How Private Equity Firms Use AI and Digital Twins

Artificial intelligence (AI) is revolutionizing industries, and private equity (PE) is no exception. Yet, while the promise of AI beckons, its adoption within PE firms remains nascent. For professionals in business acquisitions, including private equity firms, search funds, and M&A teams, understanding AI’s transformative potential is no longer optional - it’s essential. This article explores how AI, particularly digital twin technology, is reshaping value creation in private equity, grounded in insights from Bruce Sinclair, Managing Partner of AI Operating Partners.

AI's Promise for Private Equity: Beyond Hype

Private equity professionals often hear about AI’s potential but struggle to translate it into actionable strategies for portfolio companies. AI offers more than just automation of repetitive tasks; it can supercharge growth, drive cost savings, and enable multiple expansion. However, its full potential is unlocked only when approached strategically.

According to Sinclair, the critical gap in the market is education. Many PE firms lack an understanding of AI’s capabilities, which results in fragmented adoption or, worse, failed initiatives. Instead of relegating AI to tech teams or focusing solely on internal operations, Sinclair advocates for a top-down, strategy-first approach. This includes leveraging AI as a value creation tool both at the portfolio company level and within investment theses.

Digital Twins: Bridging the Physical and Digital Worlds

One of the most transformative applications of AI in private equity is the use of digital twins. While the concept originated in visual effects and simulation, digital twin technology now extends across industries. A digital twin is a virtual representation of a physical asset, process, or service, enabling advanced analysis, simulation, and optimization.

How Digital Twins Work

A digital twin captures the value-creating mechanism of a physical entity - be it a product, process, or service - and represents it mathematically in the digital realm. This allows companies to simulate operations, predict outcomes, and apply AI-driven optimization strategies.

For private equity, this technology makes AI accessible to nearly all portfolio companies, regardless of their industry. Whether it’s streamlining manufacturing processes or optimizing service delivery, digital twins act as the gateway for AI application in traditional businesses.

Real-World Example: Optimizing Steam Boilers

Sinclair shared a project involving a family-owned steam boiler company. Despite operating in a traditional sector, the company leveraged a digital twin to optimize fuel combustion. By creating a mathematical model of the boiler’s combustion process, AI was used to identify inefficiencies and recommend adjustments. Although the company couldn’t directly automate the boiler’s operations due to regulations, they empowered technicians with AI-derived insights. The result? Significant cost savings for customers and the potential for a subscription-based business model, positioning the company for modern market demands.

Value Creation Through AI: A Strategic Framework

To integrate AI effectively, PE firms must focus on value creation rather than treating AI as a science project. Sinclair proposes a structured framework that aligns AI initiatives with investment theses and value drivers. Here’s how to approach it:

1. Identify Core Value Drivers

AI’s impact should be evaluated through three primary value drivers:

• Revenue Growth: AI can identify untapped growth opportunities, enhance customer acquisition strategies, and develop new revenue streams.
• Cost Savings: By automating or optimizing processes, AI can reduce operational expenses and improve efficiency.
• Multiple Expansion: Using AI to enhance a company’s narrative or market position can increase its valuation.

2. Develop and Quantify AI Opportunities

For AI to deliver measurable impact, it must be integrated into the company’s core strategy. Sinclair advises comparing AI initiatives with other value creation tools to ensure resources are allocated effectively. Quantifying ROI is critical - without it, AI projects risk becoming expensive experiments.

3. Leverage AI Across the Investment Lifecycle

AI can play a role in both pre-deal and post-deal stages:

• Pre-Deal: AI analytics can enhance due diligence by identifying potential risks and opportunities. Firms can assess whether portfolio companies are at risk of being disrupted by AI or, conversely, whether they can lead disruption in their sector.
• Post-Deal: Once integrated, AI can be used to refine investment theses, optimize operations, and uncover new growth avenues.

Overcoming Adoption Challenges

Despite its potential, PE firms face hurdles in adopting AI. A common pitfall is approaching AI from a purely technological standpoint rather than as a value-driven tool. Sinclair warns against two prevalent issues:

1. Fragmented Initiatives: Portfolio companies often launch isolated AI projects driven by tech teams, which rarely scale or deliver value.
2. Vendor Influence: Relying on cloud vendors for strategy can lead to solutions that prioritize the vendor’s capabilities rather than the firm’s needs.

The Solution: Education and Strategy

According to Sinclair, the antidote to these challenges is education. PE firms need to understand AI’s capabilities and align them with their strategic goals. This includes educating both internal teams and portfolio company management. Workshops and strategy sessions can be a starting point, followed by triaging portfolio companies to identify those with the greatest AI potential.

Real-World Lessons: AI in Action

Sinclair provided another compelling example of AI in action. An online marketplace for recreational equipment used AI agents to replace repetitive business development tasks. By analyzing the behavior of top-performing employees, generative AI was trained to identify leads and secure listings. The AI agents not only outperformed average employees but also introduced a performance-based business model. This approach reduced fixed costs and improved scalability, demonstrating how AI can transform even routine operations.

Key Takeaways

• Education Is Key: PE firms must invest in AI education at both the firm and portfolio company levels to unlock its potential.
• Align AI with Strategy: Start with the company’s strategic goals and work down to implementation, ensuring AI initiatives are aligned with value drivers.
• Leverage Digital Twins: Use digital twins to bridge the gap between physical and digital assets, enabling AI-driven optimization in traditional industries.
• Quantify ROI: Treat AI as a value creation tool by measuring its impact on revenue, costs, and multiples.
• Avoid Fragmentation: Centralize AI efforts to prevent disjointed, low-impact projects.
• Focus on Both Risk and Opportunity: Use AI to mitigate risks like disruption while identifying new avenues for growth.
• Adopt a Balanced Approach: Combine generative and analytical AI to address diverse business needs.

Conclusion

AI and digital twin technology represent a seismic shift for private equity, offering unparalleled opportunities for value creation. However, to fully realize this potential, PE firms must approach AI strategically - starting with education and a focus on value. By aligning AI initiatives with investment theses and leveraging tools like digital twins, firms can transform their operations, optimize portfolio companies, and gain a competitive edge. The time to act is now, as those who lead the AI adoption curve will shape the future of private equity.

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.

Related Blog Posts

• AI in M&A: Benefits for Deal Sourcing Professionals
• How Listing Change Monitoring Improves Deal Sourcing
• Ultimate Guide to AI-Powered Deal Tracking
• AI in Industry-Specific Deal Targeting