AI in Private Equity: A Guide to Smarter Investing

Tribe

The PE landscape is evolving rapidly, and the way private equity firms operate and make investment decisions is being redefined by cutting-edge technologies, many of which are powered by artificial intelligence. 

In this article, we explore the role of generative AI, machine learning, and data science in the private equity industry, taking a close look at key applications, risks, and ways that private investment firms can harness the potential of artificial intelligence to maximize profits.

What is the role of AI in private equity?

On a high level, AI and data science help private equity firms source deals more efficiently, streamline decision-making, and create more value for portfolio companies and investors across the investment lifecycle, at scale.

Traditionally, deal sourcing, due diligence, and decision-making processes in private equity rely on market familiarity and expert judgment. Investment professionals support their decisions with industry knowledge, advanced financial analyses, and detailed evaluations of target investments. However, historically these decisions have been ultimately judgment calls, often affected by personal biases or human errors in the interpretation of available data.   

The same goes for portfolio management processes. While all PE firms seek to create value for portfolio companies by implementing operational improvements and growth initiatives, they often base their strategies on historical practices rather than data-driven insights, which may lead to suboptimal outcomes.

While AI will likely never be able to replicate the judgment of seasoned investment professionals, it can supercharge their work with operational efficiencies, proactive insights, detailed data analysis, and more reliable predictions — strengthening their ability to make data-informed, profitable investment choices.

Below, we dive into specific use cases in more detail. 

4 uses for AI in private equity

1. Deal sourcing

The worst thing that can happen to an investor is to miss an opportunity. 

Luckily, AI's data analytics capabilities truly shine in the realm of PE deal sourcing. AI-powered algorithms can sift through vast amounts of structured and unstructured, public and proprietary data, surfacing potential investment targets. This optimizes the analysts’ time, minimizes the risk of biased evaluations, and gives teams unique insights into the market and a competitive edge. 

Wondering what this could look like in practice? 

Tribe AI partnered with a leading PE firm to build an ML-driven market research toolkit that would improve the team’s confidence and speed in pursuing investment opportunities. 

The firm needed a way to score a specific geographical region within the US to determine how attractive it was for investment — based on publicly available data. Tribe put together a team of three specialists – a technical product manager, an ML engineer specializing in predictive models, and a data scientist with a background in working with public data sources. The team gathered publicly available data (including census data and social demographics, consumption patterns, upcoming infrastructure changes, etc.) and fed them into a custom ML model that was able to successfully produce meaningful scores based on the available information.

The custom toolkit offered the PE client multiple levels of value add. Most importantly, it empowered them to evaluate potential investments at increased speed and confidence. It also helped the investment team showcase the value they could bring to prospective portfolio companies beyond capital. 

Read the full case study here: Building a Proprietary Investment Engine Using Public Data for a Top PE Firm

2. Due diligence

With the right AI systems, private equity firms can streamline the due diligence process, including evaluating technical data, analyzing post-investment opportunities, and using algorithm-powered investment hypotheses to move quickly and confidently in a crowded market.

Let’s take a closer look at these benefits:

  • Technical data evaluation. With the help of AI systems and the guidance of data scientists, PE firms can handle technical data related to potential investments with precision. This includes accurately assessing the feasibility of technological solutions, understanding product development lifecycles, and identifying innovation opportunities within target companies.
  • Algorithm-powered investment hypotheses. AI systems can help PE firms develop data-backed scenarios that consider market trends, customer behavior, the competitive landscape, and more — and simulate different outcomes to guide investment decisions.
  • Swift and confident decision-making. AI is able to rapidly process complex datasets, providing actionable insights in real-time, and empowering PE firms to make confident investment choices.
  • Customized insights for a competitive advantage. Collaboration between data scientists and investment teams can result in data-driven insights tailored to the firm's unique investment strategy. Identifying opportunities that align precisely with the firm's vision and goals can build up a competitive advantage in the crowded private equity space.

3. Portfolio management

After an investment is made, artificial intelligence can help PE firms support portfolio companies on an ongoing basis. AI-driven solutions can be used to inform forecasting, optimize pricing, and automate highly manual processes to streamline operations and, ultimately, drive value for portfolio companies — and investors.

Here are some specific ways that AI and data science can be used to drive value across a PE firm’s portfolio:

  • Extracting actionable insights from data from various sources
  • Providing reliable market, competitive, and customer sentiment analyses
  • Analyzing performance with a focus on eliminating inefficiencies and cutting costs
  • Automating repetitive tasks, allowing employees to focus on strategic initiatives
  • Optimizing supply chain and manufacturing processes
  • Identifying improvements and personalizations in the company’s offering to better match customer expectations and increase sales
  • Suggesting optimizations to pricing models based on market conditions, demand fluctuations, and competitive positioning
  • Assisting talent management teams in identifying skill gaps, building training programs, and enhancing recruitment processes

4. Risk management

Data analysis powered by artificial intelligence and machine learning can help you identify patterns and trends that may indicate potential risks — in both your portfolio and pipeline — and empower your team to mitigate them before any damage is done.

Here’s how AI and data science can support PR firms in the realm of risk management:

  • Improved pattern recognition. AI and ML algorithms excel at identifying intricate correlations, even within large and complex datasets — and are able to uncover subtle patterns that might go unnoticed through traditional analysis methods.
  • Early warning signal detection. By monitoring data in real time, AI-powered systems can detect early signals of potential risks. For example, sudden changes in financial metrics, market sentiment, or industry regulations can trigger alerts, allowing firms to take proactive measures promptly.
  • Predictive analysis. AI-driven predictive models can forecast future outcomes based on historical data and current market conditions. These predictions arm PE firms with insights into how potential investments might perform under various circumstances and enable them to plan for risks.
  • Scalability and efficiency. AI and ML systems can analyze vast amounts of data much faster than humans. This enables private equity firms to assess risks across a larger number of investments and make informed decisions faster.

Learn more: For a thought leadership perspective on how PE investors and PE-backed companies can use data to build a competitive advantage, watch this conversation between Tribe AI co-founder Jaclyn Rice Nelson and celebrated data scientist, head of data at PE firm Two Sigma and Tribe AI advisor Drew Conway.

The risks of using AI models in private equity

While the potential outcomes of using artificial intelligence in private equity are overwhelmingly positive, it's important to consider the risks associated with adopting AI solutions in this highly regulated industry: 

1. Regulatory compliance and data privacy

The use of any technology, including artificial intelligence, in private equity has to be compliant with existing regulatory frameworks. This includes regulations specific to private equity (e.g. SEC, FINRA, and AML regulations in the United States), those specific to portfolio companies, and general data privacy regulations like GDPR. Ensuring AI models and the work they produce are transparent and auditable has to be a priority in the implementation phase.

2. Operational challenges

Without the help of AI experts, the implementation and maintenance of AI systems can result in operational disruptions or imperfect outcomes. To address this, PE firms need skilled professionals who understand both AI technology and regulatory nuances. (The contract-to-hire model of sourcing talent is a great fit for AI project maintenance — more on this in the next section.)

3. Human oversight

While artificial intelligence can supercharge the work and results of PE firms, it’s essential to avoid overreliance on AI systems. Balancing AI-driven insights with human judgment prevents risky dependence on technology. 

The best way to manage these risks is by working with an experienced team of consultants, engineers, and data scientists when implementing new technologies. Hiring an experienced team to guide the ideation and implementation of your AI project will allow you to gain a competitive edge while staying safe and compliant, and sustaining ethical standards.

How to get your PE firm started with AI

Are you ready to leverage AI to identify potential investment opportunities, enhance due diligence, and support portfolio companies?

Tribe AI offers specialized AI consulting for private equity. We help PE firms build (and implement, and maintain) solutions tailored to their unique investment strategies and business goals. 

Curious how we work with PE firms? There are two main categories that our projects fall into, based on client needs: 

If you need end-to-end project delivery

For businesses looking to solve a specific need by building a bespoke AI product or PoC, or optimizing an existing model for better performance, we offer managed projects

During managed projects, our consultants and data scientists handle the whole project from start to finish. 

Managed projects typically involve the following steps:

  • Discovery. Our consultants work with your internal team to understand your firm, complete with its goals and challenges. Then, we start mapping out recommended solutions, scoping the project, and assembling a dedicated team of experts.
  • Development. The team works in sprints to develop a proof of concept for a custom AI solution. The PoC model helps both the client and the dedicated team understand how AI can drive the most value. Throughout this process, we schedule regular check-ins between a dedicated product manager, your staff, and other stakeholders. 
  • Launch and testing. Once a PoC is finalized, we work with the firm to launch it in a live business environment. We then educate stakeholders on how to continuously drive value from the solution and make our team available for regular maintenance work. 

After the work is done, we continue the partnership through monitoring and maintenance services to make sure our clients are set up for success.

If you need specialized support for an existing team

For firms that:

  • have an existing team that needs full or part-time specialized support from an AI expert, or
  • want to explore the contract-to-hire model

… we offer staff augmentation projects during which we place highly-specialized experts (commonly engineers or data scientists) directly on a client’s team in order to bring in additional expertise, increase the team’s capacity, or meet specific project requirements or deadlines. 

Staff augmentation projects typically involve the following steps:

  • A 30-minute discovery phone call, during which consultants assess the firm’s technical and staffing needs based on current business processes and goals. 
  • Curating a staffing list. The team at Tribe AI put together a custom talent list based on the necessary experience, areas of expertise, and timelines. This is followed by a detailed walkthrough of the project roadmap. If necessary, we coordinate interviews with the project candidates. On average, this step takes 3 days. 
  • Getting started. Most of our talent is available to jump on projects immediately upon approval. The average time from kickoff to staffing is 10 days.

Contact our team to discuss your needs and get started on your AI project. 

FAQs about AI in private equity

Can private equity be automated?

While AI can automate many processes within private equity — including data analysis and reporting — complete automation of the entire investment process is unlikely due to the complexity of human decision-making and the need for strategic judgment.

How does AI make investment decisions?

AI helps PE firms make investment decisions by analyzing large datasets, identifying patterns, and generating insights.

How are investment firms using AI?

Investment firms are using AI for deal sourcing, due diligence, risk assessment, portfolio management, and predictive analytics. In general, AI improves efficiency and accuracy in decision-making processes across the investment lifecycle.

Related Stories

Applied AI

Key Takeaways from Tribe AI’s LLM Hackathon

Applied AI

How the U.S. can accelerate AI adoption: Tribe AI + U.S. Department of State

Applied AI

Why do businesses fail at machine learning?

Applied AI

How to build a highly effective data science program

Applied AI

No labels are all you need – how to build NLP models using little to no annotated data

Applied AI

A primer on generative machine learning models for music production

Applied AI

A Guide to AI in Insurance: Use Cases, Examples, and Statistics

Applied AI

Machine Learning in Healthcare: 7 real-world use cases

Applied AI

Key Generative AI Use Cases From 10 Industries

Applied AI

How to Evaluate Generative AI Opportunities – A Framework for VCs

Applied AI

How data science drives value for private equity from deal sourcing to post-investment data assets

Applied AI

AI in Construction in 2023: Use Cases and Benefits

Applied AI

An Actionable Guide to Conversational AI for Customer Service

Applied AI

A Deep Dive Into Machine Learning Consulting: Case Studies and FAQs

Applied AI

Understanding MLOps: Key Components, Benefits, and Risks

Applied AI

Everything you need to know about generative AI

Applied AI

How 3 Companies Automated Manual Processes Using NLP

Applied AI

10 ways to succeed at ML according to the data superstars

Applied AI

8 Ways AI for Healthcare Is Revolutionizing the Industry

Applied AI

AI in Banking and Finance: Is It Worth The Risk? (TL;DR: Yes.)

Applied AI

What the OpenAI Drama Taught us About Enterprise AI

Get started with Tribe

Companies

Find the right AI experts for you

Talent

Join the top AI talent network

Close
Tribe