Artificial intelligence has quickly moved from a technology discussion to a boardroom priority. For private equity firms and portfolio leaders, the question is no longer whether AI will reshape the investment lifecycle, but how quickly it can be translated into measurable enterprise value.
That was the focus of a recent executive-level webinar Codurance hosted in partnership with VirtualNonExecs - Navigating the AI Divide: Unlocking Enterprise Value in PE. The session brought together Steve Lydford, UK Managing Director at Codurance, and Gary Pearson, Technology CEO at Newthistle Consulting, to explore the opportunities, risks and practical realities of AI adoption across private equity.
AI is already influencing how investors assess opportunities, identify risk and understand where value can be created. During due diligence, AI has the potential to accelerate analysis, improve decision-making and surface patterns that may otherwise remain hidden across fragmented data, systems and operational processes.
However, the value of AI in diligence is not simply about speed. Its real potential lies in improving the quality of insight.
This is also where data moats become increasingly important. Portfolio companies that can collect, structure and apply proprietary data effectively are better positioned to build AI capabilities that competitors cannot easily replicate. For private equity firms, assessing the strength of a company’s data moat can provide a clearer view of its defensibility, growth potential and long-term valuation.
For private equity firms, this means asking sharper questions earlier, such as:
Used well, AI can support better investment decisions by helping firms understand both the opportunity and the execution risk behind a potential acquisition. This is where Technical and AI Due Diligence becomes crucial, helping uncover hidden risks, identify value creation opportunities and strengthen the investment thesis before a deal is completed.
Once a deal is complete, the first 100 days are a critical window for turning AI from concept into value creation, provided portfolio leaders focus on the right foundations.
Too often, AI initiatives start with experimentation, with tools trialled, pilots launched and productivity claims made, but without a clear link to commercial outcomes such as EBITDA growth, operating efficiency or valuation improvement.
This challenge is reflected in wider industry research, with Gartner(1) reporting in 2026 that at least 50% of generative AI projects had been abandoned after proof of concept by the end of 2025, often due to poor data quality, inadequate risk controls, escalating costs or unclear business value.
For AI to create meaningful value, portfolio companies need to identify where it can have the greatest operational impact. That might include improving customer service, streamlining internal processes, accelerating software delivery, enhancing reporting, supporting sales teams, or reducing manual effort across finance, operations and technology.
The priority should not be ‘where can we use AI?’ but ‘where can AI remove friction, improve performance or unlock growth?’
A key part of the webinar focused on risk, highlighting that for private equity firms, AI adoption risks go far beyond data privacy and compliance, extending into operational resilience, security, governance, technology and legacy debt, and the quality of decision-making across their portfolios.
This is something I’m hearing regularly from private equity firms. We recently worked with YFM Equity Partners to support this challenge, delivering an independent assessment of a suite of portfolio companies in just seven days and providing evidence-based insight into AI’s impact on growth, defensibility and long-term valuation.
AI systems are only as effective as the data, architecture and processes that support them. If a portfolio business has fragmented systems, poor data quality or limited technical governance, AI can amplify existing weaknesses rather than solve them.
This is why responsible AI adoption requires more than tool selection, it needs strong engineering practices, clear ownership, secure implementation and a realistic understanding of organisational maturity.
Private equity firms should therefore assess AI readiness as part of broader technology and operational due diligence. The question is not just whether a company is using AI, but whether it has the foundations to use it safely, effectively and at scale.
At Codurance, our Data and AI Readiness accelerator can help you evaluate your organisation across the critical areas that determine whether an AI investment will succeed, from the business case through to the infrastructure, data and team capability needed to make it real.
Another key theme from the discussion was the role of AI in exit readiness. For portfolio leaders, the work required to maximise valuation should not begin in the final year before exit, it should be embedded from the start of the hold period.
AI can support this by helping businesses become more efficient, data-driven and scalable. But the value must be evidenced. Buyers will not reward vague AI ambition. They will look for proof that technology investments have improved performance, reduced risk and strengthened the business.
That means portfolio companies need to track the impact of AI initiatives from day one. Where has efficiency improved? Where has cost been reduced? Where has decision-making become faster or more accurate? How has customer experience improved? Has the business become easier to scale, integrate or operate?
The firms that can answer these questions clearly will be better positioned to demonstrate value at exit.
The AI divide in private equity is not simply between firms that use AI and those that do not. It is between those that experiment with AI in isolation and those that embed it into a disciplined value creation strategy.
To achieve that, investors and portfolio leaders should focus on five practical steps:
AI is not a shortcut to enterprise value, but when applied with focus, discipline and strong technical foundations, it can become a powerful accelerator.
For private equity firms, the opportunity now is to move beyond the hype and build a clear, measurable approach to AI adoption across the investment lifecycle. Those that do will be better placed to make smarter investment decisions, unlock value faster in the first 100 days and build stronger, more resilient businesses ready for exit.
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.
1. How can AI create value for private equity firms?
AI can help private equity firms improve due diligence, identify operational risks, accelerate value creation plans and support more informed investment decisions across the portfolio.
2. Why are the first 100 days important for AI adoption?
The first 100 days are a critical window to move AI from experimentation into practical use cases that support growth, efficiency, reporting and operational improvement.
3. What should private equity firms assess before investing in AI?
Firms should assess the strength of their data, technology architecture, governance, security, team capability and the commercial business case behind each AI initiative.At Codurance, Rob Gray is a strategic commercial leader specialising in partnering with technology-enabled businesses and their investors to unlock enterprise value through AI-driven software transformation and craftsmanship. His expertise spans the entire investment lifecycle, from technical diligence to post-acquisition value creation, helping leadership teams reduce risk and modernise platforms to accelerate commercial performance and secure stronger exit outcomes.
Gartner, “Why 50% of GenAI Projects Fail — And How to Beat the Odds”, published 26 January 2026