The New Private Equity Playbook: From Diligence to Exit

In part 1 of this series of articles, we explored how Artificial Intelligence (AI) is reshaping both risk and value creation in private equity, and why investors must rethink how they assess competitive advantage in an increasingly AI-driven landscape.

In Article 2, we built on this by examining the rise of the “data moat” — where proprietary data becomes a critical driver of long-term differentiation. As AI capabilities become increasingly commoditised, control of high-quality data is emerging as one of the most powerful and durable sources of competitive advantage.

In this final article, we examine how these shifts are reshaping the private equity playbook from diligence through to exit. As the drivers of value evolve, investors must adopt new criteria for evaluating opportunities and develop a stronger narrative for positioning companies at exit. The new playbook places far greater emphasis on operational execution and solving the “last-mile” challenge of applying AI effectively to proprietary data and real business workflows.

What This Article Explores:

• How Private Equity (PE) firms must change their strategy from the initial investigation (diligence) to the final sale of a company in the AI era.
• How to identify "AI washing" versus genuine "data moats" where a company gets smarter as it grows.
• Why small-to-medium businesses should customise existing AI models rather than building their own from scratch.
• Moving away from charging "per user" toward charging for the actual value or outcomes the AI provides.

Operational Diligence: Red Flags vs. Green Flags

Diligence must now “look under the hood” to distinguish between genuine AI value and superficial “AI washing”.

• Red Flags: A major warning is the “Thin Wrapper” problem, where the product is merely a thin UI over a public API (e.g., OpenAI) with no proprietary data, workflow, or technology moat. These businesses lack pricing power and are highly vulnerable. Another critical flag is the “Negative Signal” of talent, where a lack of in-house AI talent signals a mismatch between the stated AI vision and the capability to execute the Value Creation Plan.
• Positive Indicators: We look for a compounding data moat… proof that this business gets smarter with every new customer. AI-native leadership is also vital, where the AI strategy is synonymous with the overall corporate strategy.

A Pragmatic Technology Strategy

For SMEs, building a foundational large language model (LLM) from scratch is neither financially viable nor strategically wise. Instead, the strategic path involves customising foundation models using Retrieval-Augmented Generation (RAG) or fine-tuning open-weight models with proprietary data. This avoids astronomical costs while building differentiated solutions. However, we must manage vendor lock-in, data privacy risks, and service availability. To mitigate this, we advocate for abstracting model integration behind an internal API layer.

Maximising Exit Value

As AI automation takes on more work, the value a platform delivers becomes independent of human users, undermining traditional seat-based models. We advise a shift toward value-based, consumption-based, or outcome-aligned pricing. To command a premium valuation, a target must represent a “Team + Data Engine”— a proven, production-grade capability already generating value from a proprietary dataset.

To succeed, PE firms must champion the “Data-to-Product” pipeline, moving data from a “cost-to-store” liability to a “revenue-to-generate” asset. For recent acquirers, this means cleaning datasets early in the 100-day plan to lay the groundwork for future monetisation.

Turning Insight into Action

Taken together, these shifts point to a fundamental change in the private equity playbook. Success will increasingly depend on how effectively investors can translate AI capabilities into proprietary data advantages, operational performance, and credible exit narratives.

To explore these themes further, download our latest white paper, The Impact of AI on Private Equity, which explores the critical themes reshaping the investment landscape and provides a strategic framework for investors, operating partners, and portfolio leaders navigating the AI revolution.

How Codurance Can Help 

If you are assessing AI adoption, preparing for diligence, exit or strengthening a Value Creation Plan, technology strategy must be directly aligned to enterprise value outcomes.

For over a decade, Codurance has partnered with private equity firms and their portfolio companies to unlock and accelerate enterprise value through AI-driven technology, while proactively identifying and mitigating technical risk. 

Across the investment lifecycle, we help businesses modernise legacy platforms, implement scalable, and resilient architectures, and build high-performing engineering capabilities through Software Craftsmanship. The outcome is faster delivery, lower technical risk, stronger operational performance, and a more resilient, exit-ready asset.

If you'd like to find out more, get in touch with us today. 

Frequently Asked Questions

1.  What should private equity firms look for when assessing AI opportunities? 

Private equity firms should look for genuine AI value, not just hype. The strongest businesses combine proprietary data, clear use cases, and the ability to apply AI effectively in real operations. 

2.  What is the “thin wrapper” problem in AI? 

The “thin wrapper” problem refers to products that simply sit on top of public AI models without any real proprietary data, workflow integration, or technical moat. These businesses can be easy to replicate and difficult to defend. 

3. How is AI changing the private equity playbook? 

AI is changing how investors assess businesses from diligence through to exit. Firms now need to evaluate data quality, execution capability, and how AI can support stronger growth and valuation. 

About the Author

Lee Sanderson has over 30 years of experience in software development, spanning roles from Developer to hands-on CTO, and is a polyglot programmer with deep experience across modern technologies. He enjoys helping teams move faster by streamlining development through XP, Agile practices, and continuous delivery, while maintaining clean architecture and high-quality code. He also enjoys spending time exploring how AI will shape the future of software engineering.