Agentic AI, which refers to artificial intelligence capable of operating independently and achieving complex goals, represents the next significant advancement in data and computing. Early iterations, such as AutoGPT, showed promise but often encountered issues, producing nonsensical results. However, with substantial advancements in 2024 and the rapid scaling of AI technology, the realization of agentic AI may be sooner than anticipated.

Private equity deal processes and research tasks stand to benefit significantly from agentic AI. In a competitive market, the ability to make data-driven decisions earlier in the process can provide firms with a crucial advantage.

Key Takeaways

• Agentic AI Evolution: Agentic AI represents the next advancement in AI capabilities, enabling dynamic adaptation, modelling, planning, and where appropriate execution, thus enhancing private equity research.
• Efficiency and Accuracy: AI-driven tools significantly reduce the time required for deal sourcing and due diligence while improving the accuracy of valuations and risk assessments.
• Real-World Applications: Leading firms like Alix Partners and BC Partners are leveraging AI for deal sourcing, enhanced due diligence, and advanced scenario modelling, resulting in better and faster decisions.  
• Future Trends: The future of Agentic AI in private equity includes enhanced deal sourcing, improved due diligence, advanced predictive analytics, and real-time portfolio management, driving better investment outcomes and strategic value creation.

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Decoding Agentic AI: The AI Edge

Unlike its predecessors that follow predefined rules, Agentic AI adapts, plans, and executes tasks dynamically over sustained periods. This capability enables it to operate more like a human, making decisions and taking actions to achieve specified objectives. The increasing autonomy of AI systems, combined with the vast amount of available data, significantly enhances the breadth and depth of research possible, even for small teams.  

Consider the research an analyst conducts on a prospective acquisition. Currently, an analyst utilizes various data platforms, websites, and search engines to perform desk research. This data is then collected, summarised, and synthesised into documents to aid decision-making. Much of this time is spent on non-value-added tasks such as data gathering and collation. With Agentic AI, a research agent could initiate this research as soon as a company is identified as being of interest and forward the findings to the human analyst for review. This process would enable the analyst to become informed much more quickly, thereby increasing the breadth and depth of research possible.

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Real-World Success: Case Studies

The case studies below illustrate how AI is currently being used and can be used to consider the potential of Agentic AI.

Alix Partners: AI to enhance Due Diligence operations

Implementation:

• Breadth of Research: AI was used to scan and analyze publicly available data, including financial reports, market trends, and competitor information to identify opportunities and mitigate risks with an asset.
• AI analysis automation: Implemented AI-driven analytics to streamline and automate parts of the due diligence process.  

Impact:

• Faster Decision Making: Reduced the time required for due diligence from months to weeks.
• Lower risk: Provided comprehensive insights into organizational structure, market dynamics, and employee sentiment, leading to more informed and lower-risk investment decisions.

Agentic AI Potential:  

• Effectiveness: Agentic AI could take this a step further by intelligently focusing research on the most promising candidates. As soon as a good opportunity arises a research pack could be communicated for the relevant person in the firm to assess.
• Collaboration: Ad-hoc research suggested by analysts could be augmented by agentic AI. Analysts could choose whether to collaborate or delegate a task depending on the importance and suitability of the task. Thus, enabling a more collaborative way of working and further enhancing the efficiency and accuracy of research.  

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BC Partners: Leveraging Generative AI for Deal Sourcing and Due Diligence

Implementation:

In addition to an approach similar to Alix partners, BC partners has also applied AI to further areas-

• Automated Document Review: AI systems reviewed legal documents, financial statements, and contracts, highlighting key information and discrepancies.
• Accelerating Document reading: The AI generated concise summaries of lengthy documents, allowing analysts to quickly grasp essential details without reading through entire documents.

• Performance Forecasting: Generative AI used historical data and market trends to predict the future performance of potential targets, aiding in more informed decision-making.
• Scenario Analysis: AI models simulated various scenarios to evaluate the potential impact of different strategies and market conditions on the target company.

Impact:

• Efficiency: Significantly reduced time spent on deal sourcing, allowing BC Partners to focus on high-value activities.
• Improved decision-making: Broad research combined with assistance modeling risks and market dynamics, and performance forecasting led to more informed investment decisions.

Agentic AI Potential:

• Strategy Creation: Agentic AI could simulate a wider range of scenarios and autonomously develop risk mitigation strategies, offering more comprehensive and proactive risk management.
• Escalation: In high urgency and importance situations agentic AI could decide to escalate to the relevant decision makers. Thus, responding to high risks or great opportunities as soon as leading indicators allow.  

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Looking Ahead: Future Trends and Predictions

The future of Agentic AI in private equity research is promising, with significant advancements expected in deal sourcing, due diligence and portfolio management.  

• Advancements in research agents as well as the availability of data will allow AI to autonomously scan vast datasets to identify and prioritise potential investment opportunities.
• Advanced predictive analytics will help firms forecast market movements and assess potential investment opportunities using leading indicators.  
• Improved due diligence processes will enable more thorough and efficient evaluations of target companies.  
• Real-time portfolio management will provide continuous insights into the performance of portfolio companies and suggested actions to create value. This will allow firms to make high quality, timely decisions to increase return on investment and mitigate risks for the duration of the investment.  

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Data-Driven Value Creation: The Strategic Advantage

By leveraging data, digital and AI to enhance the breadth and depth of research, private equity firms can achieve better investment outcomes. Agentic AI is the next step in this evolution, potentially enabling higher returns and lower risks.

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Written by Ryan Chapman, assisted by Prism

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Sources

https://www.alixpartners.com/insights/102ij37/supercharging-due-diligence-unleashing-ais-potential-in-outside-in-analysis/

https://www.accenture.com/us-en/blogs/business-functions-blog/private-equity-generative-ai