How PE Firms Use AI and Digital Twins to Create Value

Artificial intelligence (AI) and digital twin technology are transforming industries across the board, and private equity (PE) is no exception. In a recent conversation with Bruce Sinclair, Managing Partner at AI Operating Partners, we gained deep insights into how emerging technologies are reshaping value creation strategies for middle-market private equity firms. Sinclair brings decades of experience as a mathematician, programmer, marketer, CEO, and private equity professional. His expertise lies in applying advanced AI and digital twin tools to drive measurable outcomes for portfolio companies.

This article explores key takeaways from that discussion, focusing on how PE firms can adopt AI to maximize portfolio performance, streamline operations, and identify new opportunities for growth.

Understanding AI and Its Role in Private Equity

AI is often viewed as a futuristic tool, but its applications are grounded in real-world data and processes. Sinclair explains that AI's core capability is transforming proprietary data into useful information, which can then create tangible value. For private equity firms, this means deploying AI to improve portfolio company performance and maximize returns.

However, Sinclair highlights a common issue: most AI initiatives fail because they are approached as "science projects" rather than strategic drivers of value. By focusing first on business strategy and aligning AI applications with clear value drivers, firms can avoid wasted resources and achieve meaningful results.

Why Value Creation Should Be the Focus

In private equity, value creation is the ultimate goal, and AI offers a unique and powerful tool to achieve it. According to Sinclair, AI can be utilized to enhance three primary value drivers:

1. Revenue Growth: AI can identify new opportunities for market expansion, optimize pricing strategies, and personalize customer experiences.
2. Cost Savings: By automating repetitive tasks and improving process efficiency, AI reduces operational costs.
3. Multiple Expansion: AI-enhanced innovations and efficiencies can improve a company’s perceived value, leading to higher multiples during exit.

AI's potential isn't limited to improving existing practices; it can also uncover new value levers that might not have been identified in the original investment thesis.

What Are Digital Twins? The Bridge Between Physical and Digital

To fully harness AI, companies need a way to digitally represent their physical products, services, or processes. This is where digital twin technology comes into play. Traditionally, digital twins were used to create photorealistic representations of objects, but their modern application goes far beyond visuals.

As Sinclair describes, a digital twin is a mathematical model that simulates how a physical asset creates value. It acts as a bridge between the physical and digital worlds, enabling companies to apply AI to optimize processes, predict outcomes, and innovate more effectively.

By digitizing their operations, even traditional businesses - from steam boiler manufacturers to online marketplaces - can adopt AI to improve performance and reduce costs.

Real-World Examples of AI and Digital Twin Implementation

Sinclair shared two compelling examples of how AI and digital twins are currently being implemented in private equity-backed companies:

1. Optimizing Steam Boiler Efficiency with AI

One project involves a family-owned business that manufactures steam boilers, a traditional and long-standing industry. The goal was to reduce operating costs for end-users by optimizing the boilers' combustion process.

• How It Worked: A digital twin of the combustion process was created, simulating how fuel, oxygen, and flow rates interact to produce steam. Analytical AI algorithms were then used to fine-tune these parameters for maximum efficiency.
• Results: By providing technicians with AI-driven tuning recommendations, the company significantly reduced fuel consumption in its boilers. This created measurable cost savings for customers, enabling the company to shift to a subscription-based business model, further increasing recurring revenue.

2. Using AI Agents to Streamline Sales in an Online Marketplace

In another example, Sinclair worked with an online marketplace for recreational equipment to reduce its reliance on a large team of business development representatives.

• How It Worked: Generative AI agents were trained to replicate the behavior of top-performing salespeople. These agents scoured listings, contacted potential sellers, and closed deals, following a standard operating procedure derived from real-world data.
• Results: The AI agents outperformed average human employees on key metrics, such as efficiency and success rate. The company adopted a pay-per-listing business model for the AI agents, creating a scalable and cost-efficient solution.

Both examples illustrate how AI can drive measurable outcomes, whether by reducing costs, increasing efficiency, or creating entirely new revenue streams.

Challenges to AI Adoption in Private Equity

Despite its potential, many PE firms struggle to adopt AI effectively. According to Sinclair, the primary barriers are a lack of education and a bottom-up implementation approach:

1. Lack of Awareness: Many firms are unsure where to start with AI or what it can realistically achieve. This leads to underwhelming results when AI initiatives are driven by curiosity rather than strategy.
2. Science Projects vs. Strategy: AI efforts often fail because they are treated as technical experiments rather than business-driven initiatives. By focusing on technology instead of value creation, firms miss opportunities to align AI with strategic goals.
3. Vendor-Specific Solutions: Many firms rely on AI solutions offered by vendors, which often prioritize the vendor’s technology over the firm’s specific needs. This results in suboptimal implementations that fail to deliver significant value.

A Strategic Playbook for AI Adoption

Sinclair advocates a top-down approach to implementing AI, starting with strategy and focusing on value creation. Here’s how PE firms can take their first steps:

1. Educate Yourself and Your Team

Understanding AI's capabilities and limitations is the first step. Firms should invest in educational workshops to help team members grasp what AI can do, both at the firm and portfolio levels.

2. Conduct Portfolio Triage

After educating the team, firms should assess their portfolio companies to identify which ones are most suited to AI adoption. Look for businesses with repetitive processes, substantial data, and a clear alignment between AI capabilities and business objectives.

3. Develop a Value Creation Plan

AI initiatives should be tied to clear outcomes, such as reducing costs or expanding market share. Quantify the expected return on investment (ROI) and compare AI’s potential impact to other value creation tools.

4. Partner with Experts

Given the complexity of AI implementation, finding the right partner is crucial. Experienced practitioners can help firms identify opportunities, build robust strategies, and avoid common pitfalls.

5. Start Small, but Think Big

Begin with controlled pilot projects that allow firms to test AI’s effectiveness on a smaller scale. Once successful, scale up the initiatives to maximize impact across the portfolio.

Key Takeaways

• AI as a Value Creation Tool: Treat AI as a strategic asset that can drive revenue growth, cost savings, and multiple expansion.
• Digital Twins Enable AI Adoption: Digital twins act as the bridge between physical assets and AI, making AI accessible to traditional industries.
• Real-World Success: Examples like optimizing steam boilers and AI-driven sales teams demonstrate AI’s diverse applications.
• Prioritize Education: Educating PE teams and portfolio companies on AI’s capabilities is the first step to successful adoption.
• Avoid Science Projects: AI initiatives should focus on measurable business outcomes, not experimentation.
• Adopt a Top-Down Approach: Start with strategy and align AI initiatives with value drivers for maximum impact.
• Think Beyond Risk Mitigation: While AI risk management is essential, firms should also focus on its vast opportunities for growth.

Conclusion

AI and digital twins represent a transformative opportunity for private equity firms to enhance portfolio performance and gain a competitive edge. By focusing on education, strategy, and measurable value creation, firms can overcome adoption challenges and unlock the full potential of these technologies. As Sinclair aptly puts it, AI should be seen not as a futuristic novelty but as a practical and results-driven tool that will shape the future of business.

As the private equity landscape continues to evolve, those who embrace AI early and strategically will be well-positioned to lead the way. Start your journey today by exploring how AI can align with your firm’s long-term goals and portfolio needs. The future is here - make it work for you.

Source: "Unlocking AI’s Potential for Portco Value Creation | Middle Market Growth Conversations" - Association for Corporate Growth (ACG), YouTube, Aug 25, 2025 - https://www.youtube.com/watch?v=l9mIBzc-Euk

Use: Embedded for reference. Brief quotes used for commentary/review.

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