
Strategic guiding questions for portfolio companies:
a) Which value-adding processes can be supported/substituted by AI?
b) Where does new willingness to pay arise?
c) What role does my company play in the AI value chain?
d) Which offerings can be scaled through AI?
e) What does my defensive scenario look like?
The integration of AI in PE brings not only efficiency gains but also complex risks – from algorithmic biases and lack of transparency to knowledge loss and data protection/system risks. GPs address these challenges through diverse data sources, explainable AI, targeted training, robust security measures and clear governance structures.
AI has long become a strategic pillar in private equity. Those who understand it not just as a tool but as an integral part of the business model can recognize opportunities early, actively manage risks and secure sustainable competitive advantages.
Private equity (PE) is undergoing a significant transformation as artificial intelligence (AI) emerges as a third value lever alongside financial engineering and operational excellence. AI enhances efficiency and decision-making across the investment lifecycle, from deal sourcing to exit processes. With 84% of PE funds anticipating AI's transformative impact, firms are leveraging AI for faster analyses, improved forecasting and optimized internal processes. The integration of AI not only streamlines operations but also disrupts traditional business models, creating new revenue opportunities. As competition intensifies, mastering AI technology becomes essential for sustainable value creation in the PE sector.
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